Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, analytic models based on census data, which are either unable to adapt to the dynamic market or very time consuming. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data- driven prediction models for business store site selection. In this paper, we propose a Demand Driven Store Site Selection (DD3S) framework for business store site selection by mining search query data from Baidu Maps. DD3S first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and supply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also use learning-to-rank model to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiment results demonstrate the effectiveness of our methods. DD3S as the core function for store site selection has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.
{"title":"Demand driven store site selection via multiple spatial-temporal data","authors":"Mengwen Xu, Tianyi Wang, Zhengwei Wu, Jingbo Zhou, Jian Li, Haishan Wu","doi":"10.1145/2996913.2996996","DOIUrl":"https://doi.org/10.1145/2996913.2996996","url":null,"abstract":"Choosing a good location when opening a new store is crucial for the future success of a business. Traditional methods include offline manual survey, analytic models based on census data, which are either unable to adapt to the dynamic market or very time consuming. The rapid increase of the availability of big data from various types of mobile devices, such as online query data and offline positioning data, provides us with the possibility to develop automatic and accurate data- driven prediction models for business store site selection. In this paper, we propose a Demand Driven Store Site Selection (DD3S) framework for business store site selection by mining search query data from Baidu Maps. DD3S first detects the spatial-temporal distributions of customer demands on different business services via query data from Baidu Maps, the largest online map search engine in China, and detects the gaps between demand and supply. Then we determine candidate locations via clustering such gaps. In the final stage, we solve the location optimization problem by predicting and ranking the number of customers. We not only deploy supervised regression models to predict the number of customers, but also use learning-to-rank model to directly rank the locations. We evaluate our framework on various types of businesses in real-world cases, and the experiment results demonstrate the effectiveness of our methods. DD3S as the core function for store site selection has already been implemented as a core component of our business analytics platform and could be potentially used by chain store merchants on Baidu Nuomi.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"321 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89236071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tyng-Ruey Chuang, Jheng-Peng Huang, Hsin-Huei Lee, Kae-An Liu, H. Syu
We extract GPS traces from photo streams and analyze them to reveal movement types. Speed and locale patterns hint about the kinds of activity as captured by the photos of the day. When properly categorized and visualized, the photos and their movement patterns help people in navigating the itineraries of their past, and in retreating images of possible highlights. Our method is tolerant of erroneous and missing positional information in the photos' metadata. External geospatial resources can be further combined and visualized with the itineraries and photos to assist people's recollection of the places they were visiting.
{"title":"Assisted journey recollections from photo streams: (demo paper)","authors":"Tyng-Ruey Chuang, Jheng-Peng Huang, Hsin-Huei Lee, Kae-An Liu, H. Syu","doi":"10.1145/2996913.2996955","DOIUrl":"https://doi.org/10.1145/2996913.2996955","url":null,"abstract":"We extract GPS traces from photo streams and analyze them to reveal movement types. Speed and locale patterns hint about the kinds of activity as captured by the photos of the day. When properly categorized and visualized, the photos and their movement patterns help people in navigating the itineraries of their past, and in retreating images of possible highlights. Our method is tolerant of erroneous and missing positional information in the photos' metadata. External geospatial resources can be further combined and visualized with the itineraries and photos to assist people's recollection of the places they were visiting.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76242184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gautam Thakur, Kevin A. Sparks, Roger G. Li, R. Stewart, M. Urban
Crowd-sourced and volunteered information, social media, and participatory sensors are capable of providing real-time activity data. Monitoring these sources in time of relevance and then using them to gather operational knowledge is important during crisis management. Beyond that, it's important to curate this information for geo-spatial research purposes, including land use classification and population occupancy analysis. In this demonstration, we will showcase PlanetSense - a geo-spatial research platform built to harness the existing power of archived data and add to that, the dynamics of heterogeneous real-time streaming data from social media and volunteered sources, seamlessly integrated with sophisticated machine learning algorithms and visualization tools. A demonstration will focus on - 1) Recent initiative emphasizing the need to harness crowd-sources, volunteered, and social media data at scale; 2) Anatomy and insight into data collection workflow. We will show the ability to harvest and process several terabytes of raw data in real-time; 3) A detailed discussion with insight into more than 20 sources of data will be given. These sources include text, sensors, as well as imagery data; 4) PlanetSense's end to end distributed architecture will be discussed with focus on collecting and processing high-volumes of streaming data in a Geo-Data Cloud. Data fusion methods and algorithms for integrating disparate data sources with existing legacy products. Data analytics and machine learning methods for generating operational intelligence on the fly; 5) In addition, PlanetSense "App" platform will be shown with hands-on application enabling interested audience to quickly develop and deploy solutions. 6) Several case studies will be discussed relevant to, land use classification, monitoring transient population, high-resolution occupancy analysis, mapping special events population, ability to uncover global breaking events and reactions in near-real time, ability to track protest, unrest, and monitor other societal turbulences as they happen, and real-time monitoring of infrastructure outages.
{"title":"Demonstrating PlanetSense: gathering geo-spatial intelligence from crowd-sourced and social-media data","authors":"Gautam Thakur, Kevin A. Sparks, Roger G. Li, R. Stewart, M. Urban","doi":"10.1145/2996913.2996975","DOIUrl":"https://doi.org/10.1145/2996913.2996975","url":null,"abstract":"Crowd-sourced and volunteered information, social media, and participatory sensors are capable of providing real-time activity data. Monitoring these sources in time of relevance and then using them to gather operational knowledge is important during crisis management. Beyond that, it's important to curate this information for geo-spatial research purposes, including land use classification and population occupancy analysis. In this demonstration, we will showcase PlanetSense - a geo-spatial research platform built to harness the existing power of archived data and add to that, the dynamics of heterogeneous real-time streaming data from social media and volunteered sources, seamlessly integrated with sophisticated machine learning algorithms and visualization tools. A demonstration will focus on - 1) Recent initiative emphasizing the need to harness crowd-sources, volunteered, and social media data at scale; 2) Anatomy and insight into data collection workflow. We will show the ability to harvest and process several terabytes of raw data in real-time; 3) A detailed discussion with insight into more than 20 sources of data will be given. These sources include text, sensors, as well as imagery data; 4) PlanetSense's end to end distributed architecture will be discussed with focus on collecting and processing high-volumes of streaming data in a Geo-Data Cloud. Data fusion methods and algorithms for integrating disparate data sources with existing legacy products. Data analytics and machine learning methods for generating operational intelligence on the fly; 5) In addition, PlanetSense \"App\" platform will be shown with hands-on application enabling interested audience to quickly develop and deploy solutions. 6) Several case studies will be discussed relevant to, land use classification, monitoring transient population, high-resolution occupancy analysis, mapping special events population, ability to uncover global breaking events and reactions in near-real time, ability to track protest, unrest, and monitor other societal turbulences as they happen, and real-time monitoring of infrastructure outages.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83066568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theodoros Chondrogiannis, J. Gamper, R. Cavaliere, Patrick Ohnewein
In this paper, we present MoTrIS, a service-oriented framework which enables spatio-temporal query processing on multimodal networks that are composed of a road network and one or more schedule-based transportation networks. MoTrIS provides a remote access API, which allows for the development of applications that require the processing of routing queries on multimodal networks. We discuss the architecture of MoTrIS and we elaborate on each of its individual components. The data input module allows for the import of data from various sources into a spatial-enabled relational database. The network module builds a multimodal network by combining a road network with one or more transportation networks. The timetable module stores and queries the schedule for each transportation mode. The query processing module enables the execution of queries over the multimodal network. The visualization module exports the results into a visualizable format. Finally, we present a web application which allows users to create, modify and test advanced spatio-temporal services, and we demonstrate all the necessary steps for a user to build such a new service.
{"title":"MoTrIS: a framework for route planning on multimodal transportation networks","authors":"Theodoros Chondrogiannis, J. Gamper, R. Cavaliere, Patrick Ohnewein","doi":"10.1145/2996913.2997007","DOIUrl":"https://doi.org/10.1145/2996913.2997007","url":null,"abstract":"In this paper, we present MoTrIS, a service-oriented framework which enables spatio-temporal query processing on multimodal networks that are composed of a road network and one or more schedule-based transportation networks. MoTrIS provides a remote access API, which allows for the development of applications that require the processing of routing queries on multimodal networks. We discuss the architecture of MoTrIS and we elaborate on each of its individual components. The data input module allows for the import of data from various sources into a spatial-enabled relational database. The network module builds a multimodal network by combining a road network with one or more transportation networks. The timetable module stores and queries the schedule for each transportation mode. The query processing module enables the execution of queries over the multimodal network. The visualization module exports the results into a visualizable format. Finally, we present a web application which allows users to create, modify and test advanced spatio-temporal services, and we demonstrate all the necessary steps for a user to build such a new service.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"93 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91078877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Myeong-Hun Jeong, Yaping Cai, C. Sullivan, Shaowen Wang
This paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.
{"title":"Data depth based clustering analysis","authors":"Myeong-Hun Jeong, Yaping Cai, C. Sullivan, Shaowen Wang","doi":"10.1145/2996913.2996984","DOIUrl":"https://doi.org/10.1145/2996913.2996984","url":null,"abstract":"This paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83593346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.
{"title":"A spatio-temporal, Gaussian process regression, real-estate price predictor","authors":"Henry Crosby, Paul Davis, T. Damoulas, S. Jarvis","doi":"10.1145/2996913.2996960","DOIUrl":"https://doi.org/10.1145/2996913.2996960","url":null,"abstract":"This paper introduces a novel four-stage methodology for real-estate valuation. This research shows that space, property, economic, neighbourhood and time features are all contributing factors in producing a house price predictor in which validation shows a 96.6% accuracy on Gaussian Process Regression beating regression-kriging, random forests and an M5P-decision-tree. The output is integrated into a commercial real estate decision engine.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"387 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77674772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianfeng Jia, Chen Li, Xi Zhang, Chen Li, M. Carey, Simon Su
We present a system called "Cloudberry" that allows users to interactively query, analyze, and visualize large amounts of data with temporal, spatial, and textual dimensions. As a general-purpose full-stack solution, it has a friendly UI, intelligent middleware, and a powerful big data management backend running Apache AsterixDB to enable big data analytics and visualization. We will demonstrate the system using Twitter data on a computer cluster.
{"title":"Towards interactive analytics and visualization on one billion tweets","authors":"Jianfeng Jia, Chen Li, Xi Zhang, Chen Li, M. Carey, Simon Su","doi":"10.1145/2996913.2996923","DOIUrl":"https://doi.org/10.1145/2996913.2996923","url":null,"abstract":"We present a system called \"Cloudberry\" that allows users to interactively query, analyze, and visualize large amounts of data with temporal, spatial, and textual dimensions. As a general-purpose full-stack solution, it has a friendly UI, intelligent middleware, and a powerful big data management backend running Apache AsterixDB to enable big data analytics and visualization. We will demonstrate the system using Twitter data on a computer cluster.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83505868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The problem is challenging because removing a single user from the dataset will impact multiple records of the database; i.e., all the visits made by that user to various locations. Towards this end, we first derive non-trivial, tight bounds for both local and global sensitivity of LE, and show that to satisfy ε-differential privacy, a large amount of noise must be introduced, rendering the published results useless. Hence, we propose a thresholding technique to limit the number of users' visits, which significantly reduces the perturbation error but introduces an approximation error. To achieve better utility, we extend the technique by adopting two weaker notions of privacy: smooth sensitivity (slightly weaker) and crowd-blending (strictly weaker). Extensive experiments on synthetic and real-world datasets show that our proposed techniques preserve original data distribution without compromising location privacy.
{"title":"Differentially private publication of location entropy","authors":"Hien To, Kien Nguyen, C. Shahabi","doi":"10.1145/2996913.2996985","DOIUrl":"https://doi.org/10.1145/2996913.2996985","url":null,"abstract":"Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The problem is challenging because removing a single user from the dataset will impact multiple records of the database; i.e., all the visits made by that user to various locations. Towards this end, we first derive non-trivial, tight bounds for both local and global sensitivity of LE, and show that to satisfy ε-differential privacy, a large amount of noise must be introduced, rendering the published results useless. Hence, we propose a thresholding technique to limit the number of users' visits, which significantly reduces the perturbation error but introduces an approximation error. To achieve better utility, we extend the technique by adopting two weaker notions of privacy: smooth sensitivity (slightly weaker) and crowd-blending (strictly weaker). Extensive experiments on synthetic and real-world datasets show that our proposed techniques preserve original data distribution without compromising location privacy.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88234887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we investigate state-of-the-art computer vision techniques to perform large scale geolocalization of overhead imagery through image matching. We consider two types of features: scale invariant feature transform and region-based shape features. Since these features can be high dimensional and an image can contain many of them, using them to perform image matching can be computationally expensive. Therefore, we also investigate two methods for performing efficient matching: aggregating the features at the image level using a bag of words framework and using hashing to perform multiple, efficient matches and then aggregating the results. We show that hashing performs better in terms of accuracy but is expensive computationally compared to bag of words. We also show that shape features may be accurate and efficient for small data sets, but they do not scale well to large data sets.
{"title":"Large-scale geolocalization of overhead imagery","authors":"Mehul Divecha, S. Newsam","doi":"10.1145/2996913.2996980","DOIUrl":"https://doi.org/10.1145/2996913.2996980","url":null,"abstract":"In this paper, we investigate state-of-the-art computer vision techniques to perform large scale geolocalization of overhead imagery through image matching. We consider two types of features: scale invariant feature transform and region-based shape features. Since these features can be high dimensional and an image can contain many of them, using them to perform image matching can be computationally expensive. Therefore, we also investigate two methods for performing efficient matching: aggregating the features at the image level using a bag of words framework and using hashing to perform multiple, efficient matches and then aggregating the results. We show that hashing performs better in terms of accuracy but is expensive computationally compared to bag of words. We also show that shape features may be accurate and efficient for small data sets, but they do not scale well to large data sets.","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88717294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pursuing criminal activity is tied with understanding illegal or unlawful actions taken on opportunity within a geographic location. Mapping such activities can aid significantly in determining the health of a region, and the vicissitudes of civilian life. Methods to track crime and criminal activity after the fact by mapping news reports of it to geographic locations using the NewsStand system are discussed. NewsStand provides a map-query interface to monitor over 10,000 RSS news sources and making them available within minutes after publication. NewsStand was designed to collect event data given keywords centered on locations specified textually and mapping these locations to their spatial representation, a procedure called geotagging. The goal is to demonstrate how to detect and classify criminal activity by geotagging keywords pertaining to crime, and, in effect, to enhance the capabilities of NewsStand to explicitly show this category of news. The resulting system is named "CrimeStand".
{"title":"CrimeStand: spatial tracking of criminal activity","authors":"Faizan Wajid, H. Samet","doi":"10.1145/2996913.2997006","DOIUrl":"https://doi.org/10.1145/2996913.2997006","url":null,"abstract":"Pursuing criminal activity is tied with understanding illegal or unlawful actions taken on opportunity within a geographic location. Mapping such activities can aid significantly in determining the health of a region, and the vicissitudes of civilian life. Methods to track crime and criminal activity after the fact by mapping news reports of it to geographic locations using the NewsStand system are discussed. NewsStand provides a map-query interface to monitor over 10,000 RSS news sources and making them available within minutes after publication. NewsStand was designed to collect event data given keywords centered on locations specified textually and mapping these locations to their spatial representation, a procedure called geotagging. The goal is to demonstrate how to detect and classify criminal activity by geotagging keywords pertaining to crime, and, in effect, to enhance the capabilities of NewsStand to explicitly show this category of news. The resulting system is named \"CrimeStand\".","PeriodicalId":20525,"journal":{"name":"Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"403 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87803159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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