Dillingham, I.

Dykes, J.

Wood, J.

English

Dillingham, Iain

Wood, J

Dykes, J

University of St. Andrews - Pure

Types, Granularities and Combinations of Geographic Objects in the Haiti Crisis Map

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City Research Online

Dillingham, I., Wood, J. & Dykes, J. (2013). Types, Granularities and Combinations of Geographic Objects in the Haiti Crisis Map. Paper presented at the GIS Research UK 21st Annual Conference (GISRUK 2013), 3 - 5 Apr 2013, University of Liverpool, Liverpool, UK.City Research OnlineOriginal citation: Dillingham, I., Wood, J. & Dykes, J. (2013). Types, Granularities and Combinations of Geographic Objects in the Haiti Crisis Map. Paper presented at the GIS Research UK 21st Annual Conference (GISRUK 2013), 3 - 5 Apr 2013, University of Liverpool, Liverpool, UK.Permanent City Research Online URL: http://openaccess.city.ac.uk/2123/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders.  All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.Types, Granularities and Combinations of Geographic Objects in theHaiti Crisis MapIain Dillingham,1 Jo Wood2 and Jason Dykes31,2,3giCentre, School of Informatics, City University London, EC1V 0HB1Tel. +44 (0)20 7040 0295 Fax +44 (0)20 7040 8584Email: 1iain.dillingham.1@city.ac.uk, 2j.d.wood@city.ac.uk, 3j.dykes@city.ac.ukKEYWORDS: Uncertainty, Lineage, Ushahidi, Crisis information, Crowdsourcing1. IntroductionThe humanitarian community is reluctant to use social media when responding to crisis events, as thecosts associated with inaccurate information are prohibitive (Coyle & Meier 2009). One solution isto crowdsource the verification process, although this clearly introduces further uncertainty into theinformation. As part of an ongoing programme of research into the geographic uncertainty associatedwith crowdsourced crisis information, we consider location descriptions from the Haiti Crisis Map, adataset gathered in the wake of the 2010 earthquake in Haiti (Ushahidi 2009). Building on a previousstudy (Dillingham et al. 2012a) and using a named-entity recognition (NER) approach, we address threeresearch questions that relate the types, granularities and combinations of geographic objects to theirlineage and, as such, their geographic uncertainty.2. Literature reviewSeveral studies have explored location descriptions in datasets like the Haiti Crisis Map. For example,Wieczorek et al. (2004) and Guo et al. (2008) explore a collection of natural history records, and Dohertyet al. (2011) a collection of historical search and rescue incident reports, with the aim of building geo-referencing methods that better account for geographic uncertainty. To better understand these methods,Guo et al. (2008) argue that a meaningful location description will contain a named place. This namedplace either will be the location, or will act (with a spatial relationship) as a reference to the location.In a previous study (Dillingham et al. 2012a), we categorised location descriptions from the Haiti CrisisMap and found that locations were described in terms of named places more frequently than in termsof spatial relationships from named places; for example, as features, rather than as headings (e.g. ‘southof’), offsets (e.g. ‘10km from’) or ‘between’ or ‘near to’ spatial relationships from features. However,we were unable to explore the relationship between location descriptions and geographic uncertaintybecause we knew very little about the granularity of the named places, where granularity encompassesscale and definition (vagueness) (Jones et al. 2008).To determine the granularity of the named places, it is necessary to annotate, either manually or auto-matically, location descriptions. Gelernter & Mushegian (2011) argue that automatic annotation has theadvantage of scaleability. Consequently, we used an NER approach, which we discuss in Section 4.3. Research questions (RQs)1. What types of geographic objects are contained in location descriptions?2. What granularities of geographic objects are contained in location descriptions?3. How are types and granularities of geographic objects combined in location descriptions?4. MethodsWe wrote a small Java application to transform the Haiti subset of the GeoNames gazetteer (GeoNames2012b) into the format used by the GATE/ANNIE gazetteer (Cunningham et al. 2002). This applicationretains unique, lower-case values of the name, asciiname and alternatenames attributes;performs a small number of character substitutions; and groups these values by their feature codes(GeoNames 2012a). We also manually created a second GATE/ANNIE gazetteer that contains commonspatial relationships (e.g. ‘south’, ‘between’ and ‘near to’). The entries in this gazetteer are based onour familiarity with location descriptions from the Haiti Crisis Map. In total, the two gazetteers contain16,332 entries grouped by 92 feature codes. However, these entries are unevenly distributed amongstfeature codes: for example, ‘populated place’ accounts for 62.20% of entries (10,159), whilst the nextmost frequent feature code, ‘stream’, accounts for only 7.84% (1,281).We built a GATE application to annotate location descriptions using the two gazetteers. Although GATEcan export to XML, for simplicity we preserved the original format of one location description on eachline, with annotations encoded using an XML-like syntax (Figure 1). This syntax allows annotationsto be nested; in the example, ‘baie de henne’ is annotated as ‘bay’, whilst ‘henne’ is also annotated as‘populated place’. Although this example is meaningful, others are not: ‘port margot’ and ‘margot’ areannotated as ‘populated place’ because both entries appear in the gazetteer. Consequently, we ignorednested annotations when calculating frequencies.<BAY>baie de <PPL>henne</PPL></BAY><PPL>port <PPL>margot</PPL></PPL>Figure 1: Two annotated location descriptions.5. Preliminary resultsThe mean number of annotations for each location description is 1.639, with a range of 0 to 7. Figure 2shows the frequency of feature codes. Figure 3 shows the frequency of adjacent feature codes. Table 1is an index to feature codes.6. DiscussionAlthough the most frequent feature codes refer to settlements (e.g. PPL, PPLC) (RQ1), settlements arelikely to be over-represented and streets under-represented because many streets are numbered by theirsettlements but generally only settlements are entries in the gazetteer (e.g. Delmas, which is a settlementwithin Port-au-Prince, contains Delmas 50, which is a street). Similarly, airports are likely to be over-represented and capitals under-represented because many location descriptions use the abbreviation‘pap’ to refer to ‘Port-au-Prince’ rather than to ‘PAP’, the IATA code for Port-au-Prince InternationalAirport. Turning to frequencies of adjacent feature codes, capitals frequently follow populated places(PPL–PPLC, 334) (RQ3). Given that all 32 entities for capitals in the gazetteer refer to Port-au-Prince,0100020003000CLFPLN RF SCHHLL ST STMIAIRFBAYCOVESTM MT HTLCAPE FT ISL PPLAHDNGADM1LCTYADM3AIRPRLTNSHPPPLXPCLIPPLC PPLFeature codeFrequencyFigure 2: Frequency of feature codes.these populated places are likely to be districts of, or streets in (see above), the Haitian capital. Furtherwork is required to use JAPE, a GATE-based pattern language, to identify common street-like, airport-like and district-like patterns and improve the NER approach.Coarser-granularity administrative areas (e.g. PPLC, PCLI) are more frequent than finer-granularityadministrative areas (e.g. PPLA, ADM1) (RQ2). Indeed, political entities (PCLI, 753), of which allrefer to Haiti, and capitals (PPLC, 835), of which all refer to Port-au-Prince, are the most frequentadministrative areas (RQ1). Political entities tend to follow populated places (PPL–PCLI, 319) andcapitals (PPLC–PCLI, 197) (RQ3). Given that the spatial and thematic foci of the Haiti Crisis Mapare clear, these pairings cannot reduce place-name ambiguity. Instead, their existence suggests thatredundancy characterises many location descriptions.GeoNames defines a locality as “a minor area or place of unspecified or mixed character and indefiniteboundaries” (GeoNames 2012a). The low frequency of localities (LCTY, 64) suggests that locationdescriptions tend not to contain vague places, or places that have vernacular names and vague spatialextents (Jones et al. 2008) (RC1). Finer-granularity named places (e.g. HTL) are infrequent (RQ2), asare spatial relationships (RLTNSHP, 178; HDNG, 46) (RC1), a result which supports our previous study(Dillingham et al. 2012a).In summary, although there is scope to improve the NER approach, location descriptions tend to con-tain coarser-granularity administrative areas, especially settlements followed by references to Haiti. Thissuggests that redundancy characterises many location descriptions. Furthermore, location descriptionstend not to contain spatial relationships, which is surprising, given that so many buildings were de-stroyed by the earthquake (Inter-American Development Bank 2010). Consequently, we suggest thatmany location descriptions were edited to conform to a model of space that was itself highly uncertain.Although the original, unedited location descriptions are not available in the Haiti Crisis Map—and maynever be available in future crisis maps, given the ephemeral nature of these ‘mashups’—exploring pat-terns of types, granularities and combinations of geographic objects in location descriptions may allowus to infer their lineage, which is one aspect of their geographic uncertainty (MacEachren et al. 2005).ADM1ADM3AIRFAIRPCAPEFTHDNGHLLHTLISLLCTYMTPCLIPPLPPLAPPLCPPLXRLTNSHPSCHSTSTMADM1ADM3AIRFAIRPCAPE CLFCOVE FT HDNG HLLHTLISL LCTY MT PCLIPPLPPLAPPLCPPLX RFRLTNSHPST STMSTMIFirst feature codeSecond feature codeFigure 3: Frequency of adjacent feature codes.7. ConclusionWe used an NER approach to explore the types, granularities and combinations of geographic objectscontained in location descriptions from the Haiti Crisis Map. We argue that doing so may allow us toinfer the lineage of location descriptions and highlight several characteristics that suggest that many lo-cation descriptions from the Haiti Crisis Map were edited. Although further work is required to improvethe NER approach, we see the most interesting prospects for future research to be in the developmentof both analytical methods and tools that allow for the analysis of the characteristics of location de-scriptions over space and time (that is, to explore the effects of the uncertain model of space), and theirapplication to location descriptions from alternative crisis maps. Consequently, we are developing visualanalytics software (Dillingham et al. 2012b) that we hope to present in the near future.8. AcknowledgementsWe thank the Haiti Crisis Map for making their dataset publicly available. This work is funded by adoctoral studentship from City University London.9. ReferencesCoyle, D. & Meier, P. (2009), New technologies in emergencies and conflicts: The role of informationand social networks, Technical report, UN Foundation–Vodafone Foundation Partnership, Washing-ton DC, USA and London, UK. [Accessed 2 June 2011].URL: http://www.unfoundation.org/press-center/publications/new-technologies-emergencies-conflicts.htmlFeature code DescriptionADM1 first-order administrative divisionADM3 third-order administrative divisionAIRF airfieldAIRP airportBAY bayCAPE capeCLF cliff(s)COVE cove(s)FT fortHDNG headingHLL hillHTL hotelISL islandLCTY localityMT mountainPCLI independent political entityPLN plain(s)PPL populated placePPLA seat of a first-order administrative divisionPPLC capital of a political entityPPLX section of populated placeRF reef(s)RLTNSHP spatial relationshipSCH schoolST streetSTM streamSTMI intermittent streamTable 1: Index to feature codes (GeoNames 2012a).Cunningham, H., Maynard, D., Bontcheva, K. & Tablan, V. (2002), GATE: A framework and graphicaldevelopment environment for robust NLP tools and applications, in ‘Proceedings of the 40th Anniver-sary Meeting of the Association for Computational Linguistics (ACL’02)’, pp. 168–175. The 40thAnniversary Meeting of the Association for Computational Linguistics (ACL’02), Philadelphia, PA,USA, 7–12 July 2002.Dillingham, I., Dykes, J. & Wood, J. (2012a), Characterising locality descriptions in crowdsourcedcrisis information, in ‘Proceedings of the GISRUK 20th Annual Conference’. Lancaster University,Lancaster, UK, 11–13 April 2012.Dillingham, I., Dykes, J. & Wood, J. (2012b), ‘Exploring patterns of uncertainty in crowdsourced crisisinformation’, Poster presented at the EuroVis Workshop on Visual Analytics (EuroVA). Vienna,Austria, 4–5 June 2012.Doherty, P., Guo, Q., Liu, Y., Wieczorek, J. & Doke, J. (2011), ‘Georeferencing incidents from local-ity descriptions and its applications: a case study from Yosemite National Park search and rescue’,Transactions in GIS 15(6), 775–793.Gelernter, J. & Mushegian, N. (2011), ‘Geo-parsing messages from microtext’, Transactions in GIS15(6), 753–773.GeoNames (2012a), ‘GeoNames feature codes’. [Accessed 3rd October 2012].URL: http://www.geonames.org/export/codes.htmlGeoNames (2012b), ‘GeoNames Haiti’. [Accessed 3rd October 2012].URL: http://download.geonames.org/export/dump/HT.zipGuo, Q., Liu, Y. & Wieczorek, J. (2008), ‘Georeferencing locality descriptions and computing associ-ated uncertainty using a probabilistic approach’, International Journal of Geographical InformationScience 22(10), 1067–1090.Inter-American Development Bank (2010), ‘Haiti reconstruction cost may near $14 billion, IDB studyshows’. [Accessed 13 November 2012].URL: http://www.iadb.org/en/news/webstories/2010-02-16/haiti-earthquake-reconstruction-could-hit-14-billion-idb,6528.htmlJones, C. B., Purves, R. S., Clough, P. D. & Joho, H. (2008), ‘Modelling vague places with knowledgefrom the web’, International Journal of Geographical Information Science 22(10), 1045–1065.MacEachren, A. M., Robinson, A., Hopper, S., Gardner, S., Murray, R., Gahegan, M. & Hetzler, E.(2005), ‘Visualizing geospatial information uncertainty: What we know and what we need to know’,Cartography and Geographic Information Science 32(3), 139–160.Ushahidi (2009), ‘Haiti Crisis Map’. [Accessed 19 October 2011].URL: http://haiti.ushahidi.com/Wieczorek, J., Guo, Q. & Hijmans, R. (2004), ‘The point-radius method for georeferencing locality de-scriptions and calculating associated uncertainty’, International Journal of Geographical InformationScience 18(8), 745–767.10. BiographyIain Dillingham is a PhD candidate at the giCentre, City University London, researching the geographicuncertainty associated with incident reports gathered in the wake of crisis events.Dr Jo Wood is a Professor of Visual Analytics at the giCentre, City University London, with researchinterests in geovisualization and visual analytics of data with spatial and temporal components.Dr Jason Dykes is a Professor of Visualization at the giCentre, City University London, undertaking ap-plied and theoretical research in, around and between information visualization, interactive analyticalcartography and human-centred design.

Types, Granularities and Combinations of Geographic Objects in the Haiti Crisis Map

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