Fine-resolution population mapping using OpenStreetMap points-of-interest

Bakillah, Mohamed; Liang, Steve H. L.; Mobasheri, Amin; Arsanjani, Jamal Jokar; Zipf, Alexander

doi:10.1080/13658816.2014.909045

Cited by 197 publications

(125 citation statements)

References 52 publications

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“…In this research, Twitter data act as ancillary data similar to most recent approaches in areal interpolation that involve the use of point data (0-dimension) (Bakillah, Liang, Mobasheri, Jokar Arsanjani, & Zipf, 2014; Lin & Cromley, 2015; Zhang & Qiu, 2011). Due to their 0-dimension (i.e.…”

Section: Research Gap and The Current Studymentioning

confidence: 99%

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Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

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Section: Research Gap and The Current Studymentioning

confidence: 99%

“…It is worth pointing out that Bakillah et al (2014) proposed a building level disaggregation approach in an application with points-of-interest (POIs) from OpenStreetMap (OSM) that are associated with a higher density of population. Their approach merged the “point-based intelligent approach” with other existing methods.…”

Section: Modeling Population At Riskmentioning

confidence: 99%

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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“…This could be done for instance by leveraging initial examples of using CGI from collaborative maps to support activities in disaster risk management, such as in the identification of critical infrastructures to support emergency planning Schelhorn et al 2014), for instance for performing evacuation simulations (Bakillah et al 2012, Goetz & Zipf 2012 and estimating the vulnerability of urban areas based on synthetic information about the potentially affected population (Bakillah et al 2014).…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, Fuchs et al (2013) showed that event detection based on peaks of Twitter activity did not work for the 2013 floods in Germany and presented an analysis of spatiotemporal clusters. Bakillah et al (2014) applied graph clustering to support the detection of geolocated communities in Twitter after the typhoon Haiyan in the Philippines. Furthermore, a number of studies are concerned about developing tools for visualising social media data in order to enable make-sensing and location-based knowledge discovery (MacEachren et al 2011;Terpstra & de Vries 2012;Croitoru et al 2013;Spinsanti & Ostermann 2013).…”

Section: Social Mediamentioning

confidence: 99%

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

Albuquerque¹,

Eckle²,

Herfort³

et al. 2016

European Handbook of Crowdsourced Geographic Information

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In the past few years, crowdsourced geographic information (also called volunteered geographic information) has emerged as a promising information source for improving urban resilience by managing risks and coping with the consequences of disasters triggered by natural hazards. This chapter presents a typology of sources and usages of crowdsourced geographic information for disaster management, as well as summarises recent research results and present lessons learned for future research and practice in this field.

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“…Paudyal et al explored VGI in catchment management [22]. Bakillah et al conducted population mapping using OSM points of interest [23]. Clark [24] used crowdsourcing, VGI, and Citizens Acting as Sensors in Australia's Environment Sustainability.…”

Section: Related Workmentioning

confidence: 99%

Dynamically Integrating OSM Data into a Borderland Database

Zhou

Zeng

et al. 2015

IJGI

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Spatial data are fundamental for borderland analyses of geography, natural resources, demography, politics, economy, and culture. As the spatial data used in borderland research usually cover the borderland regions of several neighboring countries, it is difficult for anyone research institution of government to collect them. Volunteered Geographic Information (VGI) is a highly successful method for acquiring timely and detailed global spatial data at a very low cost. Therefore, VGI is a reasonable source of borderland spatial data. OpenStreetMap (OSM) is known as the most successful VGI resource. However, OSM's data model is far different from the traditional geographic information model. Thus, the OSM data must be converted in the scientist's customized data model. Because the real world changes rapidly, the converted data must be updated incrementally. Therefore, this paper presents a method used to dynamically integrate OSM data into the borderland database. In this method, a basic transformation rule base is formed by comparing the OSM Map Feature description document and the destination model definitions. Using the basic rules, the main features can be automatically converted to the destination model. A human-computer interaction model transformation and a rule/automatic-remember mechanism are developed to interactively transfer the unusual features that cannot be transferred by the basic rules to the target model and to remember the reusable rules automatically. To keep the borderland database current, the global OsmChange daily diff file is used to extract the change-only information for the research region. To extract the changed objects in the region under study, the relationship between the changed object and the research region is analyzed considering the evolution of the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1708 involved objects. In addition, five rules are determined to select the objects and integrate the changed objects with multi-versions over time. The objects' change-type evolution is analyzed, and seven rules are used to determine the change-type of the changed objects. Based on these rules and algorithms, we programmed an automatic (or semi-automatic) integrating and updating prototype system for the borderland database. The developed system was intensively tested using OSM data for Vietnam and Pakistan as the experimental data.

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Fine-resolution population mapping using OpenStreetMap points-of-interest

Cited by 197 publications

References 52 publications

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned

Dynamically Integrating OSM Data into a Borderland Database

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