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International Conference on GIScience Short Paper Proceedings
Title
Deriving Locational Reference through Implicit Information Retrieval
Permalink
https://escholarship.org/uc/item/0tn5s4v9
Journal
International Conference on GIScience Short Paper Proceedings, 1(1)
Authors
Hervey, Thomas
Kuhn, Werner
Publication Date
2016
DOI
10.21433/B3110tn5s4v9
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California
Deriving Locational Reference through Implicit
Information Retrieval
T. Hervey, W. Kuhn
Department of Geography and Center for Spatial Studies,
University of California, Santa Barbara, CA 93106
Email: {thomas.hervey; kuhn} @geog.ucsb.edu
Abstract
The often fragmented process of online spatial data retrieval remains a barrier to domain
scientists interested in spatial analysis. Although there is a wealth of hidden spatial information
online, scientists without prior experience querying web APIs (Application Programming
Interface) or scraping web documents cannot extract this potentially valuable implicit
information across a growing number of sources. In an attempt to broaden the spectrum of
exploitable spatial data sources, this paper proposes an extensible, locational reference deriving
model that shifts extraction and encoding logic from the user to a preprocessing mediation layer.
To implement this, we develop a user interface that: collects data through web APIs and
scrapers, determines locational reference as geometries, and re-encodes the data as explicit
spatial information, usable with spatial analysis tools, such as those in R or ArcGIS.
1. Introduction
GIS advancements have produced a growingly complex general-purpose toolbox rather than
functionality tailored to domain-specific questions. Frequently, domain scientists including
Green (2015: 717) highlight the salient lagging data and tool limitations associated with GIS. As
Kuhn (2012: 2267) notes, it is essential to rethink the fundamentals of spatial information while
promoting clarity that cuts across technical boundaries and broadens spatial literacy for non-
experts. Contributing to the work by Kuhn and Ballatore (2015: 219) and Vahedi et al. (2016) to
design an intuitive GIS language for question-driven spatial studies, we focus on bridging the
gaps between data discovery and spatial analysis tools by broadening the spectrum of exploitable
spatial data sources.
Compared to the vast amount of implicit spatial data (hidden location attributes often in the
form of metadata, auxiliary place names, and geotagged attributes (Heinzle and Sester 2003:
335)), there remains a relatively limited quantity of online explicit spatial data (georeferenced
geometry-based features (Brisaboa et al. 2011: 358)). When available, explicit data are typically
served from a limited number of administrative portals or require intensive energy and time from
a user searching, exporting, encoding and cleaning before being usable (Munson 2013: 65).
These preprocessing requirements limit the feasibility of question-driven spatial analysis (Vahedi
et al. 2016) and force domain scientists to base their studies on data availability.
It is clear that implicit spatial data is an attractive alternative. However, as the numerous
research challenges associated with GIR (geographic information retrieval) suggest (Jones and
Purves 2008: 219), current methods do not provide adequate solutions to navigate, gather or
utilize the mass of heterogeneous implicit spatial data spread across the array of online
repositories. Custom-constructed web API requests and scrapers can help retrieve and process
this unpublished information. Yet, without technical expertise to build new or use existing
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modules, this information remains unobtainable. This begs the question, why so few GIR studies
have focused on aiding implicit spatial data retrieval.
To explore this idea, we propose an extensible locational reference deriving model that shifts
extraction and encoding logic from the user to preprocessing software. In the following sections,
we describe previous work and provide a prototype architecture with applications to test the
plausibility of a model that adopts the core concepts of spatial information (Kuhn 2012: 2267).
2. Previous Work & Limitations
There have been many notable efforts to address the difficulties of GIR, but few have focused on
the extraction and encoding of implicit spatial data. INSPIRE1, NSDI2, and SPIRIT (Jones et al.
2004: 125) for example, are spatial data infrastructure and search engine projects that focus on
standardizing and indexing web documents rather than broadening access to new faster-growing
data repositories (Jones et al. 2004: 125; Brisaboa et al. 2011: 358).
Google’s Fusion Tables3 have simplified data integration and ArcGIS Online has grown
online spatial catalogs through user sharing and publishing. Yet, these tools do not aid in data
extraction from external sources. Enterprise products like Crimson Hexagon4 and Temboo5
respectively provide users with spatial social media analytics and code snippets to ease web API
querying. But these solutions provide neither data source extensibility, nor recognize multiple
location types. Furthermore, studies on linked data, the semantic web, and semantic gazetteers
(Cardoso et al. 2016: 389; Gao et al. 2014) propose a traversable data-centric organization of the
web (Kuhn et al. 2014: 173; Wiegand and García 2007: 355). But until a semantic model broadly
leverages new and existing data, significant user effort will remain necessary for collecting and
preprocessing. The following architecture aims to address these issues and bridge the gap
between discovered data and analysis tools.
3. Architecture
The proposed model’s implementation will be accessible via web interface and GIS plugins. It is
not an independent search engine, but rather works as mediation layer once a desirable data
source has been found and a user supplies the text parameters noted in Table 1. It does not aim to
replace, but rather leverage, existing extraction and encoding methods as well as supply
locational reference deriving logic not present in current systems. Illustrated in Figure 1, the
mediation layer divides preprocessing (extracting and encoding) into four sequential tasks:
extraction, context building, geometry matching, and encoding.
Table 1. User interface and plugin input parameters
Mandatory
Optional
Extraction source (e.g. URL or API endpoint)
Basic aggregation (e.g. state-level)
Source type (e.g. Twitter tweets or hashtags)
Basic conditionals (e.g. exclude attributes)
Output geometry type (e.g. point, polygon)
Error handling (e.g. toponym conflicts)
Output file format (e.g. .shp, .kml, .geoJSON)
1 http://inspire.ec.europa.eu/
2 http://www.fgdc.gov/nsdi/
3 https://support.google.com/fusiontables/answer/2571232?hl=en
4 http://www.crimsonhexagon.com/
5 https://temboo.com/
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The Extraction task makes up the majority of the codebase, which will expand as interest in new
data sources emerge. It leverages parameterized HTTP requests constructed by web APIs (for
database retrieval) and scrapers (for document text retrieval). The result will often be a structured
or semi-structured tree of attributes (e.g. .json, .xml) presented to the user for accuracy feedback.
The second task searches the data for potential location characteristics in the form of raw
geometry or toponyms using a combination of text matching and natural language processing
paired with gazetteer lookups (e.g. GeoIP, GeoNames, Getty Thesaurus of Geographic Names).
When applicable, user feedback will be necessary for place name disambiguation, scoping, and
aggregation.
The third task handles geometry pairing and the fourth, output file formatting. Once location
characteristics have been determined, associated geometries are retrieved from repositories (e.g.
TIGER/Line, Open Street Map) and paired with the data. Additionally, rudimentary spatial
extent and relationship logic is supported (using ESRI’s Java geometry API, GDAL, etc.), such
that a within operation could aggregate geotagged Flickr photo points to a bounding county
polygon. Finally, conversion tools (e.g. GDAL ogr2ogr, Python pyshp), encode the data in an
array of formats (e.g. .shp, .kml)., resulting in a spatially explicit data product usable in analysis.
Figure 1. Examples of leveraged services between implicit data and analysis tools
4. Application
To articulate the model’s potential, we present working and hypothetical examples where
domain scientists encounter data retrieval problems. In our working example, a social scientist
wants to examine the spatial interaction between crime incidents and income across Seattle,
USA. She has statistical experience with R and has imported a block-group median household
income shapefile, but cannot find explicitly spatial crime data. She has, however, found a well
formatted crime statistics table on Seattle’s web portal, and wants to extract the time, crime type,
and charge attributes by city-block. Traditionally, to integrate this information, she would have
to download and import the table, for example, into ArcGIS, remove unwanted attributes,
download a block-group shapefile, aggregate the crime points, and handle geocoding anomalies.
Alternatively, the previous steps are simplified by supplying the model’s web interface with the
Data$Source Preprocessing$Mediation$Layer Output$Format
Extraction GeometryLocation$Referencing
GeoIP2
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table’s API endpoint, specifying which attributes to keep at which aggregation level, and
specifying polygon shapefile as the output. On the back end, the software retrieves the data as
JSON objects, determines street address as its location reference, geocodes and aggregates these
points to block-group geometry served by data.gov, and produces a shapefile using ogr2ogr.
Our hypothetical example adds complexity when a user requires data from social media. A
political scientist wanting to understand spatial voting trends between Californian counties
during a presidential primary, decides to use trending Twitter hashtags as predictors. Without
programming experience, he is limited to Twitter’s simple query building wizards that returns
unwieldy and spatially unreferenced tweet data. Instead, by providing twitter API credentials,
hashtags of interest, and California counties as text parameters to the interface, the model can
filter by hashtag, extract user’s location information (through geotags or home locations) and
aggregate tweet count to the county level, producing a .kml file from OpenStreetMap geometry.
5. Conclusions
We have presented a locational reference deriving model and associated prototype preprocessing
layer that has the potential to promote critical spatial thinking by expanding data source options.
Currently, the model’s integrity is limited by the credibility of its data retrieval sources, and
limited to handling vector data. Therefore, future research will investigate integrating new data
sources, analyzing feedback to promote VGI (volunteered geographic information) supported
gazetteers, as well as integrating data credibility metrics.
References
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Cardoso S, Amanqui F, Serique K, dos Santos J, and Moreira D, 2016, SWI: A Semantic Web Interactive Gazetteer
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Gao S, Li L, Li W, Janowicz K, and Zhang Y, 2014, Constructing Gazetteers from Volunteered Big Geo-Data based
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Green T, 2015, Places of Inequality, Places of Possibility: Mapping “Opportunity in Geography Across Urban
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