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Journal of Urban Technology
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Revisiting the Spatial Definition of Neighborhood
Boundaries: Functional Clusters versus
Pablo Martí, Leticia Serrano-Estrada, Almudena Nolasco-Cirugeda & Jesús
To cite this article: Pablo Martí, Leticia Serrano-Estrada, Almudena Nolasco-Cirugeda &
Jesús López Baeza (2021): Revisiting the Spatial Definition of Neighborhood Boundaries:
Functional Clusters versus Administrative Neighborhoods, Journal of Urban Technology, DOI:
To link to this article: https://doi.org/10.1080/10630732.2021.1930837
© 2021 The Author(s). Published by Informa
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Revisiting the Spatial Deﬁnition of Neighborhood Boundaries:
Functional Clusters versus Administrative Neighborhoods
, Leticia Serrano-Estrada
, Almudena Nolasco-Cirugeda
Jesús López Baeza
Building Sciences and Urbanism Department, University of Alicante, Alicante, Spain;
Digital City Science,
HafenCity University Hamburg, Germany
This study revisits the debate surrounding the deﬁnition of
neighborhood boundaries by addressing the disconnect between
the city’s Administrative Neighborhoods and its functional
organization. A method is proposed for dividing the city into
more meaningful units through the spatial distribution of urban
activities by retrieving data from Google Places. The dataset was
pre-processed and spatially divided into Functional Clusters. A
comparison between functional and administrative subdivisions
of the city was undertaken, from which three overall conclusions
could be drawn. First, a function-based city partition allows
economically active urban areas to become the neighborhood’s
center, thereby creating a polynuclear neighborhood structure
that would potentially encourage greater cross-movement of
people throughout the city. Second, the specialization of
activities becomes more evident in Functional Clusters than in
Administrative Neighborhoods. Third, access to up-to-date data
makes possible a timely diagnosis of the quantity and diversity of
urban activities—i.e., economic activities, services, and facilities—
through Google Places data. The value of this contribution is to
inform urban decision-making and policies in order to better
balance the provision of a neighborhood’s economic activity.
functional clusters; urban
economic activities; Google
Places; social networks
Economic activity growth in cities has brought new challenges to the design of urban inter-
vention strategies (Kärrholm, 2012). As suggested by Zukin (1993), the transformation of
city “landscapes of production”into “landscapes of consumption”has aﬀected urban plan-
ning and the design of city spaces (Kärrholm, 2012; Mubi Brighenti and Kärrholm, 2018).
Moreover, the morpho-spatial reconﬁguration of cities is greatly inﬂuenced by the concen-
tration of economic activities (Kärrholm, 2012;SaraivaandPinho,2016). Indeed, the link
between the physical aspects of the urban form and the clustering of city functions contrib-
utes to create a speciﬁc type of urban life (Crooks et al., 2015;Kropf,1996).
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
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medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Dr Pablo Martí firstname.lastname@example.org University of Alicante, Carretera San Vicente del Raspeig s/n, 03690
San Vicente del Raspeig, Alicante, Spain.
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
JOURNAL OF URBAN TECHNOLOGY
Urban activity together with other factors (i.e., social preferences and urban mor-
phology) have a signiﬁcant impact on the perceived character of neighborhoods (Cran-
shaw et al., 2012; Kropf, 1996). In fact, “the availability and easy access to the range of
goods and services that provide for residents”necessities and amenities give neighbor-
hoods a sense of place and identity (Mehta and Mahato, 2018:2). Indeed, at cognitive
and perceptual levels, neighborhood units are largely deﬁned by the association of
spatial features to socioeconomic and behavioral aspects (Kropf, 1996; Lynch, 1960).
On this basis, many authors have highlighted the importance of considering the types
of functions and establishments when partitioning the city (Brown, 1991,1993; Cran-
shaw et al., 2012; Rösler and Liebig, 2013; Saraiva and Pinho, 2016) given that a concen-
tration of speciﬁc economic urban environments may contribute to the fragmentation of
the urban landscape (Graham and Marvin, 2002).
Traditionally, the catchment area of speciﬁc services—typically deﬁned by administra-
tive partitions—has been a key criterion for the deﬁnition of neighborhood boundaries
(Devine, 1975; Perry, 1929). Frequently, these administrative partitions do not correspond
to the current functional organization of the city. Lack of awareness of this disconnect chal-
lenges the eﬀective provision of economic activities, services, and facilities, aﬀecting social
indicators such as equity and accessibility planning (Abdullahi et al., 2018;Omar,2019;
Serag El Din et al., 2013), which may thereby result in poor decision-making while
deﬁning urban intervention and regeneration strategies. The identiﬁcation of neighbor-
hood units regarded as functional urban areas has been approached from a variety of per-
spectives in the Urban Studies and Planning literature (Chen et al., 2017). Research
dedicated to this issue contributes towards a better understanding of the character of the
urban landscape and makes some headway in deﬁning accurate public policies and enhan-
cing the development of more eﬀective urban intervention proposals (Gu, 2010; Kropf,
1996; Saraiva and Pinho, 2016). Grounded in the abovementioned theoretical reasoning,
this research addresses the knowledge gap identiﬁed by Ellen and Turner (1997:855)
regarding the need for a method to deﬁne more “meaningful neighborhood boundaries.”
Therefore, the aim of this study is to propose a method for partitioning the city into mean-
ingful neighborhood units—hereafter referred to as Functional Clusters—that reﬂect the
spatial distribution of economic activities, services, and facilities. The proposed method
is applied to an illustrative case study, where the delimitation of the Administrative Neigh-
borhoods is compared to the Functional Clusters. The hypothesis is that Functional Clus-
ters better reﬂect the character of the urban environment than existing traditional
Administrative Neighborhood partitions.
The novelty of this research is twofold. First, the method for obtaining Functional
Clusters uses Google Places data as a source of information on today’s urban activity
and elaborates a process that allows diﬀerent degrees of granularity in the analysis.
Second, the comparison between Functional Clusters and Administrative Neighbor-
hoods contributes to the contemporary methodological and theoretical debate on the
spatial deﬁnition of neighborhood boundaries.
This paper is structured as follows: the literature section covers some of the most rel-
evant research on both the deﬁnition of spatial neighborhood boundaries and the assess-
ment of spatial patterns related to urban activity through social media; next, a description
of the illustrative case study and the sources is given, followed by an explanation of the
method; ﬁnally, the results are presented, discussed, and the main conclusions are drawn.
2P. MARTÍ ET AL.
Spatial Neighborhood Boundaries
Jenks and Dempsey (2007) provided a comprehensive outline of the various scholarly
deﬁnitions of the term “neighborhood,”but, in general terms, scholars agree that neigh-
borhoods are “a geographically deﬁned subarea of the city where residents are presumed
to share both spatial proximity and some degree of mutual circumstance”(Chaskin et al.,
2001:98). Furthermore, these are “subareas of towns and cities whose physical or social
characteristics distinguish them from one another”(Rohe, 2009:99). Thus, the term
“neighborhood”encompasses spatial and non-spatial features, such as, intangible
social elements (Jenks and Dempsey, 2007). Therefore, when both place and human
activity are interlinked—as Hallman (1984:102) suggested, “through occupancy and
use by its residents”—a geographical area can be considered a neighborhood.
Nonetheless, the delimitation of the city’s spatial subdivisions based merely on human
activity is a challenging task, which is one of the main reasons for the controversial and
longstanding debate on the relevant determinants for deﬁning neighborhood boundaries
(Jenks and Dempsey, 2007). Previous work in the ﬁeld has considered city subdivisions
based on census tracts and administrative areas (Steiger et al., 2015), statistically deﬁned
clusters (Martin, 1998), and land use classiﬁcation (García-Palomares et al., 2017). As
these numerically-based subdivision criteria may “fail to accurately represent the neigh-
borhood conditions that make a diﬀerence in people’s lives”(Ellen and Turner,
1997:844), other more citizen-oriented approaches have considered perceptual and cog-
nitive information for identifying neighborhood boundaries (Cranshaw et al., 2012;
This paper adopts Hallman’sdeﬁnition of neighborhoods as functional entities that
support residents’needs for services and facilities (Hallman, 1984; Jenks and Dempsey,
2007), taking into account that the quantity, quality, and diversity of uses and functions
in a neighborhood encourage or discourage activities that involve using and staying in
urban spaces (Rohe, 2009). These factors aﬀect social and economic dimensions (Ellen
and Turner, 1997) and, in turn, the urban livability of the space (Mehta and Mahato,
2018). This approach recognizes that communities can be weakened by the dispersion
of people and activities (The Urban Task Force, 2003) or the lack of nearby facilities, insti-
tutions, and services, which forces residents to “concentrate activity and connections
beyond rather than within the neighborhood”spatial limits (Chaskin et al., 2001:9; Fur-
stenberg, 1993). Therefore, deﬁning more meaningful boundaries based on the availability
and proximity of urban activities could potentially encourage residents to stay within the
neighborhood, thus supporting the livability of its urban spaces.
Urban Activity Patterns through Location-Based Social Networks
Pioneering research in the identiﬁcation of geographical urban activity patterns—spatial
distribution of economic activities, services, and facilities—was traditionally carried out
by using store-by-store and street-by-street data collection and clustering techniques
(Guy, 1976; Lee and McCracken, 1982; Saraiva and Pinho, 2016). The conclusions of
this approach are similar to those that use diﬀerent techniques involving more techno-
logical processes for data collection and categorization, as well as spatial analysis and
JOURNAL OF URBAN TECHNOLOGY 3
clustering (Hossain, 1999; Saraiva, 2013; Sarma, 2006). In situ data collection is comple-
mented and/or substituted by online catalogues, or oﬃcial registers from local govern-
ments and other institutions, such as National Statistics Institute, commerce
associations, etc. Clustering, among other types of analysis, has been calculated by
using specialized software.
Nowadays, the great data collecting potential of crowd-sourced social media is being
exploited to analyze a diverse range of topics related to the functional organization of the
city (Arribas-Bel and Tranos, 2018), such as the relationship between urban form and
function (Crooks, Pfoser et al., 2015; Crooks, Croitoru et al., 2016); the identiﬁcation
of POIs—points of interest—(Van Canneyt et al., 2012; Deng and Newsam, 2017;
García-Palomares et al., 2015; Van Weerdenburg et al., 2019) and their accessibility in
terms of density and diversity (Shen and Karimi, 2016); the characterization of liveli-
hoods according to the collective behaviors of residents (Cranshaw et al., 2012); and
the delimitation of functional areas to understand social and spatio-temporal aspects
of the city (Chen et al., 2017; Rösler and Liebig, 2013).
In line with these studies, this research sources geolocated data from the social
network Google Places, which has previously been used to identify economic activity
for measuring urban entropy and urban complexity (Bustos Hernándes, 2015; López
Baeza et al., 2017; Serrano-Estrada et al., 2016), as well as to research socioeconomic pat-
terns of urban areas (Cenamor et al., 2017).
Illustrative Case Study and Sources
Alicante City, Spain was used as an illustrative case study as it is representative of other
mid-sized Mediterranean cities that have encouraged modern commercial, tourist, and
leisure formats in the last decades (Nieto, 2015). This provincial capital, at the heart of
the Spanish Mediterranean Arc, with 329,988 registered inhabitants (Spanish Statistics
Institute, 2018), is considered an important European holiday destination hub (Suau-
Sanchez and Burghouwt, 2012). Moreover, like other Mediterranean cities, Alicante
has at least two diﬀerent types of administrative spatial divisions that do not correspond
with each other, nor do they reﬂect the social or functional reality of the city. For
instance, this municipality is divided into eight census districts, which are subdivided
into 253 census sections (Spanish Statistics Institute, 2018), and into 42 Administrative
Neighborhood areas according to the oﬃcial database of the Urban Guide of the City of
Alicante (Ayuntamiento de Alicante, 2018). The latter division reﬂects the historical
growth of the city, observable in the existing patterns of the urban fabric. However,
neither division considers today’s dynamic socioeconomic perspective suggested by,
for example, the distribution and types of urban activities (Noulas et al., 2013) that are
crucial for determining the direction of urban interventions (Mankalpa et al., 2015;
Temes Cordóvez et al., 2016).
Two main sources were used for this study: The administrative neighborhood spatial
delimitation of Alicante municipality and the geo-location data from Google Places. The
spatial delimitation of Alicante’s 42 administrative neighborhoods (Ayuntamiento de
Alicante, 2018) was used as the city’s baseline division. The spatial extent of all admin-
istrative neighborhoods together was considered as the area within which Google
Places data were collected. The software program Social Media Urban Analyzer
4P. MARTÍ ET AL.
(SMUA) (Martí et al., 2019), which retrieves data via Google Places was used for this
purpose. The data from Google Places were retrieved as a listing of placeswhich
include all urban activities and open public spaces registered in the platform within
the search area up to the date of retrieval—January 23, 2018. The raw dataset included,
in total, 57,578 geo-referenced places.
Each listed place has associated metadata. From the large quantity of data variables
available through the Google Places API, this research selects and retrieves only the fol-
lowing: the spatial coordinates—latitude and longitude—the name of the place; the ID
—place unique identiﬁer number—and the place type, which are tags or descriptors.
These place types in Google Places datasets are relevant for this study since they
provide detailed information as to what kind of urban activity occurs in each place
Identifying and Delimiting Functional Clusters Using Google Places Data
The method adopted consisted of three phases. First, data pre-processing was necessary
to reﬁne the places in the Google Places dataset. Second, the place types were categorized
into the hierarchical APA benchmark categories (American Planning Association,
2018a). Finally, identiﬁcation, spatial delimitation, and analysis of Functional Clusters
were carried out.
Pre-Processing of Google Places Retrieved Data
The pre-processing of the raw dataset involved the following stages (See Figure 1):
Figure 1. Pre-processing data summary
JOURNAL OF URBAN TECHNOLOGY 5
.elimination of duplicate data
.validation of places related to urban activities—speciﬁcally economic activities, ser-
vices, and facilities
.re-assignment of Google Places place types when necessary.
These steps ensured that each datapoint represented a unique register and was correctly
assigned to a pre-deﬁned Google Places place type (Google Developers, 2018).
Elimination of Duplicate Place Listings. A script was designed to eliminate duplicate
place listings by considering three speciﬁc data variables: name of the place; place ID;
and geographic coordinates.
Google Places assigns a unique ID to each place (Google Developers, 2018), and the
criteria adopted for eliminating duplicate listings were twofold. Places are considered
duplicate listings if they have the same ID, or if they have a diﬀerent ID but the same
name and the same geographic coordinates. Those places with diﬀerent IDs, diﬀerent
names, and the same geographic coordinates are considered unique as they may, for
example, be diﬀerent economic activities within the same building.
Validation of Places. Recognizing which datapoints represent economic activities, ser-
vices, and facilities entailed revising their originally assigned place types. In total,
Google Places currently supports 128 standard predeﬁned place types. However, the
raw retrieved dataset for Alicante included only 103 place types. This is because the diver-
sity of urban activities changes from one city to another.
Another relevant consideration is that place types are automatically assigned by
Google or manually designated by the individual who is registering a service or facility
in the platform. Thus, according to the authors’experience, some places may include
more or fewer place types or descriptors than others.
Some of these place types are ambiguous and misleading; for example, the establish-
ment place type which, as opposed to electronics_store, does not oﬀer enough information
on the kind of economic activity that takes place in the space. In view of the above obser-
vations, this study adopts the most speciﬁc place type assigned to each place.
After eliminating duplicate registers, three types of datapoints were recognizable: valid
datapoints, invalid datapoints, and checkable datapoints. The valid datapoints are those
places which represent mostly urban activities, i.e., economic activities, services, and
facilities. These include a signiﬁcant number of datapoints assigned to the generic
store place type which does not provide enough information about the type of activity.
The registers tagged with this ambiguous place type have been checked and reassigned,
where possible, a more speciﬁc place type.
The invalid datapoints are those places that do not represent services or facilities and,
therefore, were excluded. Invalid datapoints included in the case study dataset can be
grouped as follows:
.geometric regions that do not represent services or facilities: administrative_area_le-
vel_1, 2, 3, 4, and 5; colloquial_area; country; geocode; locality; neighborhood; political;
6P. MARTÍ ET AL.
postal_ code; postal_code_preﬁx; postal_code_suﬃx; postal_town; sublocality; subloca-
lity_level_1, 2, 3, 4, and 5; and subpremise.
.datapoints that represent street names and/or are assigned by Google Places to the
place types: intersection; route; street_address; and street_number.
.datapoints that represent places with no economic activity. These are mostly the place
types: parks; plazas; natural_features; landmarks; and campground.
The checkable datapoints that require reviewing before being considered valid or invalid
datapoints can be divided into two groups:
(1) Datapoints assigned to the lodging place type include diﬀerent types of lodgings
(apartments, residential buildings, hotels, etc.) that, in some cases, may be con-
sidered as an economic activity, for example, hotels. Residential only buildings are
not considered as an economic activity.
(2) Datapoints assigned to several broad and generic place types, namely, Establishment,
Point_of_Interest, and Premise (EPP), that may or may not contain economic activi-
ties and would be better assigned to a more speciﬁc place type that reﬂects their type
Re-Assigning Place Types to Google Places Checkable Datapoints. This step was useful
for the grouping and homogenization of data into reﬁned place types. This procedure
consisted of ﬁrst, assigning datapoints from the categories EPP and store to a speciﬁc
pre-deﬁned Google Places place type; and second, separating and assigning the hotel
place type to hotels that were originally tagged as lodging and not as hotel.
From all the EPP datapoints retrieved (10,669), only six were originally assigned to both
premise and park place types. As for the rest, it was necessary to resort to the place name vari-
able to identify the type of activity of each datapoint and reassign a place type. All the words
included in the EPP datapoint place names weresortedbyfrequency,usingasimplePython
and the 1,500 most frequented terms were closely analyzed to see if they could be
associated with a pre-deﬁned Google Places place type. For instance, place names containing
the words “restaurant,”“meson,”and “casa de comidas”were assigned to the Google Places’
food place type. Terms related to street names were considered invalid EPP datapoints.
Finally, 76 places were eliminated as they did not represent an economic activity.
The store place type was too generic and thus, the same process was adopted for reas-
signing place types. Datapoints originally assigned as lodging were reassigned a place
type according to their kind of accommodation. Only those datapoints reassigned to
the hotel category have been considered as economic activities:
.Residential: residential buildings and condos—urbanización, ediﬁcio residencial
.Hotel: tourist accommodation, such as hotels and hostels
.Apartment: places deﬁned as rental apartments—short-term accommodation
.Lodging: places that could not be included in the previously mentioned place types.
Some of these places have been registered in Google Places with the name of a
person or a random name that does not provide further information about the type
of accommodation (e.g., La Casita, La Lonja, and Maralic).
JOURNAL OF URBAN TECHNOLOGY 7
Additionally, new place types were created for those places whose names provided
enough information about the type of economic activity, but no suitable predeﬁned
Google Places place type was found. These new place types are: association; cleaning_ser-
vice; design_studio; events_venue; industry; leisure; logistics; lottery; professional_service;
theater; and, electronics_repair. Over 25.94 percent of EPP datapoints were assigned a
place type using the described criteria and 76 (9.20 percent) datapoints in the lodging
category were assigned to the hotel place type.
Reﬁned Google Places Dataset
After data pre-processing, there were 23,845 operating datapoints remaining in the
reﬁned Google Places dataset (See Figure 1) which represent 70 percent of the unique
dataset datapoints. These included: 21,002 originally valid datapoints; 2,767 EPP data-
points with an assigned Google Places place type; and 76 hotel datapoints. As for the
number of reﬁned place types, considering both Google Places and newly created place
types, 107 place types were obtained in total.
Recategorizing Operating Datapoints: from Google Places Categories to APA
Land Based Classiﬁcation Standards Benchmark Categories
In order to permit an analysis of the types of economic activities, services, and facilities
with diﬀerent degrees of granularity, a grouping of the Google Places place types is pro-
posed following previous research from Martí et al. (2019). To this end, the Land Based
Classiﬁcation Standards categories have been adopted (American Planning Association,
2018a) as benchmark categories. Speciﬁcally, this classiﬁcation is the one that addresses
the “functional dimension,”one of the ﬁve available LBCS dimensions, which refers to
“the economic function or type of establishment using the land”(American Planning
Association, 2018b). This hierarchical classiﬁcation provides an overall ﬁne-grain land
use class taxonomy of nine Level 1 categories, 47 Level 2 categories, and 159 Level 3 cat-
egories (Deng and Newsam, 2017). All reﬁned Google Places place types were assigned to
APA Level 1 (See Figure 2) and Level 2 (See Table 1) category codes, respectively.
From the 47 APA Level 2 categories originally available, the case study’sGoogle Places
and newly proposed place types could only be assigned to 28 APA Level 2 categories. The
remaining 19 Level 2 categories have not been used. Speciﬁcally, no place types were
assigned to the following APA Level 2 subcategories:
▪1100 Private household
▪1200 Housing services for the elderly
▪3200 Wood, paper, and printing products
▪3300 Chemicals, and metals, machinery, and electronics manufacturing
▪4300 Utilities and utility services
▪5500 Natural and other recreational parks
▪7200 Machinery related
▪7400 Heavy construction
▪8100 Oil and natural gas
▪8200 Metals—iron, copper, etc.
8P. MARTÍ ET AL.
Table 1. Number of Google Places and newly proposed place types included in APA Level 2
subcategories according to the case study’s operating datapoints
APA Level 2 sub-categories
Google Places place types and newly proposed place
Number of place
types per APA Level 2
1 1300 Hotels, motels, or other
2 2100 Retail sales or service store; ﬂorist; health; grocery_or_supermarket;
clothing_store; car_repair; gas_station; pharmacy;
locksmith; car_dealer; furniture_store; bicycle_store;
home_goods_store; book_store; car_wash;
electronics_store; shopping_mall; hardware_store;
shoe_store; liquor_store; convenience_store;
art_gallery; supermarket; department_store
3 2200 Finance and Insurance atm; ﬁnance; insurance_agency; bank 4
4 2300 Real estate, and rental and
real_estate_agency; car_rental 2
5 2400 Business, professional,
scientiﬁc, and technical
lawyer; professional_service; travel_agency;
copy_store; veterinary_care; accounting;
moving_company; design_studio; electronics_repair;
6 2500 Food services food; bar; café; restaurant; meal_delivery; night_club;
7 2600 Personal services parlor; laundry; hair_care; beauty_salon 4
8 2700 Pet and animal sales or
service (except veterinary)
9 3100 Food, textiles, and related
10 3400 Miscellaneous
industry; jewelry_store 1 1
11 3500 Wholesale trade
12 3600 Warehouse and storage
13 4100 Transportation services parking; bus_station; light_rail_station; post_oﬃce;
airport; taxi_stand; transit_station; train_station;
14 4200 Communications and
15 5100 Performing arts or
stadium; movie theater; theater; events_venue 2 2
16 5200 Museums and other special
museum; natural_feature; zoo 3
17 5300 Amusement, sports, or
casino; gym; spa; amusement_park; bowling_alley;
18 5400 Camps, camping, and
19 6100 Educational services school; university 2
20 6200 Public administration courthouse; local_government_oﬃce; city_hall 3
21 6300 Other government
22 6400 Public Safety ﬁre_station; police 2
23 6500 Health and human services doctor; physiotherapist; hospital; dentist 4
24 6600 Religious institutions hindu_temple; place_of_worship; mosque; church 4
25 6700 Death care services funeral_home; cemetery 2
26 6800 Associations, nonproﬁt
27 7100 Building, developing, and
28 7300 Special trade contractor electrician; rooﬁng_contractor; plumber; painter 4
JOURNAL OF URBAN TECHNOLOGY 9
▪8400 Non-metallic mining
▪8500 Quarrying and stone cutting establishment
▪9100 Crop production
▪9200 Support functions for agriculture
▪9300 Animal production including slaughter
▪9400 Forestry and logging
▪9500 Fishing, hunting and trapping, game preserves
▪9900 Unclassiﬁable function.
As can be observed, the majority of these subcategories correspond to the APA Level 1
categories (8000 Mining and extraction establishments, and 9000 Agriculture, forestry,
ﬁshing, and hunting) (See Figure 2).
The reﬁned place types classiﬁcation provides ﬁne grain information as to speciﬁcusesin
an urban area. For instance, hair_care and beauty_salon are considered, in the reﬁned
dataset,as two diﬀerent placetypes. As previously stated, theAPA category classiﬁcationpro-
vides a hierarchical approach, grouping economic activities ofthe same type. In this case, the
hair_care and beauty_salon Google Places place types are grouped into the APA 2000 General
Sales and Services Level 1 (main category), 2600 Personal Services Level 2 (subcategory).
Therefore, in this research, the reﬁned Google Places place types are used to infer the
diversity of economic activities, services, and facilities within each urban unit, whereas
the APA categorization is useful to recognize the functional character of clusters separ-
ately and in relation to the rest of the clusters.
Delimitation of Functional Clusters
The operating datapoints are visualized by means of cartography and grouped into 42
Functional Clusters (FC 0 to FC 41) by using Quantum GIS open-software. The
spatial delimitation of these 42 clusters is compared to the existing 42 Administrative
Neighborhoods (AN 1 to AN 42). This process comprised two phases: the cluster calcu-
lation, and the cluster borders deﬁnition.
A spatial cluster calculation by proximity of the reﬁned datapoints was performed
using a k-Means clustering algorithm, a popular clustering method in literature,
adopted across diﬀerent domains (Fortunato, 2010; Khan and Ahmad, 2004), and
Figure 2. Google Places operating datapoints and their respective place types assigned to APA Level 1
10 P. MARTÍ ET AL.
useful to partition topological nodes into groups. This algorithm presents quick conver-
gence to an acceptable result and, after a certain number of iterations, the position of the
centroids remains stable (Fortunato, 2010). The only problems raised in the literature are
the need for (a) experimenting with diﬀerent numbers of clusters, and (b) close visual
exploration of the resulting clusters for further validation (Béjar et al., 2016). To over-
come these limitations several trial runs were conducted to test clustering results
yielded by diﬀerent K-values (See Figure 3, left). The experiments were performed con-
sidering K-values from k=5 to k=50. It was observed that low K-values (i.e., k=5 and k=6)
resulted in rather diﬀerent clustering results. However, higher K-values (i.e., k=40 to
k=51) yielded very similar clustering partitions (See Figure 3, right). Therefore, in this
speciﬁc case study, k=42 is selected as the K-value because the resulting clusters
provide consistency—as results started to converge around k=40, and it coincides with
the number of Administrative Neighborhoods, thus allowing a straightforward
Once the clusters were iteratively calculated by the k-Means algorithm and all data-
points were spatially linked to the centroid of the closest cluster using the Euclidean dis-
tance, each of the 42 clusters and their corresponding datapoints were given a code
number and a color to aid visual representation.
The Euclidean distance was considered instead of Manhattan or Network distances for
several reasons. First, no meaningful diﬀerences were identiﬁed when compared to the
cluster subdivision using Manhattan distance, and second, the Euclidean distance is
quicker for computer calculations. Furthermore, the diﬀerences between Manhattan
and Network distances are not signiﬁcant in the speciﬁc case of Alicante and are unlikely
to be in other Spanish Mediterranean cities whose urban fabric is mainly based on a
regular orthogonal grid (See Figure 4a and b).
The 42 clusters of datapoints obtained lack border lines. Therefore, a Voronoi diagram
was constructed for partitioning of the area into 23,845 coded Voronoi polygonal regions
Figure 3. Left: Voronoi experiments in increasing number of K-values from 5 to 42. Right: Superim-
posed representation of Voronoi experiments with high K-values ranging from 40 to 51
JOURNAL OF URBAN TECHNOLOGY 11
Figure 4. (a) Alicante’s identiﬁed 42 Functional Clusters (b) Alicante’s existing 42 Administrative
Neighborhoods; (c) Voronoi polygonal regions and (d) deﬁnition of Functional Clusters by grouping
these polygons; (e) Alicante’s central area exemplifying how economically active segments of axes
fall within Functional Cluster areas
12 P. MARTÍ ET AL.
whose center is each datapoint (See Figure 4c). The aggregation of the coded Voronoi
polygonal regions forms shapes that ultimately deﬁne the cluster borderlines (See
Figure 4d). Each spatial partition represents a grouped unit of economic activities, ser-
vices, and facilities, and thereby a Functional Cluster.
Administrative Neighborhoods vs. Delimited Functional Clusters
The Functional Clusters obtained were analyzed and compared to Administrative Neigh-
borhoods taking into account two diﬀerent aspects: their physical characteristics (shape
and size), and, their functional features, in terms of the diversity and specialization of
economic activities, services, and facilities. For this purpose, the two data categorizations
dealt with in the previous sections were considered: the reﬁned Google Places categories
and the benchmark APA categories.
The resulting tessellation of Functional Clusters diﬀers signiﬁcantly to that of the city’s
recognized Administrative Neighborhoods. In fact, the size and shape of the polygon
delimitations are more homogeneous than those of the Administrative Neighborhoods.
Moreover, these polygons progressively increase in size as they get further away from the
city center because both the amount and the proximity of economic activities, services,
and facilities decreases in the less compact urban areas of the periphery.
Unlike what is observed in the Administrative Neighborhoods, where most active
street segments and intersections tend to be located along the neighborhood boundary
assigning each side of the street to diﬀerent neighborhoods, in the Functional Cluster,
tessellation all of these active axes and nodes become the cluster’s core, thus creating a
polynuclear structure of city neighborhoods (See Figure 4e). Moreover, when one of
these active axes, due to its length, traverses two or more Functional Clusters, it is
observed that, in all cases, both sides of the street belong to the same Functional
Cluster. In general terms, a similar situation can be found in relation to intersections
Exceptionally, for the city of Alicante, only one economically active intersection was
found to be located at the junction of two clusters. This is the case of the area occupied by
Plaza Luceros (See Figure 4e), a circle recognized as the most socially relevant open
public space of the city (Martí et al., 2017) in which the southwest area belongs to the
FC 39 while the rest of the square is connected to FC 19. Therefore, the space has two
functionally—and perceptibly—diﬀerent sides, each one strongly linked to nearby clus-
ters of economic activities, services, and facilities. This fact suggests that there could be
diﬀerent signiﬁcance between the city scale and the cluster scale. Therefore, including
perceptual parameters of public spaces in the study of Functional Clusters could be an
interesting approach for future research.
Overall, the Functional Cluster tessellation obtained redeﬁnes axes according to their
actual function as an inter-neighborhood conduit of social life, rather than a neighbor-
hood boundary. Sometimes, the concentration of economic activities, services, and facili-
ties becomes a livable intersection, or sometimes it runs along an axis. In the ﬁrst case, the
aggregation of activities at the Functional Cluster’s center becomes the active
JOURNAL OF URBAN TECHNOLOGY 13
neighborhood nucleus, especially where these activities and services are located on
ground ﬂoors, as shown in Figure 4e), where the reﬁned Google Places economic activities
categorized as APA 2100 and APA 2500 can be seen.
The analysis indicated that the spatial distribution of activity types is more homogeneous
in the 42 Functional Clusters than in the Administrative Neighborhoods.
In general terms, the economic activities of the case study fall within two predominant
Level 1 APA categories: category 2000 (general sales or services) and category 6000 (edu-
cation, public administration, health care and other institutions). In all 42 Functional
Clusters both categories were ranked ﬁrst and second respectively, based on their fre-
quency of economic activities.
A greater degree of granularity was obtained in the analysis as two approaches were
adopted to analyze and compare the functional features of both the Administrative
Neighborhoods and the Functional Clusters. On the one hand, the functional diversity
and the quantity of economic activities, services, and facilities of each cluster with
respect to the rest of the clusters were analyzed using the reﬁned dataset; and, on the
other hand, the functional specialization of each cluster was identiﬁed by using APA
second-level categories. In both cases, the total number of economic activities, services,
and facilities per category was adopted as the main criteria.
In terms of the functional diversity, Figure 5 shows the number of Google Places place
types in both Functional Clusters and Administrative Neighborhoods. By comparing the
functional diversity of economic activities, services, and facilities between the Functional
Clusters and the Administrative Neighborhoods, it became apparent that the types of
activities were more evenly distributed in the former tessellation than in that of the
latter (See Figure 5). Clusters FC 40 and FC 5 are the two most relevant Functional Clus-
ters in terms of functional diversity with the presence of 25 diﬀerent place types. While FC
5 is in a central location of the city, FC 40 is located in a residential tourist area that is very
well connected to other neighborhoods, and even municipalities, through main structural
axes. This cluster is characterized by the presence of large residential blocks, isolated high-
rise buildings, and single-family housing units, with mostly private and semi-public open
spaces. The economic activity, services, and facilities in cluster FC 40 run along the inter-
section of two axes, and are concentrated mainly in the ground ﬂoor of buildings. This
intersection happens to fall right at the geometric center of the Functional Cluster.
As for the quantity of economic activity, services, and facilities per category and per
cluster, Figure 5 shows that two out of the three most relevant clusters—FC 3 and FC
15—are located in the city center. The cluster ranked third is cluster FC 40 which, as
mentioned before, is also the most diverse. The ﬁndings suggest that the diversity and
quantity of urban activities reﬂects the functional character of the urban area in which
they are located. The city center has more quantity but a slightly less diverse economic
oﬀer, being mostly stores, restaurants, bars, clothing stores, whereas residential areas
that are further away from the city center have more varied economic activity, services,
and facilities to satisfy the demand of local residents.
As for the clusters’functional specialization, the presence of the APA second-level cat-
egories in each cluster can be observed in Figure 5. The diagram permits a twofold
14 P. MARTÍ ET AL.
reading: ﬁrst, the total number of places per type found in each cluster and, second, the
distribution of each type of economic activity across all clusters and neighborhoods. Both
the Administrative Neighborhoods and the Functional Clusters are compared. In the
former, there is a high concentration of activities (mainly in neighborhoods AN 5 and
AN 4) while in the latter, the spatial distribution of urban activity across all clusters is
Figure 5. Comparison between the diversity and quantity of APA second level categories and number
of Google Places place types within Administrative Neighborhoods (upper) and Functional Clusters
(lower). In parenthesis, the total number of place types (diversity) per neighborhood and cluster,
JOURNAL OF URBAN TECHNOLOGY 15
Furthermore, almost all Functional Clusters have a noticeable presence of two APA
second-level categories: 2100 Retail sales or services and 2500 Food Services. Thus, the
functional specialization becomes more evident with the proposed city subdivision
into Functional Clusters. Speciﬁcally, worth highlighting is cluster FC 22, located at
the heart of the historic city center where not only does the predominant urban activity
correspond to businesses within the APA second-level category 2500 Food Services, but it
is also where most of these types of activities occur when compared to other Functional
Clusters. Moreover, clusters FC 8 and FC 40, two of the ﬁve top ranking clusters in terms
of the amount of economic activity, services, and facilities present, are the most relevant
of all Functional Clusters in the transportation category. This may be a potential indi-
cator of their strong connectivity to other more central neighborhoods, while being func-
Discussion and Conclusion
This research conﬁrms that there is a signiﬁcant degree of disconnect between Alicante’s
traditional Administrative Neighborhood partitions and the city’s functional organiz-
ation. In order to assess the disconnect, a method was proposed to divide the city into
Functional Clusters. These polygons represent more meaningful units in terms of
urban activity and better reﬂect the current functional character of the urban
Urban areas with clustered economic activities belonging to diﬀerent administrative
entities is one of the key issues currently being faced by many cities. For instance,
today’s Sternschanze, an entertainment and nightlife district in the city of Hamburg
was, until 2008, part of three diﬀerent districts (Hamburg-Mitte, Eimsbüttel, and
Altona). Indeed, this area, which has a clustered spatial organization of highly specialized
economic activities, has been recognized as an entity due to the administrative challenges
and lack of consensus between the policies adopted by the three districts. One of the main
challenges of this process has been to delineate the district’s limits (Bürgerschaft der Freie
und Hansestadt Hamburg, 2006), which could have beneﬁted from the method proposed
in this study.
In terms of the source of information selected, the geo-located ﬁne grain listing of
urban activities from Google Places has proven to be rather useful for the recognition
of diﬀerent clusters of urban economic activities, services, and facilities. Moreover,
since this crowd-sourced information is constantly updated, changes over time can be
monitored. These updates are valuable for keeping track of possible changes in urban
activity and are useful for informing the implementation of policies and the design of
urban intervention strategies based on the functional organization of the city.
However, using Google Places datasets is not without its challenges. A thorough pre-pro-
cessing of data is required before to using the dataset since this social network was
designed for an entirely diﬀerent purpose. For instance, duplication of places found in
the raw dataset is frequent due to the ease with which users can freely create and/or
edit place registrations. Another challenge of using Google Places datasets is that data
are classiﬁed into too many similar categories that complicate detailed analysis. Re-cate-
gorizing data into the APA functional categories has helped to overcome this challenge
and has allowed a diﬀerent granularity level in the analysis.
16 P. MARTÍ ET AL.
Indeed, the Functional Clusters derived from the present distribution of economic
activities in Alicante do not correspond to the city’s current administrative spatial deli-
mitation. In this case study, although economic activities and services on both sides of an
axis give its urban environment a unique character, currently they belong to two diﬀerent
Administrative Neighborhoods. However, with the proposed Functional Clusters, econ-
omically active axis segments take the place of main centers of activity. Hence, the
boundaries of existing administrative divisions that may have been understood as
“edges”behave more like “seams”(Jacobs, 1961), and become the heart of the new Func-
tional Clusters, thereby creating a polynuclear structure of neighborhoods with greater
cross-movement of people.
Furthermore, the Functional Clusters obtained presented a more homogeneous distri-
bution and degree of specialization in terms of the quantity and diversity of urban activi-
ties, when compared to the Administrative Neighborhoods (See Figure 5). More
speciﬁcally, in terms of quantity of urban activities, the two ANs with the most and
least registered places have 3,047 and 30 places, respectively, whereas the FCs showed
a proportionally much smaller diﬀerence, with 1,244 and 114 places, respectively. This
also means that, in terms of diversity, the cluster with the least place types—FC 32—
has almost four times more diversity than the neighborhood with the least place types
—AN 35. Still more evidence is the fact that there are only 9 FCs (as opposed to the
17 ANs) with fewer than 20 diﬀerent place types.
This was not predictable since the spatial clustering was performed exclusively by
taking into account the proximity between datapoints and not the types of urban activi-
ties. A possible explanation of this diversity in the case study responds to the functional
characteristics of the case study selected: a predominantly mixed-use Mediterranean city.
In any case, this study concurs with previous research and provides additional evi-
dence on the extent to which a city subdivision based on its current functional distri-
bution leads to a better understanding of the type of urban life that is being created
(Cranshaw et al., 2012; Crooks et al., 2015; Kropf, 1996; Noulas et al., 2013; Zhang
et al., 2013). Therefore, deﬁning Functional Clusters and acknowledging the quantity,
distribution, and diversity of economic activity, services, and facilities within them,
can be of great value in informing planning processes and shaping the direction of
urban regeneration policies (Cranshaw et al., 2012; Noulas et al., 2013; Zhang et al.,
2013). For instance, knowing the degree of functional specialization is useful when
designing land-use strategies that guarantee the availability and diversity of urban activi-
ties in a neighborhood, thus encouraging residents to stay and support the livability of its
The main contribution of this paper to the state of the art is threefold:
▪the use of Google Places data to deﬁne up-to-date functional divisions of the city
▪the pre-processing and re-categorization of Google Places listings of economic activity
into benchmark categories that allow an assessment at diﬀerent degrees of granularity
▪the capacity to identify a degree of specialization and complexity of urban activity (in
terms of quantity and diversity) for each Functional Cluster.
The novelty of the proposed methodological framework lies in two aspects. First, it uses a
social network that speciﬁcally includes information about places and urban activities
JOURNAL OF URBAN TECHNOLOGY 17
“on offer.”Therefore, a detailed listing of the available economic activities, services, and
facilities in a given urban area can be obtained. Previous studies whose focus was to delin-
eate urban functional areas using social media data have used more socially driven social
networks that do not deal with the available urban activities in a place, but rather the
demand for activities, that is, where people add a register on the particular social
network (i.e., visit, tweet or check-in). For instance, the work of Cranshaw et al.
(2012) that uses data retrieved from Foursquare and Twitter; and that of Chen et al.
(2017:49) which collects datasets from Tencent, one of the “largest online social media
platforms in China.”
Second, the grouping of the original Google Places place types into the hierarchical APA
categorization provides a degree of granularity that allows the assessment of the functional
dimension at diﬀerent scales. A more general Level 1 categorization could be used for
regional scale analysis; the Level 2 is, as demonstrated in this study, useful for city scale
assessment; and the Level 3 or its equivalent, Google Places place types, can be considered
for conducting street, and even urban block-scale analysis. The ﬁrst and third cases have
not been fully explored, and as such are potential future directions for research.
To sum up, this paper broadens the debate on how to understand the neighborhood
unit, based on today’s urban functional reality. The study suggests that existing admin-
istrative boundaries are becoming increasingly obsolete and the use of social network
data cannot be neglected in the present debate on neighborhood units.
1. Script understood as a sequence of instructions carried out by another program.
Notes on Contributors
Pablo Martí is a researcher and professor in the Urban Design and Regional Planning Unit, Uni-
versity of Alicante.
Leticia Serrano-Estrada is a researcher and lecturer in the Urban Design and Regional Planning
Unit, University of Alicante.
Almudena Nolasco-Cirugeda is a researcher and lecturer in the Urban Design and Regional Plan-
ning Unit, University of Alicante.
Jesús López Baeza is a researcher in the CityScienceLab, HafenCity University Hamburg.
This work was supported by the Conselleria de Educación, Investigación, Cultura y Deporte, Gen-
eralitat Valenciana (Spain). Project: Valencian Community cities analyzed through Location-
Based Social Networks and Web Services Data. Ref. no. AICO/2017/018.
This research was funded by Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital,
Generalitat Valenciana, Spain -(GV/2021/177).
18 P. MARTÍ ET AL.
Pablo Martí http://orcid.org/0000-0002-6877-784X
Leticia Serrano-Estrada http://orcid.org/0000-0002-7466-1974
Almudena Nolasco-Cirugeda http://orcid.org/0000-0002-3653-1650
Jesús López-Baeza http://orcid.org/0000-0002-4092-1782
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