Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geograph...Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geographic data.Traditional scan statistic uses regular shapes like circles to detect areas of high activity;the same model was extended to eclipses to improve the model.More recent works identify irregular shaped hotspots for data with geographical boundaries,where information about population within the geographical boundaries is available.With the introduction of better mapping technology,mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed.We propose an approach of spatial hotspot detection for point data set with no geographical boundary information.Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm.The time complexity of the algorithm is non-linear to the number of observations,which does not scale well for larger datasets.To improve the model,we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.展开更多
The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-tempo...The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.展开更多
With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintainin...With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this pa- per, we design iGraph, an incremental graph processing sys- tem for dynamic graph with its continuous updates. The con- tribufions of iGraph include： 1） a hash-based graph partition strategy to enable fine-grained graph updates; 2） a vertex- based graph computing model to support incremental data processing; 3） detection and rebalance methods of hotspot to address the workload imbalance problem during incre- mental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algo- rithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.展开更多
With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal referen...With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.展开更多
基金partially supported by the National Science Foundation under[grant number IIP-1160958],[grant number CNS-1650551],and[grant number CNS-1429526].
文摘Spatial scan statistics is one of the most important models in order to detect high activity or hotspots in real world applications such as epidemiology,public health,astronomy and criminology applications on geographic data.Traditional scan statistic uses regular shapes like circles to detect areas of high activity;the same model was extended to eclipses to improve the model.More recent works identify irregular shaped hotspots for data with geographical boundaries,where information about population within the geographical boundaries is available.With the introduction of better mapping technology,mapping individual cases to latitude and longitude became easier compared to aggregated data for which the previous models were developed.We propose an approach of spatial hotspot detection for point data set with no geographical boundary information.Our algorithm detects hotspots as a polygon made up of a set of triangles that are computed by a Polygon Propagation algorithm.The time complexity of the algorithm is non-linear to the number of observations,which does not scale well for larger datasets.To improve the model,we also introduce a MapReduce version of our algorithm to identify hotspots for larger datasets.
文摘The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.
文摘With the popularity of social network, the de- mand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this pa- per, we design iGraph, an incremental graph processing sys- tem for dynamic graph with its continuous updates. The con- tribufions of iGraph include： 1） a hash-based graph partition strategy to enable fine-grained graph updates; 2） a vertex- based graph computing model to support incremental data processing; 3） detection and rebalance methods of hotspot to address the workload imbalance problem during incre- mental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algo- rithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.
文摘With the advancement of telecommunications,sensor networks,crowd sourcing,and remote sensing technology in present days,there has been a tremendous growth in the volume of data having both spatial and temporal references.This huge volume of available spatio-temporal(ST)data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns,relationships,and knowledge embedded in such large ST datasets.In this survey,we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis.The focus is on outlining various state-of-the-art spatio-temporal data mining techniques,and their applications in various domains.We start with a brief overview of spatio-temporal data and various challenges in analyzing such data,and conclude by listing the current trends and future scopes of research in this multi-disciplinary area.Compared with other relevant surveys,this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives.We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.
