The most common and visible use of geographic information retrieval (GIR) today is the search for speci c points of interest that serve an information need for places to visit. However, in some planning and decision making processes, the interest lies not in speci c places, but rather in the makeup of a certain region. This may be for tourist purposes, to nd a new place to live during relocation planning, or to learn more about a city in general. Geospatial Web pages contain rich spatial information content about the geo-located facilities that could characterize the atmosphere, composition, and spatial distribution of geographic regions. But the current means of Web-based GIR interfaces only support the sequential search of geo-located facilities and services individually, and limit the end users on abstracted view, analysis and comparison of urban areas. In this work we propose a system that abstracts from the places and instead generates the makeup of a region based on extracted keywords we nd on the Web pages of the region. We can then use this textual ngerprint to identify and compare other suitable regions which exhibit a similar ngerprint. The developed interface allows the user to get a grid overview, but also to drill in and compare selected regions as well as adapt the list of ranked keywords.
Presented at EuroHCIR2013. Copyright c 2013 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
Geospatial search has become a widely accepted search mode
o ered by many commercial search engines. Their
interfaces can easily be used to answer relatively simple requests
such as \restaurant in Berlin" on a point-based map
interface, which additionally gives extended information about
entities [
To support more complex tasks we propose a new retrieval
method based on entities. For example, sometimes the
distribution of results on a map can already inform certain
views about areas, e.g., a search for \bar" may show a
clustering of results that can be used for \eyeballing" a region
of nightlife even without sophisticated geospatial analysis.
However, as users become more used to local search, more
complex search types and supporting analysis are desired
that enable a combined view onto the underlying data [
The distribution of geo-entities is used to illustrate the
characteristics and dynamics of a geographic region. A
geoentity is a real life entity at a physical location, e.g., a
restaurant, theatre, pub, museum, business, school, etc. To
open these entities up for aggregate and multi-criteria region
characterization, they need a certain depth of information
associated with them. It is obvious that only position
information or the name of a place is insu cient, so categorial
or textual description is needed. For initial studies [
To actually make the connection from a location to Web
pages, we assume that the presence of location references
on a page is a strong indication that the page is associated
with the entity at that location. We use our geoparser to
extract location references and thereby assess the geographical
scopes of a page. The geoparser is trained to the presence of
location references in the form of addresses within the page
content. This is a suitable approach for the urban areas
we are addressing in this work, because we need a
geospatial granularity at the sub-neighborhood level.
Knowledgebased identi cation and veri cation of the addresses is done
against a gazetteer extended with street names, which we
1http://www.openstreetmap.org/
fetched from OSM for the major cities of Germany. To
retrieve actual pages, we crawled the Web with a geospatially
focused crawler [
We have implemented two main interaction modes in the Web interface as shown in Figure 1. A user intends to compare multiple geographic regions of Frankfurt (target region, right in the dual-map view) with respect to a certain relevant region in Berlin (query region, left). The current reference region of interest is speci ed via a visual query. The user can then either select regions by placing markers onto the map, or alternatively use a grid overview (right side of Figure 1). In both cases, the system computes the relevance of the target regions with respect to the characteristics of the query region.
Most GIR interfaces use a conventional textual query as input method to describe user's information need or use the currently selected map viewport. We wanted to give users the ability to arbitrarily de ne their own spatial region of interest. The free de nition of the query region is important, as users may not always want a neighborhood that is easily describable by a textual query. We therefore enabled to query by spatial example, where users can de ne the query region by drawing on map. Figure 1 shows an example of a user selected region of interest via a polygon query (by mouse clicks and drag) in the city of Berlin.
Users can select several location preferences in their target
region that they would like to explore by positioning markers
on the map interface. The system de nes the targets with
a circle around the user-selected locations with the same
diameter as the reference region polygon. The target regions
obtain the ranking with respect to their similarity with the
reference region. Their relevance is shown by the
percentage similarity and the heatmap based relevance
visualization. We used a color scheme of di erent green tones which
di ered in their transparency. Light colors represented low
relevance, dark colors were used to indicate high relevance.
The color scheme selection was aided by ColorBrewer 2.
As an example, Figure 1 shows 4 user-selected locations on
the city map of Frankfurt, the circle regions around these
4 markers have the same diameter as the query region in
Berlin. The target region in the centre of the city is most
relevant with the similarity of 88%, and consequently has
the darkest green tone. If a user has not yet formed any
preference, we o er an aggregate overview of geo-entities.
We partition the map area using a grid raster [
Interaction models should provide end users the opportunity to explore the characteristics of selected regions, and adapt it further to their requirements. We initially show the most relevant keywords of the respective region using a word cloud. The word cloud provides more detailed information on keyword distribution when the mouse hovers over it. The font size and order of the keywords signify their relevance. Figure 2 shows the comparison of the query region with the most relevant target region via both their keyword distributions. In this case, the distributions of both regions are very similar, leading to the high relevance score for the target region.
Since the keyword characteristics of a query region is derived from the georeferenced Web pages, there are situations where a user might not be satis ed with the spatial descrip2http://colorbrewer2.org tion and wants to in uence the keywords. In the example of Figure 3, a user decides that pubs are more important than restaurant, fast food is not an aspect of his lifestyle and should be replaced by education facilities near his new home. In such scenarios users need to interact and adapt the generated keyword distributions of query regions. We make the word cloud interactive and editable. Users can drag keywords to alter their position and thus their signi cance. They can also edit, delete or replace keywords in the word cloud to change the criteria. After modifying the keyword distribution, users can revisualize the target regions to update their ranking. Figure 3 shows this user interaction with the word cloud, including the revisualization of the updated ranking of target regions, which are visibly di erent from the previous ranking of Figure 2.
We adapt common IR methods for ranking and similarity
measures. In relevance-based language models, the
similarity of a document to a query is the probability that a given
document would generate the query [
For a geospatial document d, we estimate P (wjd) , which is a unigram language model , with the maximum likelihood estimator, simply given by relative counts: P (wjd) = tf(w;d) , jdj here tf (w; d) is the frequency of word w in the document d and jdj is the length of the document d. A geographic region contains several geospatial documents insides its footprint area. We de ne a geographic region based on a document cluster D which contains document fd1; d2::::dkg, and the distribution of a particular word w in the geographic region would be estimated with its combine probability in the collection P (wjD) = k1 Pk
i=1 P (wjdi). The word cloud represents the most prominent keywords of the region with respect to their ranked probability distribution P (wjD). The comparison of regions is done with respect to their probability distribution using KL-divergence. A target region x will be compared to the query region as following
Relevance(Regionx) =
X P (wjDq)log w
The computation of this formula involves a sum over all the words that have a non-zero probability according to P (wjDq). Each region Regionx gets a relevance score according to its distribution comparison to the query region Regionq. All target regions (user selected regions or grid based divisions) are ranked with respect to their relevance score for visualization.
The eld of geographic information retrieval examines
documents' geospatial features at a regional scale and also at
smaller granularities and usually supports keyword@location
queries [
Our system interface di ers in the granularity of information need and representation, i.e., we focus on the ranking of regions, but base it on high-granularity geo-entities that have a very exact location, which ensures that the spatial query does not produce overlap to neighboring regions and makes the multi-criteria analysis more exact to be executed at arbitrary region sizes.
Most current local search interfaces do not o er adequate support for the exploration and comparison of geographic areas and regions. End users need visual and interactive assistance from GIR systems for an abstracted overview and analysis of geospatial data. We proposed interactive interfaces for the characterization and assessment of relevant geographic regions that enable end-users to query, analyze and interact with the rich geospatial data available on the Web in user-selected geographic regions. The relevance of regions is based on the similarity of keyword distributions. The observation of results shows satisfactory performance by uncovering realistic and meaningful keywords de ning the regions. We observed that the characterization and comparison of geographic regions show good results with respect to geo-located facilities and infrastructure of German cities, e.g., clearly distinct characteristics for university, industrial, or party districts. In the future we plan a more formal qualitative and quantitative evaluation of these interfaces, to examine the acceptance of these visualizations with regard to user-centered aspects such as exploration ability, information overload, and cognitive demand. We would also like to explore more advanced interaction methods to enhance the usability of the proposed visualizations.
Additionally, we envision more powerful region similarity measures such as landscape and topological similarity, similarity via social media, and an integration of additional data sources.
The authors are grateful to the DFG SPP 1335 `Scalable Visual Analytics' priority program which funds the project UrbanExplorer. The 2nd author acknowledges funding from the ERCIM \Alain Bensoussan" Fellowship Programme.