The electromagnetic radiations from the various electronic equipments, such as transmitters for broadcasting of radio/TV and mobile communication, are the major sources of electromagnetic pollution. This is creating a potential hazard for public health. This work aims at mapping the electromagnetic field over population map through open geospatial standards. This may facilitate proper management of man-made electromagnetic radiations and monitoring of public health.
{"title":"A geospatial framework for mapping of electromagnetic radiations","authors":"Saswati Ghosh, A. Dasgupta, Soumya K. Ghosh","doi":"10.1145/1999320.1999349","DOIUrl":"https://doi.org/10.1145/1999320.1999349","url":null,"abstract":"The electromagnetic radiations from the various electronic equipments, such as transmitters for broadcasting of radio/TV and mobile communication, are the major sources of electromagnetic pollution. This is creating a potential hazard for public health. This work aims at mapping the electromagnetic field over population map through open geospatial standards. This may facilitate proper management of man-made electromagnetic radiations and monitoring of public health.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134488962","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}
Photo sharing platforms users often annotate their trip photos with landmark names. These annotations can be aggregated in order to recommend lists of popular visitor attractions similar to those found in classical tourist guides. However, individual tourist preferences can vary significantly so good recommendations should be tailored to individual tastes. Here we pose this visit personalization as a collaborative filtering problem. We mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix. When a user wants to visit a new destination, a list of potentially interesting visitor attractions is produced based on the experience of like-minded users who already visited that destination. We compare our recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users.
{"title":"Mining social media to create personalized recommendations for tourist visits","authors":"Adrian Daniel Popescu, G. Grefenstette","doi":"10.1145/1999320.1999357","DOIUrl":"https://doi.org/10.1145/1999320.1999357","url":null,"abstract":"Photo sharing platforms users often annotate their trip photos with landmark names. These annotations can be aggregated in order to recommend lists of popular visitor attractions similar to those found in classical tourist guides. However, individual tourist preferences can vary significantly so good recommendations should be tailored to individual tastes. Here we pose this visit personalization as a collaborative filtering problem. We mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix. When a user wants to visit a new destination, a list of potentially interesting visitor attractions is produced based on the experience of like-minded users who already visited that destination. We compare our recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132814134","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}
GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.
{"title":"Road extraction using smart phones GPS","authors":"Z. Niu, Songnian Li, Neda Pousaeid","doi":"10.1145/1999320.1999342","DOIUrl":"https://doi.org/10.1145/1999320.1999342","url":null,"abstract":"GPS data crowd-sourced through smart phones is an emerging source of inexpensive data that can be used to provide real-time traffic information, identify traffic patterns, and predict traffic congestions. The same type of data can be very useful for cost-effective, fast updating of road network databases due to its rich spatial and temporal coverage and high data volume. This paper presents the results of a study that extracts road geometry data using GPS data received from smart phones. The focus is on the method for road centerlines and the study presents some promising results. It is expected the method can supplement, if not replace, the current practices of acquiring road network data using traditional expensive and time consuming survey or remote sensing approaches.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133845238","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}
We demonstrate an interactive visualization and analysis system for integrating climate data with other geospatial data sets, such as environmental and demographic data. The iGlobe system is a desktop-based visualization and analysis environment which allows seamless integration of multiple geospatial data sets from varied sources and provides an interface to interactively analyze the different data sets and apply sophisticated data analysis and mining algorithms in a near real time fashion. The framework is highly desirable in domains such as earth and climate sciences where great emphasis is placed on simultaneous analysis of different data sets such as remote sensing images, climate model simulation outputs, and other environmental and demographic databases, to understand weather and climate systems and the impact of climate change on nature and people.
{"title":"iGlobe: an interactive visualization and analysis framework for geospatial data","authors":"V. Chandola, R. Vatsavai, B. Bhaduri","doi":"10.1145/1999320.1999341","DOIUrl":"https://doi.org/10.1145/1999320.1999341","url":null,"abstract":"We demonstrate an interactive visualization and analysis system for integrating climate data with other geospatial data sets, such as environmental and demographic data. The iGlobe system is a desktop-based visualization and analysis environment which allows seamless integration of multiple geospatial data sets from varied sources and provides an interface to interactively analyze the different data sets and apply sophisticated data analysis and mining algorithms in a near real time fashion. The framework is highly desirable in domains such as earth and climate sciences where great emphasis is placed on simultaneous analysis of different data sets such as remote sensing images, climate model simulation outputs, and other environmental and demographic databases, to understand weather and climate systems and the impact of climate change on nature and people.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127149742","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}
Dieter Hildebrandt, Jan Klimke, B. Hagedorn, J. Döllner
Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In this paper, we present a system architecture for service-oriented, interactive 3D visualization of massive 3D city models on thin clients such as mobile phones and tablets. It is based on high performance, server-side 3D rendering of extended cube maps, which are interactively visualized by corresponding 3D thin clients. As key property, the complexity of the cube map data transmitted between server and client does not depend on the model's complexity. In addition, the system allows the integration of thematic raster and vector geodata into the visualization process. Users have extensive control over the contents and styling of the visual representations. The approach provides a solution for safely, robustly distributing and interactively presenting massive 3D city models. A case study related to city marketing based on our prototype implementation shows the potentials of both server-side 3D rendering and fully interactive 3D thin clients on mobile phones.
{"title":"Service-oriented interactive 3D visualization of massive 3D city models on thin clients","authors":"Dieter Hildebrandt, Jan Klimke, B. Hagedorn, J. Döllner","doi":"10.1145/1999320.1999326","DOIUrl":"https://doi.org/10.1145/1999320.1999326","url":null,"abstract":"Virtual 3D city models serve as integration platforms for complex geospatial and georeferenced information and as medium for effective communication of spatial information. In this paper, we present a system architecture for service-oriented, interactive 3D visualization of massive 3D city models on thin clients such as mobile phones and tablets. It is based on high performance, server-side 3D rendering of extended cube maps, which are interactively visualized by corresponding 3D thin clients. As key property, the complexity of the cube map data transmitted between server and client does not depend on the model's complexity. In addition, the system allows the integration of thematic raster and vector geodata into the visualization process. Users have extensive control over the contents and styling of the visual representations. The approach provides a solution for safely, robustly distributing and interactively presenting massive 3D city models. A case study related to city marketing based on our prototype implementation shows the potentials of both server-side 3D rendering and fully interactive 3D thin clients on mobile phones.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127212517","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}
Jonathan Muckell, Jeong-Hyon Hwang, Vikram Patil, C. Lawson, Fan Ping, S. Ravi
GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.
{"title":"SQUISH: an online approach for GPS trajectory compression","authors":"Jonathan Muckell, Jeong-Hyon Hwang, Vikram Patil, C. Lawson, Fan Ping, S. Ravi","doi":"10.1145/1999320.1999333","DOIUrl":"https://doi.org/10.1145/1999320.1999333","url":null,"abstract":"GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data generated. The size of this data makes it difficult to transmit it over a mobile network and to analyze it to extract useful patterns. Numerous compression algorithms have been proposed to reduce the size of trajectory data sets; however these methods often lose important information essential to location-based applications such as object's position, time and speed. This paper describes the Spatial QUalIty Simplification Heuristic (SQUISH) method that demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression. Performance is evaluated by comparison with three competing trajectory compression algorithms: Uniform Sampling, Douglas-Peucker and Dead Reckoning.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125841050","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}
Geocoding, the act of mapping place names and addresses to locations on digital maps, is an important feature of many geographical information systems. Yet, traditional geocoding algorithms can be very inaccurate, especially in rural areas. Land plot maps maintained by local governments can be used to increase accuracy but are not always available. A constraint satisfaction method proposed by Michalowski and Knoblock has the potential to greatly increase accuracy by exploiting two widely available datasets, phone book addresses and building locations derived from aerial photographs, but it may still be inaccurate when the number of buildings does not correspond to the number of addresses. Therefore, this research investigates the accuracy of a method of taking addresses and building locations and grouping the buildings into clusters where each cluster contains the buildings present at a single address. The k-means, complete-link, and a minimum spanning tree-based clustering algorithm are all tested on building locations gathered from aerial photographs of predominantly rural Fulton County, PA, to determine which method creates the most accurate clusters. A secondary hypothesis is tested to find whether geolocating to a cluster centroid or to the building within the cluster that is closest to the road produces locations closer to the address locations provided by Fulton County. If the results of these two experiments yield accurate results, they can be used as an important preprocessing step in a geocoding system based on Michalowski and Knoblock's method.
{"title":"Finding the farm: postal address-based building clustering","authors":"Christopher Eby, Alice Armstrong","doi":"10.1145/1999320.1999370","DOIUrl":"https://doi.org/10.1145/1999320.1999370","url":null,"abstract":"Geocoding, the act of mapping place names and addresses to locations on digital maps, is an important feature of many geographical information systems. Yet, traditional geocoding algorithms can be very inaccurate, especially in rural areas. Land plot maps maintained by local governments can be used to increase accuracy but are not always available. A constraint satisfaction method proposed by Michalowski and Knoblock has the potential to greatly increase accuracy by exploiting two widely available datasets, phone book addresses and building locations derived from aerial photographs, but it may still be inaccurate when the number of buildings does not correspond to the number of addresses. Therefore, this research investigates the accuracy of a method of taking addresses and building locations and grouping the buildings into clusters where each cluster contains the buildings present at a single address. The k-means, complete-link, and a minimum spanning tree-based clustering algorithm are all tested on building locations gathered from aerial photographs of predominantly rural Fulton County, PA, to determine which method creates the most accurate clusters. A secondary hypothesis is tested to find whether geolocating to a cluster centroid or to the building within the cluster that is closest to the road produces locations closer to the address locations provided by Fulton County. If the results of these two experiments yield accurate results, they can be used as an important preprocessing step in a geocoding system based on Michalowski and Knoblock's method.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865491","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}
Broadband access and availability to every American is a key pillar of this administration. Congress authorized the development and maintenance of the National Broadband Map to provide the first ever searchable and interactive map of broadband availability. Transparency in broadband availability had never before been fully realized to both consumers and policy decision makers alike. The collection, integration and publication of this data charts a new course in broadband information access and will add significantly to the policy discussion about increasing American's access to high-speed internet.
{"title":"National broadband map","authors":"Michael Byrne","doi":"10.1145/1999320.1999321","DOIUrl":"https://doi.org/10.1145/1999320.1999321","url":null,"abstract":"Broadband access and availability to every American is a key pillar of this administration. Congress authorized the development and maintenance of the National Broadband Map to provide the first ever searchable and interactive map of broadband availability. Transparency in broadband availability had never before been fully realized to both consumers and policy decision makers alike. The collection, integration and publication of this data charts a new course in broadband information access and will add significantly to the policy discussion about increasing American's access to high-speed internet.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117314257","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}
B. Bhaduri, Brandt Melick, C. Yang, Kevin Montgomery, P. Hogan
Consistent means to share geographic data among all users could produce significant savings for data collection and use and enhance decision making. Executive Order 12906 calls for the establishment of the National Spatial Data Infrastructure (NSDI) defined as the technologies, policies, and people necessary to promote sharing of geospatial data throughout all levels of government, the private and nonprofit sectors, and the academic community. The goal of this Infrastructure is to reduce duplication of effort among agencies, improve quality and reduce costs related to geographic information, make geographic data more accessible to the public, increase the benefits of using available data, and establish key partnerships with states, counties, cities, tribal nations, academia and the private sector to increase data availability. A panel of state, federal, academia and industry experts in spatial data will lead a free-wheeling discussion regarding survival in the spatial age with "data, data everywhere and yet not enough to think." 1. Hundreds of thousands of KML/SHP/etc. spatial data files are out there. How do you find the ones you want? 2. Computation-heavy data analysis is needed just to see climate data, much more do research with. How do you spread that computation load around? 3. Each local government entity throughout each state and across the nation are in need of similar data management tools, as well as access to similar datasets. How do we route ourselves away from duplicative GIS solutions and happily harness the larger community? 4. Given that natural disasters are an inevitable part of life on Earth, be they earthquakes, forest fires or storm activity, how do we optimize access to the considerable datasets (i.e., NetCDF) essential for weather forecasting, climate research and disaster management? Please join us in brainstorming smarter ways for a precious planet to do business.
{"title":"NSDI: Tower of Babel aspiring to lingua franca","authors":"B. Bhaduri, Brandt Melick, C. Yang, Kevin Montgomery, P. Hogan","doi":"10.1145/1999320.1999400","DOIUrl":"https://doi.org/10.1145/1999320.1999400","url":null,"abstract":"Consistent means to share geographic data among all users could produce significant savings for data collection and use and enhance decision making. <u>Executive Order 12906</u> calls for the establishment of the <u>National Spatial Data Infrastructure</u> (NSDI) defined as the technologies, policies, and people necessary to promote sharing of geospatial data throughout all levels of government, the private and nonprofit sectors, and the academic community.\u0000 The goal of this Infrastructure is to reduce duplication of effort among agencies, improve quality and reduce costs related to geographic information, make geographic data more accessible to the public, increase the benefits of using available data, and establish key partnerships with states, counties, cities, tribal nations, academia and the private sector to increase data availability.\u0000 A panel of state, federal, academia and industry experts in spatial data will lead a free-wheeling discussion regarding survival in the spatial age with \"data, data everywhere and yet not enough to think.\"\u0000 1. Hundreds of thousands of KML/SHP/etc. spatial data files are out there. How do you find the ones you want?\u0000 2. Computation-heavy data analysis is needed just to see climate data, much more do research with. How do you spread that computation load around?\u0000 3. Each local government entity throughout each state and across the nation are in need of similar data management tools, as well as access to similar datasets. How do we route ourselves away from duplicative GIS solutions and happily harness the larger community?\u0000 4. Given that natural disasters are an inevitable part of life on Earth, be they earthquakes, forest fires or storm activity, how do we optimize access to the considerable datasets (i.e., NetCDF) essential for weather forecasting, climate research and disaster management?\u0000 Please join us in brainstorming smarter ways for a precious planet to do business.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127715920","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}
Goal of this paper is to compare the timing/performance results of CPU and GPU on local and Cloud platform for processing massive Light Detecting and Ranging (LiDAR) topographic data. We have used locally various multi-core CPU technologies as well as GPU implementations on various graphics cards of nVidia which support CUDA, where as a cloud computing infrastructure we utilized various components of the Amazon Web Services (AWS). In order to study the performance, we have developed and implemented vertex decimation algorithm for data reduction of LiDAR point cloud. Our presentation will demonstrate the preliminary results by comparing the multi-core CPU and GPU based implementations of the code, as well as the comparison with cloud performance.
{"title":"GPU-based cloud performance for LiDAR data processing","authors":"R. Sugumaran, Dossay Oryspayev, P. Gray","doi":"10.1145/1999320.1999369","DOIUrl":"https://doi.org/10.1145/1999320.1999369","url":null,"abstract":"Goal of this paper is to compare the timing/performance results of CPU and GPU on local and Cloud platform for processing massive Light Detecting and Ranging (LiDAR) topographic data. We have used locally various multi-core CPU technologies as well as GPU implementations on various graphics cards of nVidia which support CUDA, where as a cloud computing infrastructure we utilized various components of the Amazon Web Services (AWS). In order to study the performance, we have developed and implemented vertex decimation algorithm for data reduction of LiDAR point cloud. Our presentation will demonstrate the preliminary results by comparing the multi-core CPU and GPU based implementations of the code, as well as the comparison with cloud performance.","PeriodicalId":400763,"journal":{"name":"International Conference and Exhibition on Computing for Geospatial Research & Application","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133886084","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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