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Dr. Amin Mobasheri
GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany

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Research Keywords & Expertise

0 Geospatial data quality
0 Applied remote sensing
0 Open geo-data
0 Open geo-services & software
0 Crowd sensing and volunteered geographic information (VGI)

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Editorial
Published: 20 October 2020 in Transactions in GIS
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Over the past decade, open source software has become widely accepted across governments, industries and academia. The geospatial domain is no exception and this trend is also reflected in geospatial research and practice. Nowadays, governments and stakeholders from the business sector both participate and promote open geospatial science including open geospatial data and open source geospatial software. As a result, open source geospatial science and software (i.e., open source GIS) is a growing area of research with numerous applications and great potential. The consistent prevalence of open source GIS studies motivated this thematic collection. The contributions are divided into two main categories. In the first, novel open source geospatial software and standards are presented, each of which has been implemented for and applied to a certain use case, and at the same time may be applied to other use cases due to the reproducibility of open source software. The second category presents and discusses the applicability and usability of open source GIS solutions for various interdisciplinary domains, mostly related to urban studies.

ACS Style

Amin Mobasheri; Helena Mitasova; Markus Neteler; Alexander Singleton; Hugo Ledoux; Maria Antonia Brovelli. Highlighting recent trends in open source geospatial science and software. Transactions in GIS 2020, 24, 1141 -1146.

AMA Style

Amin Mobasheri, Helena Mitasova, Markus Neteler, Alexander Singleton, Hugo Ledoux, Maria Antonia Brovelli. Highlighting recent trends in open source geospatial science and software. Transactions in GIS. 2020; 24 (5):1141-1146.

Chicago/Turabian Style

Amin Mobasheri; Helena Mitasova; Markus Neteler; Alexander Singleton; Hugo Ledoux; Maria Antonia Brovelli. 2020. "Highlighting recent trends in open source geospatial science and software." Transactions in GIS 24, no. 5: 1141-1146.

Chapter
Published: 08 September 2020 in TRANSBALTICA XI: Transportation Science and Technology
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Nowadays, governments from around the world and stakeholders from the business sector both participate to and promote open geospatial science. Governments increasingly provide free access to various types of geospatial data as they realize its potential to foster economic, social, urban and environmental opportunities. Concrete projects based on open geospatial data are now having significant and measurable impact on communities, economy, environment, health, and transportation, only to name a few areas. Hereby, we focus on the benefits that open geospatial science in general, and open geospatial data in particular bring to urban studies with particular focus on transportation and smart city analytics projects. This chapter introduces up-to-date practical studies that address some concrete challenges within the proposed domain, and ends with some remarks on the topic.

ACS Style

Amin Mobasheri. An Introduction to Open Source Geospatial Science for Urban Studies. TRANSBALTICA XI: Transportation Science and Technology 2020, 1 -8.

AMA Style

Amin Mobasheri. An Introduction to Open Source Geospatial Science for Urban Studies. TRANSBALTICA XI: Transportation Science and Technology. 2020; ():1-8.

Chicago/Turabian Style

Amin Mobasheri. 2020. "An Introduction to Open Source Geospatial Science for Urban Studies." TRANSBALTICA XI: Transportation Science and Technology , no. : 1-8.

Articles
Published: 03 July 2018 in Geo-spatial Information Science
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Nowadays, several research projects show interest in employing volunteered geographic information (VGI) to improve their systems through using up-to-date and detailed data. The European project CAP4Access is one of the successful examples of such international-wide research projects that aims to improve the accessibility of people with restricted mobility using crowdsourced data. In this project, OpenStreetMap (OSM) is used to extend OpenRouteService, a well-known routing platform. However, a basic challenge that this project tackled was the incompleteness of OSM data with regards to certain information that is required for wheelchair accessibility (e.g. sidewalk information, kerb data, etc.). In this article, we present the results of initial assessment of sidewalk data in OSM at the beginning of the project as well as our approach in awareness raising and using tools for tagging accessibility data into OSM database for enriching the sidewalk data completeness. Several experiments have been carried out in different European cities, and discussion on the results of the experiments as well as the lessons learned are provided. The lessons learned provide recommendations that help in organizing better mapping party events in the future. We conclude by reporting on how and to what extent the OSM sidewalk data completeness in these study areas have benefited from the mapping parties by the end of the project.

ACS Style

Amin Mobasheri; Alexander Zipf; Louise Francis. OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project. Geo-spatial Information Science 2018, 21, 234 -246.

AMA Style

Amin Mobasheri, Alexander Zipf, Louise Francis. OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project. Geo-spatial Information Science. 2018; 21 (3):234-246.

Chicago/Turabian Style

Amin Mobasheri; Alexander Zipf; Louise Francis. 2018. "OpenStreetMap data quality enrichment through awareness raising and collective action tools—experiences from a European project." Geo-spatial Information Science 21, no. 3: 234-246.

Software
Published: 28 May 2018 in Open Geospatial Data, Software and Standards
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OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too.

ACS Style

Franz-Benjamin Mocnik; Amin Mobasheri; Alexander Zipf. Open source data mining infrastructure for exploring and analysing OpenStreetMap. Open Geospatial Data, Software and Standards 2018, 3, 1 -15.

AMA Style

Franz-Benjamin Mocnik, Amin Mobasheri, Alexander Zipf. Open source data mining infrastructure for exploring and analysing OpenStreetMap. Open Geospatial Data, Software and Standards. 2018; 3 (1):1-15.

Chicago/Turabian Style

Franz-Benjamin Mocnik; Amin Mobasheri; Alexander Zipf. 2018. "Open source data mining infrastructure for exploring and analysing OpenStreetMap." Open Geospatial Data, Software and Standards 3, no. 1: 1-15.

Journal article
Published: 08 February 2018 in Sensors
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Tailored routing and navigation services utilized by wheelchair users require certain information about sidewalk geometries and their attributes to execute efficiently. Except some minor regions/cities, such detailed information is not present in current versions of crowdsourced mapping databases including OpenStreetMap. CAP4Access European project aimed to use (and enrich) OpenStreetMap for making it fit to the purpose of wheelchair routing. In this respect, this study presents a modified methodology based on data mining techniques for constructing sidewalk geometries using multiple GPS traces collected by wheelchair users during an urban travel experiment. The derived sidewalk geometries can be used to enrich OpenStreetMap to support wheelchair routing. The proposed method was applied to a case study in Heidelberg, Germany. The constructed sidewalk geometries were compared to an official reference dataset (“ground truth dataset”). The case study shows that the constructed sidewalk network overlays with 96% of the official reference dataset. Furthermore, in terms of positional accuracy, a low Root Mean Square Error (RMSE) value (0.93 m) is achieved. The article presents our discussion on the results as well as the conclusion and future research directions.

ACS Style

Amin Mobasheri; HaoSheng Huang; Lívia Castro Degrossi; Alexander Zipf. Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques. Sensors 2018, 18, 509 .

AMA Style

Amin Mobasheri, HaoSheng Huang, Lívia Castro Degrossi, Alexander Zipf. Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques. Sensors. 2018; 18 (2):509.

Chicago/Turabian Style

Amin Mobasheri; HaoSheng Huang; Lívia Castro Degrossi; Alexander Zipf. 2018. "Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques." Sensors 18, no. 2: 509.

Software
Published: 29 November 2017 in Open Geospatial Data, Software and Standards
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Crowdsourcing (geo-) information and participatory GIS are among the current hot topics in research and industry. Various projects are implementing participatory sensing concepts within their workflow in order to benefit from the power of volunteers, and improve their product quality and efficiency. Wheelmap is a crowdsourcing platform where volunteers contribute information about wheelchair-accessible places. This article presents information about the technical framework of Wheelmap, and information on how it could be used in projects dealing with accessibility and/or multimodal transportation.

ACS Style

Amin Mobasheri; Jonas Deister; Holger Dieterich. Wheelmap: the wheelchair accessibility crowdsourcing platform. Open Geospatial Data, Software and Standards 2017, 2, 27 .

AMA Style

Amin Mobasheri, Jonas Deister, Holger Dieterich. Wheelmap: the wheelchair accessibility crowdsourcing platform. Open Geospatial Data, Software and Standards. 2017; 2 (1):27.

Chicago/Turabian Style

Amin Mobasheri; Jonas Deister; Holger Dieterich. 2017. "Wheelmap: the wheelchair accessibility crowdsourcing platform." Open Geospatial Data, Software and Standards 2, no. 1: 27.

Journal article
Published: 31 October 2017 in Sensors
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Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets.

ACS Style

Amin Mobasheri. A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation. Sensors 2017, 17, 2498 .

AMA Style

Amin Mobasheri. A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation. Sensors. 2017; 17 (11):2498.

Chicago/Turabian Style

Amin Mobasheri. 2017. "A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation." Sensors 17, no. 11: 2498.

Journal article
Published: 19 June 2017 in Sustainability
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As it is widely accepted, cycling tends to produce health benefits and reduce air pollution. Policymakers encourage people to use bikes by improving cycling facilities as well as developing bicycle-sharing systems (BSS). It is increasingly interesting to investigate how environmental factors influence the cycling behavior of users of bicycle-sharing systems, as users of bicycle-sharing systems tend to be different from regular cyclists. Although earlier studies have examined effects of safety and convenience on the cycling behavior of regular riders, they rarely explored effects of safety and convenience on the cycling behavior of BSS riders. Therefore, in this study, we aimed to investigate how road safety, convenience, and public safety affect the cycling behavior of BSS riders by controlling for other environmental factors. Specifically, in this study, we investigated the impacts of environmental characteristics, including population density, employment density, land use mix, accessibility to point-of-interests (schools, shops, parks and gyms), road infrastructure, public transit accessibility, road safety, convenience, and public safety on the usage of BSS. Additionally, for a more accurate measure of public transit accessibility, road safety, convenience, and public safety, we used spatiotemporally varying measurements instead of spatially varying measurements, which have been widely used in earlier studies. We conducted an empirical investigation in Chicago with cycling data from a BSS called Divvy. In this study, we particularly attempted to answer the following questions: (1) how traffic accidents and congestion influence the usage of BSS; (2) how violent crime influences the usage of BSS; and (3) how public transit accessibility influences the usage of BSS. Moreover, we tried to offer implications for policies aiming to increase the usage of BSS or for the site selection of new docking stations. Empirical results demonstrate that density of bicycle lanes, public transit accessibility, and public safety influence the usage of BSS, which provides answers for our research questions. Empirical results also suggest policy implications that improving bicycle facilities and reducing the rate of violent crime rates tend to increase the usage of BSS. Moreover, some environmental factors could be considered in selecting a site for a new docking station.

ACS Style

Yeran Sun; Amin Mobasheri; Xuke Hu; Weikai Wang. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. Sustainability 2017, 9, 1060 .

AMA Style

Yeran Sun, Amin Mobasheri, Xuke Hu, Weikai Wang. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users. Sustainability. 2017; 9 (6):1060.

Chicago/Turabian Style

Yeran Sun; Amin Mobasheri; Xuke Hu; Weikai Wang. 2017. "Investigating Impacts of Environmental Factors on the Cycling Behavior of Bicycle-Sharing Users." Sustainability 9, no. 6: 1060.

Journal article
Published: 09 June 2017 in Sustainability
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Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most famous crowdsourced mapping platforms, we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. We assess the completeness of OSM data regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods, we present the results of fitness for use of OSM data for routing services of disabled persons. Based on empirical results, it is concluded that OSM data of relatively large spatial extents inside all studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed. Finally, the article ends with an outlook for future research study in this area.

ACS Style

Amin Mobasheri; Yeran Sun; Lukas Loos; Ahmed Loai Ali. Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility. Sustainability 2017, 9, 997 .

AMA Style

Amin Mobasheri, Yeran Sun, Lukas Loos, Ahmed Loai Ali. Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility. Sustainability. 2017; 9 (6):997.

Chicago/Turabian Style

Amin Mobasheri; Yeran Sun; Lukas Loos; Ahmed Loai Ali. 2017. "Are Crowdsourced Datasets Suitable for Specialized Routing Services? Case Study of OpenStreetMap for Routing of People with Limited Mobility." Sustainability 9, no. 6: 997.

Journal article
Published: 08 March 2017 in International Journal of Environmental Research and Public Health
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With the development of information and communications technology, user-generated content and crowdsourced data are playing a large role in studies of transport and public health. Recently, Strava, a popular website and mobile app dedicated to tracking athletic activity (cycling and running), began offering a data service called Strava Metro, designed to help transportation researchers and urban planners to improve infrastructure for cyclists and pedestrians. Strava Metro data has the potential to promote studies of cycling and health by indicating where commuting and non-commuting cycling activities are at a large spatial scale (street level and intersection level). The assessment of spatially varying effects of air pollution during active travel (cycling or walking) might benefit from Strava Metro data, as a variation in air pollution levels within a city would be expected. In this paper, to explore the potential of Strava Metro data in research of active travel and health, we investigate spatial patterns of non-commuting cycling activities and associations between cycling purpose (commuting and non-commuting) and air pollution exposure at a large scale. Additionally, we attempt to estimate the number of non-commuting cycling trips according to environmental characteristics that may help identify cycling behavior. Researchers who are undertaking studies relating to cycling purpose could benefit from this approach in their use of cycling trip data sets that lack trip purpose. We use the Strava Metro Nodes data from Glasgow, United Kingdom in an empirical study. Empirical results reveal some findings that (1) when compared with commuting cycling activities, non-commuting cycling activities are more likely to be located in outskirts of the city; (2) spatially speaking, cyclists riding for recreation and other purposes are more likely to be exposed to relatively low levels of air pollution than cyclists riding for commuting; and (3) the method for estimating of the number of non-commuting cycling activities works well in this study. The results highlight: (1) a need for policymakers to consider how to improve cycling infrastructure and road safety in outskirts of cities; and (2) a possible way of estimating the number of non-commuting cycling activities when the trip purpose of cycling data is unknown.

ACS Style

Yeran Sun; Amin Mobasheri. Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data. International Journal of Environmental Research and Public Health 2017, 14, 274 .

AMA Style

Yeran Sun, Amin Mobasheri. Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data. International Journal of Environmental Research and Public Health. 2017; 14 (3):274.

Chicago/Turabian Style

Yeran Sun; Amin Mobasheri. 2017. "Utilizing Crowdsourced Data for Studies of Cycling and Air Pollution Exposure: A Case Study Using Strava Data." International Journal of Environmental Research and Public Health 14, no. 3: 274.

Book chapter
Published: 25 August 2016 in European Handbook of Crowdsourced Geographic Information
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This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives.The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society?Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development.The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

ACS Style

Alexander Zipf; Amin Mobasheri; Adam Rousell; Stefan Hahmann; Cristina Capineri; Muki Haklay; HaoSheng Huang; Vyron Antoniou; Juhani Kettunen; Frank Ostermann; Ross Purves. Crowdsourcing for individual needs – the case of routing and navigation for mobility-impaired persons. European Handbook of Crowdsourced Geographic Information 2016, 325 -337.

AMA Style

Alexander Zipf, Amin Mobasheri, Adam Rousell, Stefan Hahmann, Cristina Capineri, Muki Haklay, HaoSheng Huang, Vyron Antoniou, Juhani Kettunen, Frank Ostermann, Ross Purves. Crowdsourcing for individual needs – the case of routing and navigation for mobility-impaired persons. European Handbook of Crowdsourced Geographic Information. 2016; ():325-337.

Chicago/Turabian Style

Alexander Zipf; Amin Mobasheri; Adam Rousell; Stefan Hahmann; Cristina Capineri; Muki Haklay; HaoSheng Huang; Vyron Antoniou; Juhani Kettunen; Frank Ostermann; Ross Purves. 2016. "Crowdsourcing for individual needs – the case of routing and navigation for mobility-impaired persons." European Handbook of Crowdsourced Geographic Information , no. : 325-337.

Journal article
Published: 07 June 2016 in ISPRS International Journal of Geo-Information
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The increased development of Volunteered Geographic Information (VGI) and its potential role in GIScience studies raises questions about the resulting data quality. Several studies address VGI quality from various perspectives like completeness, positional accuracy, consistency, etc. They mostly have consensus on the heterogeneity of data quality. The problem may be due to the lack of standard procedures for data collection and absence of quality control feedback for voluntary participants. In our research, we are concerned with data quality from the classification perspective. Particularly in VGI-mapping projects, the limited expertise of participants and the non-strict definition of geographic features lead to conceptual overlapping classes, where an entity could plausibly belong to multiple classes, e.g., lake or pond, park or garden, marsh or swamp, etc. Usually, quantitative and/or qualitative characteristics exist that distinguish between classes. Nevertheless, these characteristics might not be recognizable for non-expert participants. In previous work, we developed the rule-guided classification approach that guides participants to the most appropriate classes. As exemplification, we tackle the conceptual overlapping of some grass-related classes. For a given data set, our approach presents the most highly recommended classes for each entity. In this paper, we present the validation of our approach. We implement a web-based application called Grass&Green that presents recommendations for crowdsourcing validation. The findings show the applicability of the proposed approach. In four months, the application attracted 212 participants from more than 35 countries who checked 2,865 entities. The results indicate that 89% of the contributions fully/partially agree with our recommendations. We then carried out a detailed analysis that demonstrates the potential of this enhanced data classification. This research encourages the development of customized applications that target a particular geographic feature.

ACS Style

Ahmed Loai Ali; Nuttha Sirilertworakul; Alexander Zipf; Amin Mobasheri. Guided Classification System for Conceptual Overlapping Classes in OpenStreetMap. ISPRS International Journal of Geo-Information 2016, 5, 87 .

AMA Style

Ahmed Loai Ali, Nuttha Sirilertworakul, Alexander Zipf, Amin Mobasheri. Guided Classification System for Conceptual Overlapping Classes in OpenStreetMap. ISPRS International Journal of Geo-Information. 2016; 5 (6):87.

Chicago/Turabian Style

Ahmed Loai Ali; Nuttha Sirilertworakul; Alexander Zipf; Amin Mobasheri. 2016. "Guided Classification System for Conceptual Overlapping Classes in OpenStreetMap." ISPRS International Journal of Geo-Information 5, no. 6: 87.

Review
Published: 31 May 2016 in International Journal of Geographical Information Science
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With the ubiquity of advanced web technologies and location-sensing hand held devices, citizens regardless of their knowledge or expertise, are able to produce spatial information. This phenomenon is known as volunteered geographic information VGI. During the past decade VGI has been used as a data source supporting a wide range of services, such as environmental monitoring, events reporting, human movement analysis, disaster management, etc. However, these volunteer-contributed data also come with varying quality. Reasons for this are: data is produced by heterogeneous contributors, using various technologies and tools, having different level of details and precision, serving heterogeneous purposes, and a lack of gatekeepers. Crowd-sourcing, social, and geographic approaches have been proposed and later followed to develop appropriate methods to assess the quality measures and indicators of VGI. In this article, we review various quality measures and indicators for selected types of VGI and existing quality assessment methods. As an outcome, the article presents a classification of VGI with current methods utilized to assess the quality of selected types of VGI. Through these findings, we introduce data mining as an additional approach for quality handling in VGI.

ACS Style

Hansi Senaratne; Amin Mobasheri; Ahmed Loai Ali; Cristina Capineri; Mordechai Haklay. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science 2016, 31, 139 -167.

AMA Style

Hansi Senaratne, Amin Mobasheri, Ahmed Loai Ali, Cristina Capineri, Mordechai Haklay. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science. 2016; 31 (1):139-167.

Chicago/Turabian Style

Hansi Senaratne; Amin Mobasheri; Ahmed Loai Ali; Cristina Capineri; Mordechai Haklay. 2016. "A review of volunteered geographic information quality assessment methods." International Journal of Geographical Information Science 31, no. 1: 139-167.

Conference paper
Published: 01 July 2015 in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Disaster response actors and decision makers need to perform several tasks and decisions in a short time. Handling such tasks requires access to sufficient, relevant and up-to-date datasets. Some of these datasets are static such as road network infrastructure and map of buildings. While several other kind of required information are dynamic and change during the occurrence of disaster. Such information may include number of casualties, wind speed, wind direction, road obstacles, etc. Semantic integration of various sources of information is the key to making efficient and fast actions by the actors in the filed as well as top-level decision makers. In this paper, we elaborate on the research challenges of data integration from multiple heterogeneous sources by proposing the system architecture of ASSIST (Access, Semantic Search and Integration Service and Translation). The paper concludes with discussing the future work on this smart service.

ACS Style

Amin Mobasheri; Mohamed Bakillah. Towards a unified infrastructure for automated management and integration of heterogeneous Geo-datasets in disaster response. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015, 4570 -4573.

AMA Style

Amin Mobasheri, Mohamed Bakillah. Towards a unified infrastructure for automated management and integration of heterogeneous Geo-datasets in disaster response. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015; ():4570-4573.

Chicago/Turabian Style

Amin Mobasheri; Mohamed Bakillah. 2015. "Towards a unified infrastructure for automated management and integration of heterogeneous Geo-datasets in disaster response." 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 4570-4573.

Original articles
Published: 24 April 2014 in International Journal of Geographical Information Science
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Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.

ACS Style

Mohamed Bakillah; Steve Liang; Amin Mobasheri; Jamal Jokar Arsanjani; Alexander Zipf. Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science 2014, 28, 1940 -1963.

AMA Style

Mohamed Bakillah, Steve Liang, Amin Mobasheri, Jamal Jokar Arsanjani, Alexander Zipf. Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science. 2014; 28 (9):1940-1963.

Chicago/Turabian Style

Mohamed Bakillah; Steve Liang; Amin Mobasheri; Jamal Jokar Arsanjani; Alexander Zipf. 2014. "Fine-resolution population mapping using OpenStreetMap points-of-interest." International Journal of Geographical Information Science 28, no. 9: 1940-1963.

Conference paper
Published: 01 September 2013 in 2013 5th Computer Science and Electronic Engineering Conference (CEEC)
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The rapidly growing number of crowdsourcing platforms generates huge volumes of volunteered geographic information (VGI), which requires analysis to reveal their potential. The huge volumes of data appear as an opportunity to improve various applications, including routing and navigation services. How existing techniques for dealing with Big Data could be useful for the analysis of VGI remains an open question, since VGI differs from traditional data. In this paper, we focus on examining the latest developments and issues associated with big data from the perspective of the analysis of VGI. This paper notably presents our new architecture for exploiting Big VGI in event service processing in support to optimization of routing service. In addition, our study highlights the opportunities that are created by the emergence of Big VGI and crowdsourced data on improving routing and navigation services, as well as the challenges that remain to be addressed to make this a reality. Finally, avenues for future research on the next generation of collaborative routing and navigation services are presented.

ACS Style

Mohamed Bakillah; Steve H. L. Liang; Amin Mobasheri; Alexander Zipf. Towards an efficient routing web processing service through capturing real-time road conditions from big data. 2013 5th Computer Science and Electronic Engineering Conference (CEEC) 2013, 152 -155.

AMA Style

Mohamed Bakillah, Steve H. L. Liang, Amin Mobasheri, Alexander Zipf. Towards an efficient routing web processing service through capturing real-time road conditions from big data. 2013 5th Computer Science and Electronic Engineering Conference (CEEC). 2013; ():152-155.

Chicago/Turabian Style

Mohamed Bakillah; Steve H. L. Liang; Amin Mobasheri; Alexander Zipf. 2013. "Towards an efficient routing web processing service through capturing real-time road conditions from big data." 2013 5th Computer Science and Electronic Engineering Conference (CEEC) , no. : 152-155.

Conference paper
Published: 01 August 2013 in Third International Conference on Innovative Computing Technology (INTECH 2013)
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Evaluating the quality of geospatial dataset is an important aspect that needs to be considered in order to improve the quality of results in any project. This issue has become even more critical these days due to the ever growing platforms and services that produce and share Volunteered Geographic Information (VGI) in the world wide domain. In this paper, we introduce the architecture of QualEvS4Geo; a web processing service for evaluating the quality of geo-data in Spatial Data Infrastructure (SDI). QualEvS4Geo is supposed to utilize a wide range of technologies, tools and knowledge bases in order to hamper interoperability between various services, each of which evaluating geo-datasets based on a certain data quality element and sub-element. This paper proposes a peer-to-peer infrastructure for enabling a universal geo-data quality evaluation service, which will enable end-users with different needs in various application domains to evaluate their geo-datasets through a single interface. Requirements are derived, the system architecture is proposed and implementation aspects are discussed.

ACS Style

Amin Mobasheri; Alexander Zipf; Mohamed Bakillah; Steve H. L. Liang. QualEvS4Geo: A peer-to-peer system architecture for semi-automated quality evaluation of geo-data in SDI. Third International Conference on Innovative Computing Technology (INTECH 2013) 2013, 7 -11.

AMA Style

Amin Mobasheri, Alexander Zipf, Mohamed Bakillah, Steve H. L. Liang. QualEvS4Geo: A peer-to-peer system architecture for semi-automated quality evaluation of geo-data in SDI. Third International Conference on Innovative Computing Technology (INTECH 2013). 2013; ():7-11.

Chicago/Turabian Style

Amin Mobasheri; Alexander Zipf; Mohamed Bakillah; Steve H. L. Liang. 2013. "QualEvS4Geo: A peer-to-peer system architecture for semi-automated quality evaluation of geo-data in SDI." Third International Conference on Innovative Computing Technology (INTECH 2013) , no. : 7-11.