It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution.In this context,geography,with the human-nature relationship as its ...It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution.In this context,geography,with the human-nature relationship as its core,is undergoing a transition from strictly earth observations to the observation of human activities.Geocomputation for social science is one manifestation thereof.Geocomputation for social science is an interdisciplinary approach combining remote sensing techniques,social science,and big data computation.Driven by the availability of spatially and temporally expansive big data,geocomputation for social science uses spatiotemporal statistical analyses to detect and analyze the interactions between human behavior,the natural environment,and social activities;Remote sensing(RS)observations are used as primary data.Geocomputation for social science can be used to investigate major social issues and to assess the impact of major natural and societal events,and will surely be an area of focused development in geography in the near future.We briefly review the background of geocomputation in the social sciences,discuss its definition and disciplinary characteristics,and highlight the main research foci.Several key technologies and applications are also illustrated with relevant case studies of the Syrian Civil War,typhoon transits,and traffic patterns.展开更多
During the last decade, spatio-temporal databases have become increasingly important in many applications such as geographic information systems (GIS) and engineering information systems. This paper discusses the de...During the last decade, spatio-temporal databases have become increasingly important in many applications such as geographic information systems (GIS) and engineering information systems. This paper discusses the design and implementation of a geocomputing platform for the development of location-based services (LBS) focusing on mobile mapping. During the analysis, design, and implementation of the geocomputing platform, an effective method is proposed for the real-time processing of geographic information acquired by a camera attached to a personal digital assistant (PDA). This method combines location information given by the global position system (GPS) with man's ability to recognize the location of objects and their geographical relationship to improve object mapping.展开更多
Ensuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal(SDG).Inequality in access to health care remains one of the primary challenges in achieving the goal.With the ever-i...Ensuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal(SDG).Inequality in access to health care remains one of the primary challenges in achieving the goal.With the ever-increasing expansion of urban areas and population growth,there is a need to regularly examine the pattern of accessibility of basic amenities across regions,States and urban areas.This study examined geographic access to Primary Health Care Facilities(PHCF)in Nigeria using the combination of open data and geospatial analysis techniques.Thus,showcasing an approach can be replicated across different regions in Sub-Saharan Africa due to issues of information gap.Data on elevation,location of health care facilities,population and network data were utilised.The result shows that PHCF aggregate at certain locations,e.g.major urban agglomerations,and transit route leading to these places.High concentrations are found in the capital city.The average travel time to the nearest PHCF is about 14 min(Standard Deviation±13.30 min)while the maximum is about 2 hours.Pockets of low accessibility areas exist across the Akwa Ibom State in the Niger Delta region of Nigeria.There is an indication that most places have good geographic access.Across the 1787 settlements identified in our dataset,98.3%are with good access(<30 min),27 settlements are located in the poor access class(31–60 min),while two settlements are within the very poor access class(>60 min).Geographic access is not the main limiting factor to health care access in the region.Therefore,computation of access to health care should take into consideration other dimensions of accessibility,to create a robust measure which will support effective and efficient health care planning and delivery.展开更多
A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At pre...A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).展开更多
Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic in...Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic information systems(GIS)may open new opportunities for data scientists.In theory,analysts may simply ask spatial questions to exploit diverse geographic information resources,without a need to know how GIS tools and geodata sets interoperate.In this outlook article,we investigate the scientific challenges of geo-analytical question answering,introducing the problems of unknown answers and indirect QA.Furthermore,we argue why core concepts of spatial information play an important role in addressing this challenge,enabling us to describe analytic potentials,and to compose spatial questions and workflows for generating answers.展开更多
Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial d...Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.展开更多
文摘It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution.In this context,geography,with the human-nature relationship as its core,is undergoing a transition from strictly earth observations to the observation of human activities.Geocomputation for social science is one manifestation thereof.Geocomputation for social science is an interdisciplinary approach combining remote sensing techniques,social science,and big data computation.Driven by the availability of spatially and temporally expansive big data,geocomputation for social science uses spatiotemporal statistical analyses to detect and analyze the interactions between human behavior,the natural environment,and social activities;Remote sensing(RS)observations are used as primary data.Geocomputation for social science can be used to investigate major social issues and to assess the impact of major natural and societal events,and will surely be an area of focused development in geography in the near future.We briefly review the background of geocomputation in the social sciences,discuss its definition and disciplinary characteristics,and highlight the main research foci.Several key technologies and applications are also illustrated with relevant case studies of the Syrian Civil War,typhoon transits,and traffic patterns.
基金supported by the Basic Science Research Program through the National Research Foundation (NRF) funded by Korea government (MEST) under Grant No. 2012-0005127 and No. 2011-0026536
文摘During the last decade, spatio-temporal databases have become increasingly important in many applications such as geographic information systems (GIS) and engineering information systems. This paper discusses the design and implementation of a geocomputing platform for the development of location-based services (LBS) focusing on mobile mapping. During the analysis, design, and implementation of the geocomputing platform, an effective method is proposed for the real-time processing of geographic information acquired by a camera attached to a personal digital assistant (PDA). This method combines location information given by the global position system (GPS) with man's ability to recognize the location of objects and their geographical relationship to improve object mapping.
文摘Ensuring healthy lives and promoting well-being for all ages is the 3rd Sustainable Development Goal(SDG).Inequality in access to health care remains one of the primary challenges in achieving the goal.With the ever-increasing expansion of urban areas and population growth,there is a need to regularly examine the pattern of accessibility of basic amenities across regions,States and urban areas.This study examined geographic access to Primary Health Care Facilities(PHCF)in Nigeria using the combination of open data and geospatial analysis techniques.Thus,showcasing an approach can be replicated across different regions in Sub-Saharan Africa due to issues of information gap.Data on elevation,location of health care facilities,population and network data were utilised.The result shows that PHCF aggregate at certain locations,e.g.major urban agglomerations,and transit route leading to these places.High concentrations are found in the capital city.The average travel time to the nearest PHCF is about 14 min(Standard Deviation±13.30 min)while the maximum is about 2 hours.Pockets of low accessibility areas exist across the Akwa Ibom State in the Niger Delta region of Nigeria.There is an indication that most places have good geographic access.Across the 1787 settlements identified in our dataset,98.3%are with good access(<30 min),27 settlements are located in the poor access class(31–60 min),while two settlements are within the very poor access class(>60 min).Geographic access is not the main limiting factor to health care access in the region.Therefore,computation of access to health care should take into consideration other dimensions of accessibility,to create a robust measure which will support effective and efficient health care planning and delivery.
文摘A significant Geographic Information Science(GIS)issue is closely related to spatial autocorrelation,a burning question in the phase of information extraction from the statistical analysis of georeferenced data.At present,spatial autocorrelation presents two types of measures:continuous and discrete.Is it possible to use Moran’s I and the Moran scatterplot with continuous data?Is it possible to use the same methodology with discrete data?A particular and cumbersome problem is the choice of the spatial-neighborhood matrix(W)for points data.This paper addresses these issues by introducing the concept of covariogram contiguity,where each weight is based on the variogram model for that particular dataset:(1)the variogram,whose range equals the distance with the highest Moran I value,defines the weights for points separated by less than the estimated range and(2)weights equal zero for points widely separated from the variogram range considered.After the W matrix is computed,the Moran location scatterplot is created in an iterative process.In accordance with various lag distances,Moran’s I is presented as a good search factor for the optimal neighborhood area.Uncertainty/transition regions are also emphasized.At the same time,a new Exploratory Spatial Data Analysis(ESDA)tool is developed,the Moran variance scatterplot,since the conventional Moran scatterplot is not sensitive to neighbor variance.This computer-mapping framework allows the study of spatial patterns,outliers,changeover areas,and trends in an ESDA process.All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb#(or,in the near future,myGeooffice.org).
基金supported by the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement no.803498(QuAnGIS)).
文摘Question Answering(QA),the process of computing valid answers to questions formulated in natural language,has recently gained attention in both industry and academia.Translating this idea to the realm of geographic information systems(GIS)may open new opportunities for data scientists.In theory,analysts may simply ask spatial questions to exploit diverse geographic information resources,without a need to know how GIS tools and geodata sets interoperate.In this outlook article,we investigate the scientific challenges of geo-analytical question answering,introducing the problems of unknown answers and indirect QA.Furthermore,we argue why core concepts of spatial information play an important role in addressing this challenge,enabling us to describe analytic potentials,and to compose spatial questions and workflows for generating answers.
基金Anselin’s research was supported in part by award OCI-1047916,SI2-SSI from the US National Science Foundation.
文摘Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.