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When we think about living environments of the urban poor, slums might be the most immediate association. These slums evoke a more or less stereotype impression of built environments: complex, high dense alignments of small makeshift or run-down shelters. However, this perceived characteristic morphology is neither globally homogeneous nor is this perception covering morphologic appearances of urban poverty in a comprehensive way. This research provides an empirical baseline study of existing morphologies, their similarities and differences across the globe. To do so, we conceptually approach urban poverty as places which provide relatively cheap living spaces serving as possible access to the city, to its society and to its functions-so called Arrival Cities. Based on a systematic literature survey we select a sample of 44 Arrival Cities across the globe. Using very high resolution optical satellite data in combination with street view images and field work we derive level of detail-1 3D-building models for all study areas. We measure the spatial structure of these settlements by the spatial pattern (by three features-building density, building orientation and heterogeneity of the pattern) and the morphology of individual buildings (by two features-building size and height). We develop a morphologic settlement type index based on all five features allowing categorization of Arrival Cities. We find a large mor-phologic variety for built environments of the urban poor, from slum and slum-like structures to formal and planned structures. This variability is found on all continents, within countries and even within a single city. At the same time detected categories (such as slums) are found to have very similar physical features across the globe.
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Applied Geography
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The morphology of the Arrival City - A global categorization based on
literature surveys and remotely sensed data
H. Taubenböck
, N.J. Kra, M. Wurm
German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Oberpfaenhofen, Germany
Informal settlements
Urban poverty
Building morphologies
Urban pattern
Remote sensing
When we think about living environments of the urban poor, slums might be the most immediate association.
These slums evoke a more or less stereotype impression of built environments: complex, high dense alignments
of small makeshift or run-down shelters. However, this perceived characteristic morphology is neither globally
homogeneous nor is this perception covering morphologic appearances of urban poverty in a comprehensive
way. This research provides an empirical baseline study of existing morphologies, their similarities and dier-
ences across the globe. To do so, we conceptually approach urban poverty as places which provide relatively
cheap living spaces serving as possible access to the city, to its society and to its functions so called Arrival
Cities. Based on a systematic literature survey we select a sample of 44 Arrival Cities across the globe. Using very
high resolution optical satellite data in combination with street view images and eld work we derive level of
detail-1 3D-building models for all study areas. We measure the spatial structure of these settlements by the
spatial pattern (by three features building density, building orientation and heterogeneity of the pattern) and
the morphology of individual buildings (by two features building size and height). We develop a morphologic
settlement type index based on all ve features allowing categorization of Arrival Cities. We nd a large mor-
phologic variety for built environments of the urban poor, from slum and slum-like structures to formal and
planned structures. This variability is found on all continents, within countries and even within a single city. At
the same time detected categories (such as slums) are found to have very similar physical features across the
1. Introduction
We did not see a mountain full of houses, but rather a house the size of
a mountain(UTT, 2016). This statement captures the overwhelming
impression one gets of informal land occupation capitalizing every inch
of urban space in cities across the globe. Organic, amorphous, complex,
and dense seas of makeshift shelters have signicantly dierent phy-
sical appearances than formal, planned parts in cities (e.g. Fig. 1). With
it, the built environment can be an expression of inequality in cities,
and socio-economic disparities even become visible from space (e.g.
Davis, 2007; Sliuzas, Mboup, & de Sherbinin, 2008). While a rst su-
percial observation may suggest forms of living at the lower end of
urban societies feature great similarities in terms of their physical ap-
pearance, (informal) processes such as illegal land occupation do not
always shape such distinct and demarcating building morphologies and
patterns for this social group (Saunders, 2010; Vaz & Berenstein, 2004).
Settlements of the urban poor are by no means a homogeneous
physical phenomenon (e.g. Schneider-Sliwa & Bhatt, 2008; Taubenböck
& Kra, 2015). Nevertheless, most studies describing the physical ap-
pearance of such areas are of qualitative nature observing e.g. high
building densities or organic patterns as characteristic (e.g. Davis,
2007; Glaeser, 2010); however, relatively little systematic quantitative,
spatial research exists about their explicit physical appearance
(Hofmann, 2001; Kuer, Pfeer & Sliuzas, 2016), not to mention a
systematic inventory of morphologic types across the globe.
In this paper, we aim at reducing these knowledge gaps about set-
tlements of the urban poor by an empirical baseline study taking stock
of physical building types and determining structural patterns across
the globe. Avoiding terminological imprecision and related conceptual
restrictions of terms such as slumor informal settlement, we base this
study on the term Arrival City(introduced by Saunders, 2010). The
term conceptually integrates all places which provide comparably
cheap living spaces serving as possible access to the city, to its society
and to its functions for rural-urban migrants as well as for the existing
urban poor; this conceptual umbrella allows a broader perspective on
the specic places and their built morphologies.
Received 19 September 2017; Received in revised form 26 January 2018; Accepted 3 February 2018
Corresponding author.
E-mail address: (H. Taubenböck).
Applied Geography 92 (2018) 150–167
0143-6228/ © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Arrival Cities show a large variety of built forms. Earth observation
(EO) data are the crucial data source to consistently capture built en-
vironments. However, due to conceptual imprecision of the target class,
due to challenges for image classications algorithms in these complex
environments and due to unavailability or high costs of appropriate EO-
data, spatial data on the level of individual buildings (level of detail-1
(LoD-1)) for the neglected parts of cities are mostly inconsistent, gen-
eralized or simply inexistent. The purposeful development of
Fig. 1. The morphologic appearances of informal versus formal settlements for the example of Kibera, Nairobi; Top: Photography of Kibera:
Johnny Miller/Thomson Reuters
Foundation; Bottom: High resolution optical satellite imagery
Google Earth.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
classication algorithms for mapping living environments of the urban
poor from EO-data demands for empirical knowledge on the morphol-
ogies of the respective target class.
In this paper, we nd 44 Arrival Cities by an extensive literature
survey. They function as representatives for built environments of
urban poor across the globe. For these samples, we produce three-di-
mensional (3D) building models in LoD-1 using a combination of high
resolution satellite data as well as auxiliary data sources (such as geo-
tagged photos or in-situ surveys). Based on these 3D-building models
we introduce a method for classication of physical built-up categories
of Arrival Cities. Methodologically we extend established approaches
on the measurement of patterns of the built environment from EO data
(based on Taubenböck & Kra, 2014). In consequence, this study relies
on a data set which has been produced in a consistent and comparable
way, allowing a single methodological logic and is thus unbiased from
possible inconsistencies that usually occur, if multiple input data across
countries are applied. With it, we aim to add to the current body of
literature for a better empirical documentation of the settlement
morphologies of Arrival Cities.
Specically, we aim to answer the following research questions: (1)
Which physical morphologic settlement categories of Arrival Cities can
be distinguished? (2) Which similarities and dierences between
building morphologies of Arrival Cities exist within a city, a country or
a continent?
The remainder of the work is structured as follows: Section 2briey
presents the relevance of this topic on the political development agenda
and reviews the state of the art from an urban remote sensing per-
spective. Section 3introduces the Arrival Cityas conceptual frame-
work for using a physical approach towards classifying and character-
izing morphologies of the urban poor. Section 4presents the
methodology of the literature survey, the selection process of re-
presentative Arrival Cities, the classication of 3D building models, and
nally the analysis of spatial patterns and their categorization. In sec-
tion 5the results on the measured spectrum of morphologies in Arrival
Cites and the resulting categories are presented. This is followed by a
discussion in section 6, where we also try to evaluate which inuence
determinants such as the topographic situation or pre-existing patterns
have on urban morphology. Section 7concludes with a perspective.
2. Background and state of the art
The intergovernmental agreement on the Sustainable Development
Goals (SDGs) acts as the post 2015 Development Agenda (successor to
the Millennium Development Goals) (UN, 2015). To end poverty, en-
sure healthy lives, provide access to quality education, water and sa-
nitation, to build sustainable cities and communities, among others are
goals directly relating to challenges for the urban poor. However, the
demand for sustainable data for sustainable developmentor, in other
words, improved data availability, quality, consistency, timeliness and
disaggregation is often not met (UN, 2015).
2.1. Approaches towards assessment of the urban poor
A number of statistics underpin the urgent need for more extensive
empirical knowledge on the places of the urban poor. 25% of the global
urban population (which is almost 1 billion people) live in slums or
informal settlements (UN-Habitat, 2015). The builders of informal
housing have become the largest builders of housing in the world
(Tiwari, 2007), and thus, they are creating the cities of tomorrow. Davis
(2007) estimates 200,000 slums globally, not to mention the unknown
higher quantity of Arrival Cities. These often neglected parts of the city
are of crucial importance due to their arrival functions providing access
to the labour markets, education, etc. In Mumbai, for example, 65% of
the workforce is employed in the informal sector (Sudjic, 2010)orin
Mexico City 60% of construction is informally done (Burdett & Sudjic,
Although we observe an increasing quantity and variety of pub-
lications on this topic of urban poverty, these still lack consistent con-
ceptual understandings across or even within disciplines, agreed
methods of measurement or empirical data necessary. This leads to, as
Satterthwaite (2003) proclaimed there is no lack of nonsense statistics on
levels of urban poverty, or the general statement in the World Migration
Report (2015) that we have a massive lack of basic data about urban
From a spatial point of view, the coverage of data on areas of the
urban poor varies signicantly across the globe; especially in the Global
South large data gaps exist. In consequence, a systematic quantitative
spatial documentation of morphologic forms is absent. With the recent
massive increasing availability of EO sensors, remote sensing data have
become crucial for spatially capturing urban inequality. Freed from any
administrative boundaries, the data are consistent and (in theory as e.g.
data costs are still a restriction) available globally. Nevertheless, with
only 87 key publications identied to date in the eld of remote sensing
(Kuer, Pfeer & Sliuzas, 2016), we are still far away from a global
inventory of Arrival Cities or a systematic classication of physical
characteristics of these places. However, we must be aware that using
the morphologic structures as proxy for identifying the social group of
the urban poor is simplifying the spatial and social complexity (e.g.
Wurm & Taubenböck, 2018). In consequence, the remote sensing
community conceptualizes poverty by features describing the mor-
phologic urban appearance (Sliuzas et al., 2008). In a generic slum
ontology indicators are conceptualized allowing a localization of set-
tlements of the urban poor from EO-data (Kohli, Sliuzas, Kerle, & Stein,
2012). However, as slums are a relative concept (Gilbert, 2007), a
physical approach to mapping these areas still lacks an internationally
agreed concept as well as systematic empirical documentation. As a
result studies using remote sensing data still lead to incomparable data
sets across studies and study sites due to conceptual dierences and the
relativity of the target classes.
2.2. Mapping the urban poor with EO-data
From a remote sensing perspective, recent work predominantly fo-
cused on the development of methodologies for automatic classication
of slum areas. Based on morphologic assumptions dening these areas
of the urban poor, considerable methodological development has been
achieved to automatically classify locations and extents of slums from
optical (e.g. Hofmann, Strobl, Blaschke, & Kux, 2008; Kuer & Barros,
2011; Kuer, Pfeer, Sliuzas, & Baud, 2016; Shekhar, 2012) as well as
radar (e.g. Stasolla & Gamba, 2008; Wurm, Taubenböck, Weigand, &
Schmitt, 2017) data. The developed approaches rely on physical fea-
tures characterizing the built environment by indicators such as high
heterogeneity in building alignments (e.g. Jain, 2007; Owen & Wong,
2013), irregular street networks (e.g. Gueguen, 2014; Niebergall, Loew,
& Mauser, 2008), small building sizes (e.g. Baud, Kuer, Pfeer,
Sliuzas, & Karuppannan, 2010; Graesser et al., 2012) or high building
densities (e.g. Kit, Lüdeke, & Reckien, 2012; Taubenböck & Kra, 2014)
to dierentiate these housing areas from formal ones. Studies use
spectral, spatial and textural features and combinations of them (e.g.
Engstrom et al., 2015; Graesser et al., 2012; Kit & Lüdeke, 2013;
Sandborn & Engstrom, 2016; Wurm, Weigand, Schmitt, Geiß, &
Taubenböck, 2017) for operationalizing the observed built-up features
by image features. In this regard, Kohli, Stein and Sliuzas (2016) pre-
sent related uncertainties in image interpretation.
Beyond, the combination of remote sensing with other data sources
for detecting (and characterizing) areas of the urban poor is an emer-
ging research eld. The interplay of EO-data with census information
proves correlations of image features with socio-economic parameters
of the areas (e.g. Duque, Patino, Ruiz, & Pardo-Pascual, 2015; Sandborn
& Engstrom, 2016; Taubenböck et al., 2009). Wurm and Taubenböck
(2018) prove that morphologic slums classied from EO-data allow the
localization of urban poor with high accuracies; however, spatial
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
dierences between morphologic and census slums also reveal short-
comings of the physical approach towards completeness. Applications
of EO-based morphologic slum classications are e.g. the assessment of
slum populations (Kit, Lüdeke & Reckien, 2013; Taubenböck & Wurm,
2015) or the combination with geotagged data from social media
identifying most areas of urban poor being digital deserts (Klotz, Wurm,
Zhu, & Taubenböck, 2017).
At the spatial level of 3D building models, studies documenting the
built environment are very scarce. This is due to the complexity of slum
morphologies in combination with the low availability of highest re-
solution (< 1 m) digital surface models. Few studies measure the
mentioned physical features of slums in a quantitative sense and com-
pare morphologies within (Taubenböck & Kra, 2014) and across cities
(Taubenböck & Kra, 2015). Studies also acknowledge that even within
one city, physical morphologies, roof appearances as well as types of
slums vary signicantly (Kuer, Pfeer, Sliuzas, Baud, & Maarseveen,
Summarizing, although remote sensing is a crucial data source for
detecting and characterizing areas of the urban poor, signicant
knowledge gaps exist: 1) remote sensing reduces the social group of the
urban poor to slum areas; 2) classication algorithms remain proof of
concept for limited test sites failing to provide consistent large area
(continental scale) classications of slums with high accuracies; 3)
documentation of the variability of morphologic forms of the urban
poor at 3D city model resolutions remain scarce; and 4) an inventory of
morphological types as conceptual foundation is inexistent due to data
gaps. In this paper, we aim with our comprehensive empirical baseline
study at reducing the data gaps regarding physical morphologic cate-
gories of Arrival cities across the globe and we aim at providing the
knowledge for algorithm development capturing these areas in EO-data.
3. Conceptualization of this study the Arrival City
The challenge of a global characterization of physical appearances
of settlements titled slum,informal,squatter,spontaneous,ghetto,
illegalor irregularor described by local names such as favela,bi-
donville,townshipor gecekondu,among many others, starts with
terminology and related conceptualizations. Given the attention in lit-
erature, one expects the evolution of a clear and consistent con-
ceptualization and method of measurement; however, these terms do
not base on a standard denition, they are conceptually neither distinct
nor consistent (Ghani & Ranbur, 2015).
By comparing the often used terms slum versus informal settlement,
the conceptual fuzziness becomes obvious. UN-Habitat (2003) denes
slums by using qualitative measures based on the so called shelter
deprivationindicators such as the lack of durable housing and tenure
security, overcrowding, and limited access to clean water and accep-
table sanitation. The denition of informal settlements diers (although
the term is often applied synonymously; in consequence, this leads to an
imprecise usage in literature): Informal settlements refer to those areas
that developed through unauthorized occupation of land outside a
legal, regulatory, planned and professional framework (e.g. Bähr &
Mertins, 2002; Huchzermeyer & Karam, 2006). The consequence is that
slums refer to highly precarious living conditions whereas informal
settlements, in contrast, are not exclusive to these living conditions as
the concept relates to the legal jurisdiction. Thus, informality may also
refer to better quality of construction and living conditions. Other as-
sociated problems arise e.g. as slums are relative in their conceptual
meaning: What is considered to be a slum in one country may be re-
garded as perfectly acceptable accommodation in another (Gilbert,
2007). In consequence, slums cannot be dened safely (or measured in
absolute terms) in any universally acceptable way; in the case of in-
formal settlements, their genesis may have occurred outside formal
means, but over time got formalized (e.g. by slum upgrading pro-
grammes) making it dicult for unambiguous classication. Ambiguity
on these areas relates to the common understanding that informal (and
also formal) forms of settlements rarely occur in any pure form; hybrid
conditions (e.g. complex compositions of organic building layouts in-
terwoven with structured planned forms) are the norm (Werthmann &
Bridger, 2016).
Against these issues on terminology and related conceptual com-
plexity, we base this study on the term and concept of the Arrival City.
The term has been introduced by Saunders (2010), capturing places
with the main function of arrival to the city. Semantically the term
'arrival' refers to the relocation of rural populations to urban environ-
ments. However, conceptually the term is not just meant as a sole
physical arrival functioning as a mere place for living and working; it is
most importantly meant as a place of comparatively cheap living con-
ditions that opens up the possibility to become part of the urban society.
In consequence, this concept integrates all places which provide low-
cost access to arrival functions(living, working, education, cultural,
etc.) in cities, which morphologically span from spontaneous, pre-
carious shacks built overnight to squatting of informal or formal land
and/or structures, to (once respectable) housing aected by deteriora-
tion. The concept of the Arrival City is thus embracing the conceptual
dierences of the other mentioned terms.
But why is this concept of the Arrival City necessary for this study?
The literature survey (cf. section 4.1.; appendix 1) reveals that an un-
ambiguous conclusion on the status of a study site regarding in-
formality, security of tenure, access to sanitation, etc. is not always
possible; furthermore, as hybrid forms are the norm a discrete classi-
cation can obscure reality. A conclusion on the functioning as Arrival
City is, in turn, more straight-forward and unambiguous. Another issue
is that, as mentioned above, various popular terms such as slumor
informalityare very inconsistently applied in dierent studies (e.g.
Kuer, Pfeer & Sliuzas, 2016); a comparison of morphological pat-
terns is at least at risk to be conceptually illegitimate. The lack of ter-
minological consistency especially in literature dealing with physical
appearances (mostly using remote sensing data) results in ontologies
and classications which remain conceptually vague, inconsistent and
With this umbrella concept of the Arrival City we overcome these
issues and aim at the following specic aims: 1) with the increasing
availability of EO data, this data source recently became essential for
classication of these areas. However, with respect to the conceptual
ambiguity, the capability to measure inequality in cities from space by a
spatial approach needs a systematic catalogue of morphological forms.
Thus, we aim to bring clarity to the variability of morphological types
of Arrival Cities. 2) The existing dierent concepts lead to incomparable
data across nations or continents. With the morphological catalogue,
we aim at a distinct conceptual framework; in terms of remote sensing,
this aims at baseline information for algorithm development by mor-
phologically dened target classes.
4. Data and methodology
This section is structured into two parts: The rst part (4.1.) in-
troduces the strategy of study sites selection aiming at a representative
global sample of Arrival Cities. The second part (4.2.) introduces the
spatial concept for classication of physical appearances of the settle-
ment structures, the quantitative spatial analysis of the morphologic
patterns and the categorization strategy.
4.1. Literature survey and selection of study sites
A comprehensive global knowledge repository about Arrival Cities
to draw from does not exist. Beyond, an exhaustive compilation of the
entire variability of the physical appearances of settlements is, at a
resolution of individual buildings, still an utopia. Reasons are that in-
formal settlements are mostly absent in ocial plans, not all existing
physical types of informality have been reported or systematized and
more generally, availability of adequate highest resolution geodata
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
especially in slum-like structures is still scarce. This means a high un-
certainty on amount, spatial distribution and types of Arrival Cities
exists. In consequence, we conduct a systematic literature survey to
compile a large variety of documented Arrival Cities across the globe to
provide a representative sample.
For the systematic search we use general search terms such as ar-
rival city,informal settlement,slum,squatter,shanty townand
ghettoas well as synonyms in the respective local context such as
favela,gecekondu, among many others. Our search relies on common
search engines such as scopus or google scholar (cf. all criterions ap-
plied for the systematic search are presented in Table A-2 in the ap-
pendix). Yet, not any documented and identied Arrival City in lit-
erature is intended to be listed; instead we aim at selecting
representative study sites by reason of dierent attributes:
(a) we select Arrival Cities documented in literature
(b) we select Arrival Cities from dierent cultural areas and continents
for a representative global geographic distribution.
A main challenge arises from the necessity for 3D building models
for a structural spatial analysis (cp. spatial concept in 4.2.). The selec-
tion process therefore is guided by the limitation that every selected
Arrival City needs an extensive classication based on available very
high resolution (VHR) EO-data. Based on these considerations we
choose Arrival Cities for a quantitative spatial analysis by reason of the
following attributes:
c) we select Arrival Cities consisting of a spatial extent formed by a
signicant group of buildings (> 1000), not by one individual or
few buildings.
d) we select a generally even distribution of Arrival Cities in dierent
topographic locations; this allows specic analytic questions whe-
ther the physical appearance shows dierences due to topography.
e) we select for few examples more than one Arrival City within one
city; this allows specic analytical questions whether the physical
appearance reveals intra-urban dierences.
f) we select Arrival Cities originating from time periods between 2010
and 2016.
In the literature survey many morphologic forms of Arrival Cities
are identied which do not t into our spatial concept; e.g. these are
areas with untypical spatial extents that dissent to a signicant amount
of buildings or there are forms that are less conform to settlements
with regard to accommodation. For a more comprehensive picture we
additionally provide a qualitative description of these morphologies for
amore comprehensive presentative of physical forms of living.
4.2. Classication and characterization of settlement morphologies
What denes the appearance of a settlement? And how can it be
measured and characterized? In general, types and conditions of in-
dividual buildings as well as the spatial alignment of these buildings dene
the urban spatial structure. In consequence, we aim at measuring the
appearance of a settlement by the spatial pattern buildings create (1)
and the (building) morphology (2):
(1) For characterizing spatial patterns of settlements we apply three
features: building density (in 2D), orientation of buildings and hetero-
geneity index (relating to the variance of density within the area of
(2) For characterizing the morphology of the individual buildings we
use two features: size (ground oor) and height (number of stories).
The condition of the building is disregarded here, as this parameter
is dicult to assess using EO-data.
In the workow we use three spatial scales of analysis, from the
smallest to the largest: Building level (a), block level (b), district level (c)
(Fig. 2).
Fig. 2. Hierarchical structure of spatial scales building, block and district level for the morphologic settlement analysis and the workow for calculating the ve structural variables
and the nal categorization of Arrival Cities.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
a) Building level: The building level refers to the highest spatial detail
the individual buildings (Fig. 2). We derive the necessary high
detailed geoinformation from very high resolution optical satellite
data in combination with Google Street view information, geor-
eferenced photos and partially in-situ observations. As structural
complexity in Arrival Cities does mostly not allow for fully auto-
matic delineation of individual building footprints with acceptable
accuracies from EO-data, we derive the complex settlement struc-
tures using the cognitive perception of an experienced image ana-
lyst. Employing a standardized digitization protocol a consistent
data base is derived at building level: Using EO-data having geo-
metric resolutions of 1 m and better (e.g. QuickBird, WorldView)
and a consistent scale of 1:1000 for image analysis, each building is
digitized and represented by a single polygon. The polygons contain
several vertices representing real shapes of ground oors even for
more complex buildings than rectangle structures. We are aware
that in certain areas the complexity of dense building patterns and
respective roof structures is even outdrawing the visual capabilities
to distinguish individual buildings in VHR optical EO-data; thus it
might lead in certain cases to a generalized derivation of a polygon
representing a mixture of houses instead of an individual un-
ambiguous polygon per building. However, as Sliuzas et al., 2008
remark, the visual interpretation still oers the best capability for
deriving these complex structures.
From the building classication, the individual building sizes and
orientations are calculated. Beyond this, building heights (as number of
oors) are attributed to every individual building. To do so, we in-
corporate in-situ information on building heights from eld surveys
(done for the samples in Mumbai, Izmir, Istanbul, Sao Paulo, Athens,
Bucharest, Berlin, Nairobi and Cape Town), we employ spectral and
spatial characteristics in VHR EO-data (we use estimations by the in-
terpreter such as shadows or building patterns allowing an assessment
of building height) and analyse georeferenced photos in online plat-
forms such as GoogleEarth or, if available, Google Street view in-
formation (where we count the number of oors). In a previous study
the building heights assessed using this approach resulted in an accu-
racy of 91,9% (Taubenböck & Kra, 2014). Based on the parameters
building sizes and heights we achieve 3-D city models in level of detail
1 (LoD1). All variables measuring the appearance of settlements relate
to these 3D city models.
b) Block Level: The city block is a spatial entity combining the in-
dividual elements (buildings) into spatial aggregates aiming to
capture spatial patterns of the settlements on a meso scale (Fig. 2).
To do so, the close meshed street network is used to dene city
blocks; or, if in organic (informal) settlement structures streets are
absent, visually identiable pathways or obvious structural change-
overs are introduced manually for the sub-division of space. As ex-
ample, if a dense settlement area turns abruptly into an open space
without a street as separating line, we introduce one to provide
spatial entities capturing high and low density without mixed areas
with blurring eects. In general, the city blocks are spatially sub-
dividing the district level.
At this block level, we calculate ve features to spatially measure
the built environment of a settlement: we calculate the building density
as the ratio between the sums of all cumulated building ground oors to
the respective block unit area (eq. (1)).
A = area; B
= building density; BF = extent of building footprint;
= reference unit; n = number of buildings;
We derive the orientation of buildings as proxy for measuring the
complexity of the alignment of buildings. We assume unplanned areas
are by nature more irregular than planned. To do so, we calculate the
individual orientation of each building husing the longitudinal side.
Subsequently, the nearest spatial neighbor of each building is detected
and the dierence in main orientation O
is calculated as dierence in
angular degree (eq. (2)).
=− = = ≤ ≤
1, ..., B; h 1, ..., H ; 0 90
bh bh N bh bh() b (2)
where Bis the total number of building blocks, H
is the number of
buildings per block b,
bh is the orientation to geographic north and N
(bh) describes the one nearest neighbor of each building within the
We assume a geometric order of building pairs if orientation dif-
ferences are close to 0 or 90°. In contrary, we assume complex align-
ments closer to orientation dierences of 45°. We convert this into a
continuous index I
of orientation complexity ranging from 0 (=geo-
metric) to 1 (=highest degree of geometric chaos) for every building
pair (eq. (3)).
=− − × +IOΔ45
bh bh
We aggregate all individual index values of building pairs to de-
termine the median alignment index for the respective blocks (eq. (4)).
===Imed b{I h 1, ,H }| 1,....,
bbh b
where H
is the number of buildings in the block b.
As third feature, we measure the morphologic heterogeneity of the
pattern over space. The heterogeneity index I
quanties dierences in
building densities of a block with all adjacent neighboring blocks.
Therefore, absolute building density dierences from a center block to
all adjacent neighboring blocks are calculated. The individual density
dierences are added in absolute values (as the subtraction of density
values of the center block with the density value of an adjacent block
may result either in a positive or a negative value). The respective
quantity of norm changes is noted also. This sum of norm dierences
gets divided by the quantity of neighboring blocks for normalization
leading to a nondimensional number. The number of norm changes will
be multiplied with the sum of building density dierences; if a block has
no norm changes, it will always be added to 1. With increasing dier-
ences of density values in adjacent blocks, the index indicates a higher
heterogeneity of a pattern (eq. (5)) (for more details see also
Taubenböck & Kra, 2014).
|bd bd
= heterogeneity index; k = quantity of adjacent neighboring blocks;
= built-up density value of the center block; bd = built-up density
value of an adjacent block, NC = quantity of norm changes.
As fourth feature, we calculate the building sizes as the average of all
building ground oors in the respective block unit area (eq. (6)).
= average building size.
And, as fth feature, we calculate the building heights as the average
of all building oors within the respective block unit area (eq. (7)).
= building height; BA
= average building height.
c) District level: As main spatial level of analysis we employ the dis-
trict level (Fig. 2). The district level represents the entire area of one
Arrival City (or at least for the parts where geoinformation have
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
been derived). This unit functions as one, consistent spatial level for
the aggregated morphologic settlement analysis. Here, the varia-
bility of the ve features at block level are presented by providing
the median (for building heights we apply the mean, as the limited
number of classes and the often dominant low building heights do
not allow for a precise dierentiation using the median) and the
data distributions are presented in box plots.
4.3. Categorization of settlement morphologies
For the categorization of the measured settlement morphologies we
quantify deviations from measured spatial features against an expected
(model) value. The expected (model) value represents the measured
maxima per feature across all Arrival Cities. For every one of the ve
spatial features we state a hypothesis. We let ourselves guide from the
common usages in literature for a subtype of Arrival Cities morpho-
logic slums which we assume to have patterns of highest morphologic
1) We expect high building densities as open space in cities is limited
and precious, population pressure is high and planning regulations
are absent, which leads to a minimization of open public space.
2) We expect non-geometric, irregular orientations of buildings as
planning regulations are absent, regular street pattern not given,
individual decision form random lay-outs, and in certain cases
adjustments to complex topographic situations may lead to non-
regular lay-outs.
3) We expect high structural homogeneity of the building patterns as
full utilization of space is targeted as space is limited.
4) We expect shelters of small sizes as available land is limited and
precious, population pressure is high and nancial capabilities of
the dwellers are low.
5) We expect low building heights as nancial capabilities of the
dwellers are low and building materials are limited.
The virtual combination of measured maxima per feature generates
atheoretical ideal type morphologic slum. This virtual combination is, of
course, not existent in reality as the maxima per feature result from
dierent Arrival Cities; however, it marks the virtual end of measured
real-world structures for calibration in the analysis.
We categorize a morphologic settlement type index value I
as devia-
tion from the theoretical ideal type morphologic slum. To do so, we nd
maximum and minimum values per feature of the calculated medians
(mean for building heights). By minimum-maximum normalization [0,
1] (eq. (8)), we adjust heterogeneous values measured to a notionally
common scale per variable.
x:= (xx
) (8)
is the minimal median value per feature; x
is the maximum
median value per feature.
The I
results from the cumulative deviation from the theoretical
ideal type morphologic slum (eq. (9)).
=x(d) + x(o) + (1x(h
)) + (1x(s)) + (1x(h)) (9)
is the morphologic index, d = density; o = orientation;
= heterogeneity index; s = building size; h = building height.
With it, the measured physical appearances of Arrival Cities can be
classied along a continuous scale for categorization. However, as
auxiliary means for semantic description we apply discrete categories
using equal distances among groups.
Beyond, we classify additional categories by descriptive analysis for
the Arrival Cities identied in the literature survey, but not fullling the
spatial concept of analysis.
5. Results
This section is organized as follows: First, the selected Arrival Cities
used for the spatial analysis of settlements are presented and related
background information from the literature survey is provided (5.1.).
Second, the physical characteristics of the selected Arrival Cities are
presented (5.2.). Third, we introduce the categorization of the Arrival
Cities (5.3.). In this part we relate the categories to dierent location
based observations, i.e. whether categories have obvious similarities
within one city or country. Fourth, we present other types of Arrival
Cities which do not t into the spatial concept of analysis but are re-
levant forms (5.4.).
5.1. Global representatives for Arrival Cities
Based on the literature survey we select 44 Arrival Cities across the
globe. With 10 Arrival Cities from Africa,9 from America,15 from Asia
and 10 from Europe, a global distribution is realized. Within the con-
tinents a spatial distribution is carried out (e.g. for Asia we chose
Arrival Cities from western (e.g. Turkey), central (e.g. Iran, India), and
eastern parts (Mongolia, China, The Philippines)). Beyond, the sample
also contains dierent Arrival Cities within the same country (e.g. for
Brazil, China, France, Turkey and USA) as well as dierent Arrival
Cities within sample cities (e.g. Cairo, Cape Town, Mumbai and Dhaka).
Fig. 3 illustrates the spatial distribution of the selected samples and
visualizes spatial appearances of selected settlement structures in very
high resolution optical EO-data. It becomes obvious that building
morphologies being the home of the urban poor feature a large varia-
In the appendix (Table A-1) comprehensive background information
on all 44 selected Arrival Cities are introduced: locations, names, sizes
of the areas of interest, the number of buildings classied from Earth
observation data. Furthermore, the table gives systematic background
information on the topographic situation, descriptive information on
housing types and materials, access to infrastructure, legal status, if
possible, and the estimated population. The reference from literature
which denes the selected sample as Arrival City complements the
5.2. Physical characteristics of Arrival Cities
Under the conceptual umbrella of the Arrival City we unite terms
such as slums, informal settlements and the like. These terms implicitly
carry the idea of a uniform urban type including morphologic simi-
larity. The reported physical appearances of these settlements are
mostly of qualitative nature and do not allow for an objective, quan-
titative generalization. Other studies using EO-data take conceptually a
certain morphologic similarity as a given basis.
In general, the classications of Arrival Cities from EO-data pre-
sented in ground gure plans (Fig. 4) illustrate that morphologic si-
milarity cannot be taken for granted. The visualized building ground
oors and their patterns feature variabilities, but also similarities. We
nd that, as many descriptive studies suggested, high building den-
sities, complex, organic patterns and small building sizes are features
applying to many of the Arrival Cities. However, a closer inspection
reveals that it is not as simple as that: We also nd signicant lower
building densities, large building sizes, lay-outs with a geometric or-
ganization, in all kinds of combinations. As examples, we nd in highly
dense areas both, complex (e.g. number 06 in Fig. 4) and ordered (e.g.
13) alignments of buildings; we nd large (e.g. 37) vs. small buildings
(e.g. 31). We also nd highest building densities (e.g. 05) vs. com-
paratively low densities (e.g. 44).
The visual inspection of the ground gure plans does not allow for a
quantitative, resilient analysis of the building patterns and
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
Fig. 3. Selected Arrival Cities across the globe from the literature survey and illustration of the appearance of dierent morphologic forms of the built environment in EO-data.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
Fig. 4. Ground gure plans of the 44 selected Arrival Cities.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
Fig. 4. (continued)
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
morphologies. Fig. 5 presents and contrasts the ve features dening
these urban morphologies at block levelbuilding density,building or-
ientation and heterogeneity index representing the patterns and building
size and height representing the building morphologies in form of
boxplots. The 44 Arrival Cities are grouped based on their continental
location. Beyond, each variable is ordered in decreasing or respectively
Fig. 5. Boxplots illustrating ve variables dening urban morphologies for all 44 selected Arrival Cities.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
increasing manner of the median (mean for building heights) for the
particular feature.
In general, we nd that a simplistic approach towards the mor-
phology of the living areas of urban poor obscures reality. On the one
hand, the boxplots for almost all features and study sites reveal that we
are dealing with hybrid forms of building patterns and morphologies, as
they feature more or less variance within and across Arrival Cities. First,
we detect that a large variance within continents appears (e.g. we nd
on every continent Arrival Cities with building densities of 70% and
more to below 40%). Second,wend large varieties of morphologic
forms within one country (e.g. with building heights of 1.57 stores on
average for Gülsuyu-Gülensu, Istanbul vs. 4.59 stores in Kadifekale,
Izmir). Third,wend large varieties of morphologic forms within one
city (e.g. building sizes of Eldarb el-Ahmar in Cairo are in median with
131 m
more than twice as large as the ones in Manshiet Nasser with
60.5 m
). On the other hand, the boxplots also reveal that although these
morphologically hybrid forms are identied, the stated hypothesis on
typical morphologic features (cf. section 4.3) can be conrmed to a
certain degree: First, we measure that high building densities are a
characteristic feature of Arrival Cities. Although building densities
uctuate across the globe, 21 out of the 44 Arrival Cities feature den-
sities higher than 60% (26 with densities higher than 50%). This re-
markable intense utilization of space can be benchmarked when we
relate these values to other parts in cities. As examples, the medians of
building densities are signicantly lower in central areas of mega cities
(e.g. in a circle of a 10 km radius around the center of London, UK the
density is 30% or in Paris, France it is 34%). They are also signicantly
lower for planned cities (Chandigarh, India 35%; Lingang, China 24%),
large housing estates (e.g. Neuperlach, Munich 18%; Gropiusstadt,
Berlin 16%) or residential suburbs (e.g. Sun City, USA 24%, garden city
Letchworth, UK 15%). Within our (incomplete) sample for comparison
of building densities to Arrival Cities, historical European city centers
reveal densities closest to Arrival Cities (Hamburg, Germany 46%;
Dortmund, Germany 46%; Munich, Germany 52%). Second, we measure
that the hypotheses for the heterogeneity index (29 Arrival Cities below
the value of 15, which is comparatively homogeneous), the building
sizes (half of the study sites have ground oors below 60 m
) and
heights (24 Arrival Cities are not higher than 2 oors in average) can
largely be conrmed. In contrast, for the building orientation a clear
trend towards complex alignments is not existent (12 Arrival Cities are
below 0.2, what indicates a relatively regular orientation; 16 Arrival
Cities feature values between 0.2 and 0.4, and 16 Arrival Cities are
above the value 0.4).
5.3. Morphologic categorization of Arrival Cities
For the morphologic categorization of Arrival Cities we transfer the
features into a common scale by the minimum-maximum normal-
ization. The cumulative combination of all normalized variables results
in the morphologic settlement type index. The index allows classication
of the dierent Arrival Cities into morphologic categories.
As we base our categorization on relative deviation from the
Fig. 6. Categorization based on the developed morphologic settlement type index value I
for all 44 Arrival Cities.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
assumptions we stated per variable, the morphologic type fullling the
expectations of all variables with 100% is a virtual combination of
maxima measured for all 44 Arrival Cities (class A
in Fig. 6). The
maxima are Islambagh, Dhaka for building density (Median = 90.2%),
the refugee camp Afgooye for building orientation (0.27), Bharat
Nagar, Mumbai for the heterogeneity index (5.2), the north cemetery,
Manila for building size (7.7 m
) and West Adams, Los Angeles for
building height (1.5 oors). The large range of morphologic categories
becomes tangible when we compare these values with the ones mea-
sured with maximum deviation (class D
in Fig. 6). This is the virtual
combination of the refugee camp Al-Zataari for building density
(Median = 24.3%), North Philadelphia, Philadelphia for building or-
ientation (0.0), Khayelitsha (Victoria Merge), Cape Town for the het-
erogeneity index (50.8), Les Pyramide, Évry for building size
(685.9 m
) and Baishizhou, Shenzhen for building height (7.7 oors).
They mark the unexpected virtual end of morphologic categories (cf.
Table 1).
In general, we nd there is no homogeneous morphological global
everywhere of Arrival Cities. The social group of urban poor trying to
get access to urban societies and functions live in very dierent struc-
tural patterns and building morphologies. Referring to the rst specic
research questions stated in the introduction, we classify three main
categories and three respective transitional forms. They stretch from slum
(Cat. A; threshold > 3.75) and slum-like (Cat. AB; range 3.753.25)
morphologies to mixed unstructured-structured neighborhoods (Cat. B;
range 3.252.75) and mixed structured-unstructured neighborhoods (Cat.
BC; range 2.752.25) even to structured (Cat. C; range 2.251.75) and
formally planned (Cat. CD; range 1.751.25) areas. In general, the ap-
plied continuous scale allows an unambiguous assessment of the mor-
phologic conditions. However, for a more general description and no-
menclature we use the mentioned thresholds of equal-distance. Table 1
introduces the categories, describes the measured physical features and
lists samples and the resulting morphologic settlement type index va-
Regarding the second specic research question stated in the in-
troduction we nd similarities and dierences between building
morphologies of Arrival Cities: Within a single city we nd that places of
urban poor do not necessarily reect similar morphologies. In Cape
Town the morphologic dierences reach from the (informal) slum-like
structures of Griths Mxenge (Cat. AB; I
of 3.60) to the planned
township of Mandela Park (Cat. C; I
of 2.32); in Dhaka they reach from
mature downtown slums where continuously re-densication processes
Table 1
Morphologic categories of Arrival Cities.
Cat. Terminology Description Samples & morphologic index
Theoretical ideal type
morphologic slum
This theoretical morphology reects the combination of the extrema
per physical feature measured from the 44 samples across the globe.
Density: Islambagh, Dhaka (90.2%); orientation: Afgooye (0.27);
heterogeneity index: Bharat Nagar, Mumbai (5.2); size: north
cemetery, Manila (7.7 m
); height, West Adams, L.A. (1.5 oors);
(morphologic index =5.0).
A Morphologic slum The morphology measured in one real Arrival City corresponds to the
greatest possible extent with the physical assumptions in our spatial
concept as well as with the suggested ontologies and qualitative
descriptions in literature. Small makeshift shelters are huddled
together in most complex alignments.
Kibera, Nairobi (4.36); Bharat Nagar, Mumbai (4.19); Tondo,
Manila (4.19); Makoko, Lagos (4.08); Petare, Caracas (4.03);
Dharavi, Mumbai (4.01);
Santosh Nagar, Mumbai (3.95); Magdalena Contreras, Mexico City
(3.93); Islambagh, Dhaka (3.82); Hutong, Beijing (3.81); Afgooye
AB Slum-like morphology The morphology features deviations from the measured extrema or
the common assumptions in at least one of the ve physical features.
However, the dominant physical appearance is a very dense,
complex pattern of deprived building types.
Lo Prado, Santiago de Chile (3.62); Griths Mxenge, Cape Town
(3.60); Nadezhda, Sliven (3.58); Turano, Rio de Janeiro (3.56);
Cova de Moura, Lisboa (3.50); Victoria Merge, Cape Town (3.50);
Gülsuyu-Gülensu, Istanbul (3.43); North Cemetery, Manila (3.41);
Manshiet Nasser, Cairo (3.39); Independencia, Lima (3.36);
Paraisópolis, Sao Paulo (3.30); Imbaba, Cairo (3.30); Khoroo 9,
Ulan Bator (3.30); Refugee Camp, Al-Zataari (3.30); Ezbet el-
Hagana, Cairo (3.29); West Adams, L. A. (3.29); Eldarb el Ahmar,
Cairo (3.25);
B Mixed unstructured-
structured neighborhoods
The morphology features signicant deviations from the measured
extrema or the common assumptions of slum morphology in more
than one of the ve physical features; it contains mixed forms still by
trend closer to slum morphology than to structured, formal
neighbourhoods: Forms include further developed once slum-like
morphologies (e.g. increase in building heights), run-down, deprived
(and once higher quality) building blocks, inltration of shelters into
existing residential structures, or converting of shelter usages for
urban poor.
Tei Toboc, Bucharest (3.25); Eslamsahar, Teheran (3.17); Agios
Panteleimonas, Athen (3.07);
Canada Real, Madrid (2.99); Kadifekale, Izmir (2.97); Belleville,
Paris (2.80); Karaagac-Altigagac, Ankara (2.78);
BC Mixed structured-
unstructured neighborhoods
The morphology features signicant deviations from the measured
morphologic slums and slum-like morphologies as well as from the
related common assumptions. The morphology combines typical
features of structured (e.g. geometric alignments, frequent spatial
transition of buildings and open spaces) und unstructured
neighbourhoods. The morphology is by trend closer to structured,
formal neighbourhoods
Kadamtali, Dhaka (2.73); Baishizhou, Shenzhen (2.57);
Molenbeek, Brussels (2,43); Mandela Park, Cape Town (2.32);
Kreuzberg, Berlin (2.26);
C Transition to structured
character of neighborhoods
The morphology combines typical features of planned, structured
neighborhoods such as regular alignments or lower densities with
few slum-like features.
North Philadelphia, Philadelphia (2,17); Tower Hamlets, London
CD Formal, structured
The morphology provides typical features of planned, formal,
structured neighborhoods: low densities, geometric alignments, large
and high buildings.
Le Pyramide, Évry (1.53). Bronx, New York City (1.52);
Theoretical ideal type formal,
structured neighborhood
This theoretical morphology reects the combination of the minima
per physical feature measured from the 44 samples across the globe.
Density: Al-Zataari (24.3%); orientation: North Philadelphia,
Philadelphia (0.00); heterogeneity index: Victoria Merge, Cape
Town (50.8); size: Le Pyramide, Évry (685.9m
); height:
Baishizhou, Shenzhen (7.7 oors);
(morphologic index =1.0).
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
occurred over time (Islambagh, Cat. A; I
of 3.82) to more recent, more
peripheral informal developments where full utilization of space still
lies ahead (Kadamtali; Cat. BC; I
of 2.73). However, we also observe at
the same time that structures of the same category are reproduced in
very similar manner within one city (e.g. in Cape Town the areas of
Griths Mxenge and Victoria Merge are both Cat. AB with I
of 3.60
and 3.50; or in Cairo all four Arrival Cities are classied as Cat. AB).
Logically, also within a single country places of urban poor do not
necessarily reect similar morphologies. In China the traditional
Hutongs feature with slum morphologies (Cat. A; I
of 3.81) signicant
morphologic dierences to urban villages (Cat. BC; I
of 2.57). In the
USA we nd signicant morphologic dierences between the low-rise
inll housing in West Adams, L.A. (Cat. B, I
of 3.29) vs. high rise
ordered structures in the Bronx, New York City (Cat. CD; I
of 1.52).
However, we also observe that morphologies are reproduced in similar
manner within one country (e.g. Turkey with Gülsuyu-Gülensu in
Istanbul (Cat. AB; I
of 3.43), Kadifekale in Izmir (Cat. B; I
of 2.97) or
Karaagac-Altigagac, Ankara (Cat. B; I
of 2.78) or in Brazil with Turano
in Rio de Janeiro (Cat. AB; I
of 3.56) and Paraisópolis in Sao Paulo
(Cat. B; I
of 3.30)).
At the continental level Arrival Cities obviously feature a large variety
of morphologies and we basically identify almost all categories at every
continent. The category morphologic slums (Cat. A) is found typical for
cities of the Global South: America (e.g. Petare, Caracas; I
of 4.03),
Africa (Kibera, Nairobi; I
of 4.36) and Asia (e.g. Tondo, Manila; I
4.19). Categories AB, B, BC generally are found to exist at every con-
Fig. 7 exemplies LoD-1 building models for dierent categories
based on the developed morphologic index. From the high dense, low
rise slum-like structures in Kibera Nairobi (Cat. A) to the structured
high rise pattern in Évry (Cat. CD) the visualization aims to illustrate
this morphological transition.
5.4. Forms of Arrival Cities not fullling the spatial concept of analysis
Beyond the identied three main and three transitional categories of
building categories in Arrival Cities, we identied other physical ap-
pearances of Arrival Cities (or types of living conditions) in the litera-
ture survey. They are not part of our morphologic analysis of settle-
ments patterns due to their incongruity with the applied spatial
concept, as they consist e.g. of just one building, a very small amount of
shelters, or they have no shelters at all. However, for a more compre-
hensive perspective on the physical appearances of Arrival Cities and
respective living conditions we present these forms in a descriptive
way. Table 2 introduces four further categories (E-H) and lists examples.
6. Discussion and interpretation
When you walk through the city, one immediately senses the
neighborhood. The physical appearance of the built environment plays
a decisive role for its atmosphere rich, poor, safe, unsafe, busy or
sleepy. One particular part in cities the so called Arrival City fullls
a specic function: cheap living spaces for the poor to get access to
urban functions. The common perception of these areas is very much
connected to slums. While slums might be the most prominent example
suggesting a globally uniform morphologic type, this study reveals and
documents the large morphological variety of Arrival Cities (or places
of the urban poor) which existed or exist across the globe. We ac-
knowledge that the variability of physical manifestations of Arrival
Cities is inexhaustible, and that their morphology is often highly dy-
namic, which is not captured comprehensively in this study either.
Although we entered an age of a massive increase of (geo-)data, the
highly resolved geoinformation (LoD1) necessary for the morphologic
analysis of these places is still widely absent. A morphologic catalogue,
an inventory, or even global maps of these places are inexistent and
only very few examples have been documented in explicit, quantitative
spatial manner at this geometric level (Kuer, Pfeer & Sliuzas, 2016).
In consequence, taking stock of morphologic appearances con-
ceptualized as Arrival City with 44 test sites across the globe in LoD1
functions as empirical baseline to reduce these knowledge gaps and
approximate existing categories across the globe. However, as we refer
to the selection of 44 Arrival Cities it is likely that our categorization of
morphologic types using maxima and minima is biased by our samples
and the analysis might need an adjustment if other Arrival Cities may
feature more unexpected morphologic appearances.
6.1. Capabilities for mapping of Arrival Cities
Remote sensing functions here as crucial data source as it is,
compared to e.g. census data consistent, up-to-date and available across
the globe and it has the capability to spatially capture certain loca-
tions of the urban poor based on the assumption of morphologic cor-
relation. This study enlarges empirical knowledge on the morphological
characteristics of Arrival Cities and presents a catalogue of measured
structural categories. We acknowledge that measurement of spatial
structures and patterns is complex and calculation methods, spatial
units of analysis or thematic dimensions have signicant inuence
(Taubenböck, Standfuß, Klotz, & Wurm, 2016). However, we rely on
the block units suggested in literature for capturing the features of in-
dividual building structures. Beyond, we are in line with the structural
features from the ontologies presented by Kohli et al. (2012) and Sliuzas
et al. (2008). In consequence, we assume the physical appearance is
measured in a transparent and reproducible way. Using these features
we detect three main morphological categories and three respective
transitional forms as well as four categories not tting into the pre-
sented spatial concept. This result of seven main categories and three
transitional classes reveals the morphologic complexity and variety of
such places.
In a way, this also reects current challenges regarding conceptual
complexity and inconsistent usages of various terms in literature (slum,
informal settlement, ghettos, etc.) aiming at urban poor. This leads to
imprecise target classes and varying measurement methods. And, it
consequently is intrinsically leading to incomparable data and biased
conclusions. The morphological perspective taken here is, naturally,
only one part of a comprehensive understanding of such areas; how-
ever, with the adopted umbrella concept of the Arrival City a distinct
catalogue of morphological categories allows systematic documentation
of such places. With this approach, we are not targeting a pre-dened
class which is conceptually fuzzy, internationally not agreed, or am-
biguously dened; rather, this allows nding distinct morphological
types without restrictions by conceptual ambiguity, legal status, or
The measured large morphological variety reveals that we have to
accept that solely remote sensing as single discipline is not capable for a
global classication of Arrival Cities and related urban poverty in a
complete and unambiguous way. Some forms of the measured mor-
phology do not dier suciently to other physical appearances within
the urban built landscape (e.g. Categories C and CD). In consequence, it
is legitimate to reduce EO-based applications to capturing urban pov-
erty to the categories morphologic slums(Cat. A) and slum-like
morphologies(Cat. AB), as these are morphologically the most sig-
nicant categories found anywhere across the globe. This is conrmed
as it has been shown that these reect the social group of urban poor
with high correlations (e.g. Sandborn & Engstrom, 2016; Wurm &
Taubenböck, 2018). However, the categories B and BC, although con-
taining characteristic morphologic features of Arrival Cities, reect
hybrid forms. This means they contain signicant deviations from the
stated morphologic assumptions and, in addition, their relative mor-
phological dierences to other, surrounding morphological appear-
ances often become more and more marginal. Naturally, the social
groups residing there are also mixed. In consequence, a comprehensive
detection of Arrival Cities, especially for the latter cases, needs
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
Fig. 7. Selected examples of 3-D building models of the dened categories A, AB, B, BC, C and CD.
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
additional data sources (such as data on income, education, among
others). This shows that EO data and classication algorithms are fea-
sible to contribute signicantly to a global catalogue or inventory of
Arrival Cities (categories A and AB), but the morphologic catalogue also
reveals that a comprehensive global inventory needs multi-disciplinary
data and methods.
6.2. Determinants of morphologic appearances: theory and evidence
What remains in this discussion is the question what might de-
termine the seemingly idiosyncratic physical appearances that evolved.
The most widely acknowledged causality is the natural landscape;
against the general acceptance, we found that when classifying the 44
test sites into generally at and generally hilly terrains (using a digital
terrain model; cf. Table A-1 appendix), the structural features in each
group do not signicantly dier (e.g. median building densities show
57.4% for hilly vs. 54.9% for at terrains). If we base this comparative
analysis only on slums (Cat. A) and slum-like (Cat. AB) structures
> 3.25), we nd building densities in hilly terrain with 66.1%
slightly denser than in at terrain with 64.8%. Admittedly, although
there are no major dierences, we nd that in our sample steep terrains
cause higher utilization of space.
Another dening issue of idiosyncratic appearances is pre-urban land
division (Kostof, 1991), which can be inuenced by topographical issues
but goes further involving patterns of ownership (e.g. pre-existing rural
properties or current informal land developers) or the disposition of
previous usages (e.g. farming practices). Here we nd that areas with
clear pre-existing geometric street patterns for the categories A, AB, and
B (we selected unambiguous examples of Imbaba, Paraisópolis, West
Adams, Eslamsahar, Nadezhda and Lo Prado) feature a median building
density of 66.8%. This is signicantly lower compared to areas of no
obvious pre-existing geometric order (we selected unambiguous ex-
amples of Makkoko, Kibera, Petare, Tondo, Dharavi, Bharat Nagar and
Santosh Nagar) featuring 75.6%. The remaining Arrival Cities are a
mixture of both classes and are thus not considered. This result lets us
assume that in areas of no pre-existing order space is utilized to a higher
degree. Pre-existing patterns also lead to a more clear geometric order:
For slums (Cat. A) and slum-like (Cat. AB) structures (I
> 3.25) we
see building orientations for these areas with 0.065 more geometric
than without (0.108).
In general, we nd the two most prominent theories topography
and pre-existing patterns tested here do inuence urban patterns. But,
we nd there is nothing completely instinctive or predestined regarding
evolving urban patterns either planned or unplanned. Landscape
features may sometimes be embraced, but others may also be rejected.
So, it is the complexity of local determinants within globalized pro-
cesses that form these structures.
7. Conclusion and outlook
This study documents that there is not one global morphologic
settlement type solely characteristic for Arrival Cities. And we show
that remote sensing is a crucial data source for detecting and char-
acterizing built environments of the urban poor as they are still widely
neglected in ocial maps. This empirical baseline study can now
function as initial point to develop EO-based classication algorithms
beyond proof of concept for morphologic slum and slum-like areas.
Beyond, matching the spatial knowledge for morphologically insignif-
icant areas with location-based information of urban geography may
allow a more systematic, consistent approach of localizing patterns,
quantities and forms of poor urban living on global scale. With it we
add a step towards a comprehensive morphological catalogue or even
an inventory as foundation for urban geography and studies about
urban poverty. Or, with a development agenda perspective, this study
may provide additional knowledge for a more comprehensive regis-
tration of the dimension and distribution of urban poverty.
We shape our buildings; thereafter they shape us. This famous quote
of Winston Churchill reveals how built environments shape societies. In
times of the largest migration ever from rural areas into cities (UN,
2014) with cities extensively sprawling into their hinterlands
(Taubenböck et al., 2012), informal developers have become the largest
builders of housing in the world. They shape a large share of living
environments in Arrival Cities. In consequence, the responsibility for
shaping societies by the built environment is widely left to them. This is
not necessarily negative, as many of the former European downtown
slums have turned into beloved spots of today (Glaeser, 2010). It is of
course not the built environment alone that denes whether Arrival
Cities become an integral part of the urban landscape and society, but it
is argued to play a relevant role. In light of this it is alarming that
although we are aware of this issue as well as of the dramatic global
process of migration into cities, systematic spatial knowledge on the
physical appearance of Arrival Cities is still underrepresented.
Table 2
Categories of physical appearances of Arrival Cities not being selected for the morphologic analysis.
Cat. Terminology Description Samples & literature
E Small Inll occupation Informal occupation of small urban empty spaces (e.g. by tents or
makeshift shelters; or so called laneway alleysare an often
informal way (and sometimes tolerated by city ocials) for urban
density increase by housing inll)
e.g. the Curvy, Berlin, Germany (Wehner, 2015); Terras do Lelo
Martins Lissabon, Portugal (Campos Costa et al., 2013); laneway
alleys in Hamilton or Toronto, Canada (Cubitt, 2008).
F Illegal squatting in and at
existing structures
Roof top dwellers (informal top up urban densication virtueing
and replenishing space); basement suites (Informal occupation or
illegal squatting of basements); formally planned structures
(informal occupation of formal structures, e.g. when unnished due
to construction stops); other forms of illegal squatting include
converting cubicles, verandas, staircases into living environments.
Roof top dwellers, e.g. Hong-Kong, China; Singapore, Singapore
Pomeroy (2012); basement suites, e.g. Calgary, Canada (Tanasescu,
Wing-tak, & Smart, 2010); e.g. Torre de Davide in Caracas,
Venezuela (Davis, 2007); e.g. converting cubicles verandas,
staircases into living environments in Hong Kong (Schmitt, 2007).
G Trailer homes/Traveller
camps/Mobile homes/Boat
A group with a nomadic life for example in caravan pitches or boats;
e.g. in Great Britain for about 25%, legal caravan pitches are
inexistent and lead to informal parking; or in Hong Kong boat
people nding homes in cargo boats, houseboats, small shing
crafts ashore close to the city
e.g. Dale Farm, Basildon, England; Hayes road, Sully, Wales (Porter
& Taylor, 2010); or, 0.4.% of Australians are counted caravan park
residents(Greenhalgh, 2002); boat people in Hong Kong (Schmitt,
2007) or Australia (O'Doherty & Lecouteur, 2007)
H Houseless/Homeless/Rooess
e.g. pavement dwellers includes people sleeping on streets without
any or inadequate shelter
e.g. pavement dwellers; e.g. in Indian cities such as Calcutta,
Mumbai, or in Dhaka, Bangladesh (Padgett & Priyam, 2017)orin
Australia where 0.5 of the population is reported to be without
shelter (ABS, 2012); for more detailed classications see (European
Federation of National Organizations Working with the Homeless,
H. Taubenböck et al. Applied Geography 92 (2018) 150–167
We sincerely thank our supporters for all the dedication to this
project: P. Aravena-Pelizari, L. Banzer, S. Brandstätter, V. Färber, S.
Grund, M. Kühnl, P. Majhen, M. Neumann, I. E. Pistopoulou, I.
Standfuß, M. Weigand. Beyond we thank European Space Imaging
(EUSI) for providing high resolution optical satellite data. This work has
received also funding from the European Research Council (ERC) under
the European Unions Horizon 2020 research and innovation pro-
gramme (grant agreement No [714087]- So2Sat).
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... However, despite these stipulated targets, informal settlements continue to grow [6]. Ameliorating the conditions of deprivation in informal settlements requires up-to-date base maps with comprehensive information on their spatial locations and dimensions [3], which is mostly inconsistent, generalized or simply non-existent [7]. Given the dynamic nature of these deprived areas [8], there is an exigency for techniques that can provide rapid and reliable information on their morphological layouts. ...
... However, the intricacy of the semantic abstraction of informal settlements has been emphasized [16][17][18]. Firstly, the cost-prohibitive nature and, sometimes, unavailability of high-resolution earth observation (EO) data [7] is a major drawback in promoting efforts for the accurate delineation of urban deprived areas. Secondly, the inherent variations in informal settlement morphological appearances, either within or across geographical locations [19], confound the task. ...
... Evidencing this, Fallatah et al. [22] alluded to the complexity of urban areas because of their characteristic intermixing of diverse man-made and natural features, which may engender confusion between the object and its spectral reflectivity. According to Taubenböck et al. [7], a vital requirement in the delineation of informal settlements is the capability to identify small pockets of deprivation for informed decision-making. Although all LULC classes could be accurately captured in the current study, the approach failed to capture some discrete informal settlement patches. ...
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Mapping informal settlements’ diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity.
... To do so, we define a gradient scale ordering these mixtures based on the relative importance of spontaneous urban processes against urban planning processes. We draw on the categorizations and scales from the works of (Cozzolino, 2020;Dovey, 2020;Kuffer, Van Maarseveen, Sliuzas, & Pfeffer, 2017;Noizet & Clémençon, 2020;Taubenböck, Kraff, & Wurm, 2018a). In the following, this scale is what we refer to as the 'intensity of plannedness' (IoP) of cities. ...
... Overall, this creates an imbalance going against our goal of multi-dimensional representativity in data, study sites and thus, our categories of IoP. Therefore, we complete our dataset by data from the study of (Taubenböck et al., 2018a) covering deprived places across the globe on building level and by manual digitization when Open Street Map do not provide consistent or comprehensive enough data. When digitizing, we remain consistent with the OSM format. ...
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The physical appearance of the built urban landscape is the result of multiple, intertwined processes. The relationship between the existing morphology and the diversity of planning processes, however, has been little studied empirically at a global scale. In this study, we develop an ontology of planning intensity: conceptualizing intemediate categories of whether an urban structure is planned single-handedly or constantly updated by myriads of participants. Thus, we move away from the 'planned/unplanned' dichotomy and develop a continuum of Intensity of Plannedness (IoP). The focus of research is whether these conceptualized categories of IoP show demonstrable differences in morphology. Hence, we operationalized the urban structure by three structural elements: buildings, morphological units and streets. Curating geodata on 381 study sites across the globe, we empirically investigate the relation of the IoP to the structural complexity of the urban fabric. Tests of significance of difference and post hoc analyzes are performed on the statistical distribution of structural complexities of the categories of IoP. This study proves empirically that the distinct IoP has significantly contrasting structural complexities. From this, we conclude that there is indeed a relationship between both, the intensity of the process of planning and the resulting urban morphology and that this relationship is non-linear.
... This circumstance leads us to the central tension between qualitative and quantitative approaches addressed at the beginning of this paper. On the one hand, informal settlements are areas that differ from their surroundings by their morphology [28] and can thus be analyzed with quantitative statistical methods [29][30][31]. On the other hand, the development of these settlements cannot be described without analyzing the human behavior of their inhabitants, and future informal areas cannot be predicted if no models of this behavior are known. ...
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Urbanization is one of the defining trends of our time and appropriate models are needed to anticipate the changes in cities, which are largely determined by human behavior. In the social sciences, where the task of describing human behavior falls, a distinction is made between quantitative and qualitative approaches, each of which has its own advantages and disadvantages. While the latter often provide descriptions of exemplary processes in order to describe phenomena as holistically as possible, the goal of mathematically motivated modeling is primarily to make a problem tangible. Both approaches are discussed in terms of the temporal evolution of one of the dominant settlement types in the world today: informal settlements. These areas have been modeled in conceptual works as self-organizing entities and in mathematical works as Turing systems. It is shown that the social issues surrounding these areas need to be understood both qualitatively and quantitatively. Inspired by the philosopher C. S. Peirce, a framework is proposed in which the various modeling approaches describing these settlements can be combined to arrive at a more holistic understanding of this phenomenon by using the language of mathematical modeling.
... In the end, socioeconomic phenomena of deprivation and informality in urban development seem not reducible to the same visible built expression in space. On the contrary, the combined degrees of diversity and consistency in their visible urban form expression, as found in and supported by previous studies (Engstrom et al., 2022;Taubenböck et al., 2018), seems to reflect the complexity of social, economic and legal aspects intertwined in making urban "slums". ...
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Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed “EO + Morphometrics” approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world.
... That is, the intersection between various forms of survival and architectural inspirations, and in some cases also urban inspirations from the consolidated urban centre of Maputo, births an environment in which every square centimetre of space is used. From these simple iterations of 'city making', an organic, complex and dense urban system emerges, reflected by the improvised dwellings that generally have a different physical appearance from those of the so-called 'formal' city [107]. ...
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The current dimension of informal settlements in Maputo requires the definition of action models framed by empirical evidence, taking advantage of pre-existing socio-spatial and environmental conditions to define physical interventions through sustainable urban design strategies, with a view to their physical (and socio-economic) upgrading. Thus, this paper highlights the potential of urban design in the environmentally sustainable upgrading of Maputo’s informal neighbourhoods. This article aimed to develop sustainable and resilient urban design proposals and identify strategies capable of guiding the future process of physical territorial transformation towards a more sustainable model. Methodologically, a literature review was undertaken for the purpose of understanding the issues related to the theme and the general characteristics of informal neighbourhoods, as well as for exploring a case study: the George Dimitrov Neighbourhood. It was concluded that the current fabric of informal settlements possesses physical characteristics which facilitate the application of sustainable and responsive urban design strategies for the requalification of these deprived areas. Despite the marked difference between the spatial configurations of informal neighbourhoods and those of formal cities, it is possible to increase the level of resilience and sustainability of informal settlements through surgical and deep solutions, anchored on the particularities of the existing fabric.
... The study found out that the physical characteristic of slums is not consistent across the globe. However, it is also observed that a particular city usually replicate a similar slum structure (Taubenböck et al., 2018). Another study by Kuffer using high resolution satellite images highlighted that deprived areas are not homogeneous in their dimensions and considering them as one class ignores their vast diversity (Kuffer, 2017). ...
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Objective: The overall goal of this investigation is to describe the pattern of distribution of various socio-economic groups in Bhubaneswar, India. Background: Social stratification studies are inconsistent across countries. While the deprived communities are clustered around the center of the city in the US and Shanghai, recent Indian studies showed that they are mostly located at the periphery of the city. Most of these studies have used census data to differentiate the socio-economic groups. However, there are limited studies that examine the spatial structure of various socio-economic neighborhoods in India. This study is unique because it combines census data and satellite images to examine how different socio-economic neighborhoods are different geographically and spatially. Method: Census tract or ward is considered representative of neighborhoods. Census data on socio-economic indicators such as household size, literacy, home ownership, car-jeep ownership, availing banking facility, and workers population are used for an empirical PCA analysis. The wards are divided into five quintiles based on their deprivation index. A spatial study is performed for two of the most deprived neighborhoods and two of the least deprived neighborhoods using satellite maps. Results: The least deprived neighborhoods are located mostly around airports. However, the most deprived neighborhoods are located at the periphery. Informal housing or slums are not clumped in deprived neighborhoods only but are distributed throughout the city. All four wards studied are heterogeneous with informal housing combined with independent housing and apartments. Conclusion: Most deprived and least deprived neighborhoods are not differentiated based on informal housing locations but on parcel and apartment size
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Sub-Saharan Africa is currently experiencing growth in the number of people living in poverty, and the situation is worsening due to climate change and the COVID-19 pandemic. Cities are increasingly under stress because of urbanization and the demand for low-cost housing. Slum dwellers face daunting social, environmental, and economic challenges. Geospatial analysis of remote sensing, demographic, economic, social, and environmental data is being used to delineate slums. The application of circular economy guidelines for an intelligent transformation of slums combines technical and social innovation that reaches beyond the slums to the whole urban ecosystem. Examples of contributions to the circular economy are provided. Finally, some ideas are introduced on how the internet of things can improve access to goods and services and strengthen interconnectedness through the ability to participate more readily in the social dialogue of the city. The city of Accra in Ghana, West Africa, is discussed as a potential slum city to functional intelligent city transformation.
The diversity of informal settlement morphologies across locales makes their mapping inherently challenging in heterogeneous urban landscapes. The aim of this study was to evaluate the potential of pansharpening techniques on Sentinel 2A data, and textural features, in enhancing informal settlement identification accuracy in a fragmented urban environment. Brovey transform, intensity, hue and saturation transform, Environmental Systems Research Institute (ESRI), simple mean, and Gram–Schmidt techniques were employed to pansharpen multispectral bands of Sentinel 2A, bands 5, 6, and 7 in the first group, and bands 8A, 11 and 12 in another, using an average of bands 4 and 8 as the panchromatic band. The main objective was to investigate the efficacy of pansharpening Sentinel 2A imagery and texture analysis in automated mapping of morphologically varied informal settlements. An evaluation of the quality of fused images was undertaken through computation of the correlation between the spectral values of the original multispectral and pansharpened image. Grey-level-co-occurrence matrix texture features were extracted from the pansharpened images, and subsequently incorporated in the classification process, using a support vector machine classifier. Our results confirm that the Gram–Schmidt fusion technique yielded the highest informal settlement identification accuracy (F-score 95.2%; overall accuracy 91.8%). The experimental results demonstrated the potential of pansharpening Sentinel 2A, and the added value of image texture for a more nuanced characterisation of informal settlements.
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Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general context of digital twin (DT) creation are not sufficiently explored. Therefore, this review aims to identify appropriate urban 3D modelling method(s) for city DT, which can be used for 3D property valuation (3DPV) in the future (both short-term and long-term). We focused on 3D modelling studies investigating buildings and urban elements directly linked with residential properties. In total, 180 peer-reviewed journal papers were selected between 2016 and 2020 with a narrative review approach. Analytical criteria for 3D modelling methods were explicitly defined and covered four aspects: metadata, technical characteristics, users’ requirements, and ethical considerations. From this, we derived short-term and long-term prospects for 3DPV. The results provide references for integrating 3D modelling and DT in property valuation and call for interdisciplinary collaboration including researchers and stakeholders in the real estate sector, such as real estate companies, house buyers and local governments.
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Informal settlements behave very dynamical over space and time and the number of people living in such housing areas is growing worldwide. The reasons for this dynamical behavior are manifold and are not matter of this article. Nevertheless, informal settlements represent a status quo of housing and living conditions which is from a humanitarian point of view in the most cases below acceptable levels. Therefore, reliable spatial information about informal settlements is vital for any actions of improvement of these living conditions. Since remote sensing data is a well suited data source for mapping and monitoring we demonstrate a methodology to detect informal settlements (favelas) from QuickBird data using an object-based approach. On the one hand we therefore use experiences and adapt them which were already made by Hofmann, P. (2001) regarding the image segmentation of an IKONOS scene of Cape Town. On the other hand we resort to a general ontology of informal settlements which we then transfer to a fuzzy-logic rule base which acts as basic classifier of the generated segments. This basic rule base is than extended in a way that features of segregation given by the ontology (namely neighbour hood) are applied to the extraction method as an iterative process (i.e. a knowledge based region growing). Finally, we assess the results of the simple and iterative method by comparing them with the results of a manual mapping.
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Over the last decades, massive urbanization processes lead to the emergence of large slum areas making them home to about a seventh of the global population. Although the variety of morphological characteristics varies significantly within as well as across cities, common determinants exist. Informal, or unplanned settlements in particular, do show similar morphologies over the world. They are characterized mostly by extremely high building densities and small building sizes, irregular arrangement of buildings and street network and are often located at exposed sites . Based on these characteristics, we deploy satellite images for a systematic mapping of morphological slum areas in the city of Rio de Janeiro, Brazil based solely on physical characteristics and analyse the mapping result with the official census data. Outcomes show first that morphological slums are a semantic and spatial sub-group of all slum areas contained by the Brazilian census and that remote sensing-based mapping yields accuracies of almost 94%. Second, analysis of census-based income data proofs that while almost 45% of all mapped slum blocks are characterized by incomes below the poverty line, as defined by the Organisation for Economic Co-operation and Development (OECD), this holds true for only about 6% of the formal urban neighbourhoods.
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Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.
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Driven by massive urbanization processes, particularly in developing countries, people flock into the cities resulting in an evolution of large slum areas. Mapping and monitoring of slum areas have become an invaluable source for decision-making processes to implement policies related to improve living conditions. Space-borne remotely sensed data has been explored in the past for slum mapping, however, to a large extent supported by optical imagery. In this paper, we explore the capabilities of dual-polarized (HH/VV and VV/VH) X-band Synthetic Aperture Radar (SAR) from TerraSAR-X images for slum extent mapping using the Kennaugh element framework for image preprocessing. In this way, spatial image descriptors based on texture, morphological profiles and polarimetric features have been tested at various window sizes [11 × 11, …, 161 × 161] for mapping slums using the random forest classifier in a series of experiments. For benchmarking the classification results, LDA as parametric linear classifier is used for comparison. Classification performance was evaluated by comparison with a reference map indicating that texture features hold the highest contribution to discriminating slums from other urban structures. Best window size was found using a spatial neighborhood of 81 × 81 pixels resulting in Overall Accuracy of 88.58 and Kappa of 0.7809 for RF classifier. A patch-based analysis of classification results reveals areal dependencies of the classifier in terms of larger slum patches that are mapped with higher precision than smaller patches. Analyses including additional spatial image descriptors based on mathematical profiles reveal no significant contribution to the classification result.
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Density is among the most important descriptive as well as normative measures in urban research. While its basic concept is generally understandable, approaches towards the density measure are manifold, diverse and of multidimensional complexity. This evolves from differing thematic, spatial and calculative specifications. Consequently, applied density measures are often used in a subjective, non-transparent, unspecific and thus non-comparable manner. In this paper, we aim at a systematic deconstruction of the measure density. Varying thematic, spatial and calculative dimensions show significant influence on the measure. With both quantitative and qualitative techniques of evaluation, we assess the particular influences on the measure density. To do so, we reduce our experiment setting to a mere physical perspective; that is, the quantitative measures building density, degree of soil sealing, floor space density and, more specifically, the density of generic structural classes such as open spaces and highest built-up density areas. Using up-to-date geodata derived from remote sensing and volunteered geographic information, we build upon high-quality spatial information products such as 3-D city models. Exemplified for the comparison of two European megacities, namely Paris and London, we reveal and systemize necessary variables to be clearly defined for meaningful conclusions using the density measure. Density is among the most important descriptive as well as normative measures in urban research. While its basic concept is generally understandable, approaches towards the density measure are manifold, diverse and of multidimensional complexity. This evolves from differing thematic, spatial and calculative specifications. Consequently, applied density measures are often used in a subjective, non-transparent, unspecific and thus non-comparable manner. In this paper, we aim at a systematic deconstruction of the measure density. Varying thematic, spatial and calculative dimensions show significant influence on the measure. With both quantitative and qualitative techniques of evaluation, we assess the particular influences on the measure density. To do so, we reduce our experiment setting to a mere physical perspective; that is, the quantitative measures building density, degree of soil sealing, floor space density and, more specifically, the density of generic structural classes such as open spaces and highest built-up density areas. Using up-to-date geodata derived from remote sensing and volunteered geographic information, we build upon high-quality spatial information products such as 3-D city models. Exemplified for the comparison of two European megacities, namely Paris and London, we reveal and systemize necessary variables to be clearly defined for meaningful conclusions using the density measure.
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The body of scientific literature on slum mapping employing remote sensing methods hasincreased since the availability of more very-high-resolution (VHR) sensors. This improves the abilityto produce information for pro-poor policy development and to build methods capable of supportingsystematic global slum monitoring required for international policy development such as theSustainable Development Goals. This review provides an overview of slum mapping-related remotesensing publications over the period of 2000–2015 regarding four dimensions: contextual factors,physical slum characteristics, data and requirements, and slum extraction methods. The review hasshown the following results. First, our contextual knowledge on the diversity of slums across theglobe is limited, and slum dynamics are not well captured. Second, a more systematic exploration ofphysical slum characteristics is required for the development of robust image-based proxies. Third,although the latest commercial sensor technologies provide image data of less than 0.5 m spatialresolution, thereby improving object recognition in slums, the complex and diverse morphology ofslums makes extraction through standard methods difficult. Fourth, successful approaches showdiversity in terms of extracted information levels (area or object based), implemented indicator sets(single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machinelearning). In the context of a global slum inventory, texture-based methods show good robustnessacross cities and imagery. Machine-learning algorithms have the highest reported accuracies andallow working with large indicator sets in a computationally efficient manner, while the upscalingof pixel-level information requires further research. For local slum mapping, OBIA approaches showgood capabilities of extracting both area- and object-based information. Ultimately, establishing a moresystematic relationship between higher-level image elements and slum characteristics is essential to trainalgorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.
Conference Paper
In this paper we use image texture and morphological profiles for mapping slums in Sentinel-2A imagery. Varying sizes of the respective spatial descriptors (GLCM, differential morphological profiles) are tested for classification using a random forest classifier. Results are interpreted based on pixel-based and patch-based accuracy assessment. Best classification results have been reached at the pixel-based level with a kappa of 81.65 for the combined feature set with both GLCM and DMP. At the patch level, the analyses show that higher accuracies are reached with large kernel sizes and detection is better for large slum areas.
Conference Paper
Slums are among the most visible manifestation of urban poverty. In this vein, earth observation (EO) has been widely accepted as a tool to approximate associated socioeconomic disparities at the city level. In this work, we explore the potential of a novel data source – location-based social networks – in conjunction with EO-based slum maps. Applying meaningful location quotients for spatial clustering of digital hot and cold spots in an experimental setting, we find that such data can add generalized spatial knowledge to space-based methods via the designation of less digitally-oriented population groups. Conversely, slums derived from remote sensing show substantial quantitative correspondence with clustering results, and thus, even enable to reflect underlying intra-urban socioeconomic characteristics.
This article examines survival among homeless persons ("pavement dwellers") in Delhi, India. In particular, we explore the role of formal and informal relationships in meeting the demands of daily existence and how and when public social welfare programs assist pavement dwellers. Over fifteen months, beginning in 2013, participant observation was conducted, and approximately 200 individuals (homeless persons, government officials, and NGO representatives) were interviewed in Hindi or English. Triangulated data including documents, interviews, and fieldnotes were subjected to thematic analyses. Results produced five themes: persistent illegality, dependence on charitable others, personhood and worthiness, migration and social isolation, and precarious relationships and distrust. Based on the research findings, we make recommendations for legal inclusion, decriminalization, access to health care, and income support for parents with dependent children. Broader concerns about global homelessness are also discussed in the context of growing income inequality.
Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology.