Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches to build models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses with Hadoop.
{"title":"HMM-based address parsing: efficiently parsing billions of addresses on MapReduce","authors":"Xiang Li, Hakan Kardes, Xin Wang, Ang Sun","doi":"10.1145/2666310.2666471","DOIUrl":"https://doi.org/10.1145/2666310.2666471","url":null,"abstract":"Record linkage is the task of identifying which records in one or more data collections refer to the same entity, and address is one of the most commonly used fields in databases. Hence, segmentation of the raw addresses into a set of semantic fields is the primary step in this task. In this paper, we present a probabilistic address parsing system based on the Hidden Markov Model. We also introduce several novel approaches to build models for noisy real-world addresses, obtaining 95.6% F-measure. Furthermore, we demonstrate the viability and efficiency of this system for large-scale data by scaling it up to parse billions of addresses with Hadoop.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126681752","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}
Lipeng Wan, Zhibo Wang, Zheng Lu, H. Qi, Wenjun Zhou, Qing Cao
With advances in vehicle-to-vehicle communication, future vehicles will have access to a communication channel through which messages can be sent and received when two get close to each other. This enabling technology makes it possible for authenticated users to send queries to those vehicles of interest, such as those that are located within a geographic region, over multiple hops for various application goals. However, a naive method that requires flooding the queries to each active vehicle in a region will incur a total communication overhead that is proportional to the size of the area and the density of vehicles. In this paper, we study the problem of spatial queries for vehicle networks by investigating probabilistic methods, where we only try to obtain approximate estimates within desired confidence intervals using only sublinear overheads. We consider this to be particularly useful when spatial query results can be made approximate or not precise, as is the case with many potential applications. The proposed method has been tested on snapshots from real world vehicle network traces.
{"title":"Towards approximate spatial queries for large-scale vehicle networks","authors":"Lipeng Wan, Zhibo Wang, Zheng Lu, H. Qi, Wenjun Zhou, Qing Cao","doi":"10.1145/2666310.2666490","DOIUrl":"https://doi.org/10.1145/2666310.2666490","url":null,"abstract":"With advances in vehicle-to-vehicle communication, future vehicles will have access to a communication channel through which messages can be sent and received when two get close to each other. This enabling technology makes it possible for authenticated users to send queries to those vehicles of interest, such as those that are located within a geographic region, over multiple hops for various application goals. However, a naive method that requires flooding the queries to each active vehicle in a region will incur a total communication overhead that is proportional to the size of the area and the density of vehicles. In this paper, we study the problem of spatial queries for vehicle networks by investigating probabilistic methods, where we only try to obtain approximate estimates within desired confidence intervals using only sublinear overheads. We consider this to be particularly useful when spatial query results can be made approximate or not precise, as is the case with many potential applications. The proposed method has been tested on snapshots from real world vehicle network traces.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126905586","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 present an automatic system that learns symbolic representations of activities from examples in Wide Area Aerial Surveillance (WAAS). In the previous work, we presented an ERM (Entity Relationship Models)-based activity recognition system in which finding an activity is equivalent to sending a query, defined by SQL statements, to a Relational DataBase Management System (RDBMS). The system enables us to identify spatial and geo-spatial activities in WAAS as long as activities are carefully defined by human operators. Here, we show how to infer a structured definition of an activity from examples provided by a user. Our system randomly generates a set of possible SQL statements using a logic generator in a MCMC framework, uses a memory-based RDBMS to validate generated SQL statements with the input data/database, and selects the best answer that allows the RDBMS to explain the input positive examples while excluding negative examples. We have evaluated our system on real visual tracks. Our system can find activity definitions from input examples and associated query results including motion patterns (e.g., "loop") and geospatial activities (e.g., "parking in a lot").
{"title":"Learning symbolic descriptions of activities from examples in WAAS","authors":"Jongmoo Choi, G. Medioni","doi":"10.1145/2666310.2666434","DOIUrl":"https://doi.org/10.1145/2666310.2666434","url":null,"abstract":"We present an automatic system that learns symbolic representations of activities from examples in Wide Area Aerial Surveillance (WAAS). In the previous work, we presented an ERM (Entity Relationship Models)-based activity recognition system in which finding an activity is equivalent to sending a query, defined by SQL statements, to a Relational DataBase Management System (RDBMS). The system enables us to identify spatial and geo-spatial activities in WAAS as long as activities are carefully defined by human operators. Here, we show how to infer a structured definition of an activity from examples provided by a user. Our system randomly generates a set of possible SQL statements using a logic generator in a MCMC framework, uses a memory-based RDBMS to validate generated SQL statements with the input data/database, and selects the best answer that allows the RDBMS to explain the input positive examples while excluding negative examples. We have evaluated our system on real visual tracks. Our system can find activity definitions from input examples and associated query results including motion patterns (e.g., \"loop\") and geospatial activities (e.g., \"parking in a lot\").","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123079238","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 present an approach to "participatory route planning," a novel concept that takes advantage of mobile devices, such as cellular phones or embedded systems in cars, to form an interactive, participatory network of vehicles that plan their travel routes based on the current traffic conditions and existing routes planned by the network of participants, thereby making more informed travel decision for each participating user. The premise of this approach is that a route, or plan, for a vehicle is also a prediction of where the car will travel. If routes are created for a sizable percentage of the total vehicle population, an estimate for the overall traffic pattern is attainable. Taking planned routes into account as predictions allows the entire traffic route planning system to better distribute vehicles and minimize traffic congestion. We present an approach that is suitable for realistic, city-scale scenarios, a prototype system to demonstrate feasibility, and experiments using a state-of-the-art microscopic traffic simulator.
{"title":"Participatory route planning","authors":"David Wilkie, Cenk Baykal, M. Lin","doi":"10.1145/2666310.2666406","DOIUrl":"https://doi.org/10.1145/2666310.2666406","url":null,"abstract":"We present an approach to \"participatory route planning,\" a novel concept that takes advantage of mobile devices, such as cellular phones or embedded systems in cars, to form an interactive, participatory network of vehicles that plan their travel routes based on the current traffic conditions and existing routes planned by the network of participants, thereby making more informed travel decision for each participating user. The premise of this approach is that a route, or plan, for a vehicle is also a prediction of where the car will travel. If routes are created for a sizable percentage of the total vehicle population, an estimate for the overall traffic pattern is attainable. Taking planned routes into account as predictions allows the entire traffic route planning system to better distribute vehicles and minimize traffic congestion. We present an approach that is suitable for realistic, city-scale scenarios, a prototype system to demonstrate feasibility, and experiments using a state-of-the-art microscopic traffic simulator.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131285827","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}
Louai Alarabi, A. Eldawy, Rami Alghamdi, M. Mokbel
Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not easily available for most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing and experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This paper presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap data set; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. Experimental results show that TAREEG is highly accurate and efficient.
{"title":"TAREEG: a MapReduce-based system for extracting spatial data from OpenStreetMap","authors":"Louai Alarabi, A. Eldawy, Rami Alghamdi, M. Mokbel","doi":"10.1145/2666310.2666403","DOIUrl":"https://doi.org/10.1145/2666310.2666403","url":null,"abstract":"Real spatial data, e.g., detailed road networks, rivers, buildings, parks, are not easily available for most of the world. This hinders the practicality of many research ideas that need a real spatial data for testing and experiments. Such data is often available for governmental use, or at major software companies, but it is prohibitively expensive to build or buy for academia or individual researchers. This paper presents TAREEG; a web-service that makes real spatial data, from anywhere in the world, available at the fingertips of every researcher or individual. TAREEG gets all its data by leveraging the richness of OpenStreetMap data set; the most comprehensive available spatial data of the world. Yet, it is still challenging to obtain OpenStreetMap data due to the size limitations, special data format, and the noisy nature of spatial data. TAREEG employs MapReduce-based techniques to make it efficient and easy to extract OpenStreetMap data in a standard form with minimal effort. Experimental results show that TAREEG is highly accurate and efficient.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133249077","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 consider the problem of labeling linear objects (such as streets) in interactive maps where the user can pan, zoom, and rotate continuously. Our labels contain text (such as street names). They are embedded into the objects they label, i.e., they follow the curvature of the objects, they do not move with respect to the map background, but they scale in order to maintain constant size on the screen. To the best of our knowledge, this is the first work that deals with curved labels in interactive maps. Our objective is to label as many streets as possible and to select label positions of high quality while forbidding labels to overlap at street crossings. We present a simple but effective algorithm that takes curvature and crossings into account and produces aesthetical labelings. On average over all interaction types, our implementation reaches interactive frame rates of more than 85 frames per second.
{"title":"Labeling streets in interactive maps using embedded labels","authors":"Nadine Schwartges, A. Wolff, J. Haunert","doi":"10.1145/2666310.2666494","DOIUrl":"https://doi.org/10.1145/2666310.2666494","url":null,"abstract":"We consider the problem of labeling linear objects (such as streets) in interactive maps where the user can pan, zoom, and rotate continuously. Our labels contain text (such as street names). They are embedded into the objects they label, i.e., they follow the curvature of the objects, they do not move with respect to the map background, but they scale in order to maintain constant size on the screen. To the best of our knowledge, this is the first work that deals with curved labels in interactive maps. Our objective is to label as many streets as possible and to select label positions of high quality while forbidding labels to overlap at street crossings. We present a simple but effective algorithm that takes curvature and crossings into account and produces aesthetical labelings. On average over all interaction types, our implementation reaches interactive frame rates of more than 85 frames per second.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133603807","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}
A. Qamar, Imad Afyouni, Mohamed Abdur Rahman, F. Rehman, D. Hossain, Saleh M. Basalamah, A. Lbath
In this paper, we present a novel idea of a map-based therapy environment for people with Hemiplegia. The therapy environment is designed according to the suggestions of therapists, which consists of a spatial map browsing serious game augmented with our novel multi-sensory natural user interface (NUI). The NUI is based on 3D motion sensors that can recognize different hand and body gestures used for browsing a 3D or 2D map. The 3D motion sensors work in a non-invasive way; hence, they do not require any wearable body attachments and can be used at home without assistance from the therapists. The map-browsing environment provides an immersive experience to the disabled users, which helps in performing therapy in an interesting and entertaining manner. We have developed analytics for measuring certain quality of health improvement metrics from each type of spatial map browsing movements. The 3D motion sensors have been tested with Nokia, Google, ESRI, and a number of other maps that allow a subject to visualize and browse the 3D and 2D maps of the world. The map browsing session data shows the nature of big data; hence, the session data is stored in a cloud environment. Our developed serious game environment is web-based; thus anyone having the appropriate low cost sensor hardware can plug it in and start experiencing a natural way of hands free map browsing. We have deployed our framework in a hospital that treats Hemiplegic patients. Based on the feedback obtained, the developed platform shows a huge potential for use in hospitals that provide physiotherapy services as well as at patients' home as an assistive therapeutic service.
{"title":"A GIS-based serious game interface for therapy monitoring","authors":"A. Qamar, Imad Afyouni, Mohamed Abdur Rahman, F. Rehman, D. Hossain, Saleh M. Basalamah, A. Lbath","doi":"10.1145/2666310.2666376","DOIUrl":"https://doi.org/10.1145/2666310.2666376","url":null,"abstract":"In this paper, we present a novel idea of a map-based therapy environment for people with Hemiplegia. The therapy environment is designed according to the suggestions of therapists, which consists of a spatial map browsing serious game augmented with our novel multi-sensory natural user interface (NUI). The NUI is based on 3D motion sensors that can recognize different hand and body gestures used for browsing a 3D or 2D map. The 3D motion sensors work in a non-invasive way; hence, they do not require any wearable body attachments and can be used at home without assistance from the therapists. The map-browsing environment provides an immersive experience to the disabled users, which helps in performing therapy in an interesting and entertaining manner. We have developed analytics for measuring certain quality of health improvement metrics from each type of spatial map browsing movements. The 3D motion sensors have been tested with Nokia, Google, ESRI, and a number of other maps that allow a subject to visualize and browse the 3D and 2D maps of the world. The map browsing session data shows the nature of big data; hence, the session data is stored in a cloud environment. Our developed serious game environment is web-based; thus anyone having the appropriate low cost sensor hardware can plug it in and start experiencing a natural way of hands free map browsing. We have deployed our framework in a hospital that treats Hemiplegic patients. Based on the feedback obtained, the developed platform shows a huge potential for use in hospitals that provide physiotherapy services as well as at patients' home as an assistive therapeutic service.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115673831","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}
Meiling Liu, Kaiqun Fu, Chang-Tien Lu, Guangsheng Chen, Huiqiang Wang
As a form of social media, Twitter records real life events in our cities as they happen. Huge numbers of tweets under the heading of transportation or metro are published every day. This paper presents an application for Traffic Events Detection and Summary (TEDS) based on mining representative terms from the tweets posted when anomalies occur. The proposed ensemble application contains an efficient TEDS search engine with multiple indexing, ranking, and scoring schemes. Spatio-temporal analysis and a novel wavelet analysis model are applied for traffic event detection. This application could benefit both drivers and transportation authorities. Users can search transportation status and analyze traffic events in specific locations of interest. Utilizing the proposed signal processing technology, we demonstrate the system's effectiveness by examining traffic and metro travel in the Washington D.C. area. As the collaboration between a citizen's life and social media becomes ever greater, this could have a significant impact on the prediction of traffic flow, travel selection, and other city computing functions.
{"title":"A search and summary application for traffic events detection based on Twitter data","authors":"Meiling Liu, Kaiqun Fu, Chang-Tien Lu, Guangsheng Chen, Huiqiang Wang","doi":"10.1145/2666310.2666366","DOIUrl":"https://doi.org/10.1145/2666310.2666366","url":null,"abstract":"As a form of social media, Twitter records real life events in our cities as they happen. Huge numbers of tweets under the heading of transportation or metro are published every day. This paper presents an application for Traffic Events Detection and Summary (TEDS) based on mining representative terms from the tweets posted when anomalies occur. The proposed ensemble application contains an efficient TEDS search engine with multiple indexing, ranking, and scoring schemes. Spatio-temporal analysis and a novel wavelet analysis model are applied for traffic event detection. This application could benefit both drivers and transportation authorities. Users can search transportation status and analyze traffic events in specific locations of interest. Utilizing the proposed signal processing technology, we demonstrate the system's effectiveness by examining traffic and metro travel in the Washington D.C. area. As the collaboration between a citizen's life and social media becomes ever greater, this could have a significant impact on the prediction of traffic flow, travel selection, and other city computing functions.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273036","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 describes a pedestrian population trend estimation method using location data of smartphone users. This technique is intended to be an alternative to traffic censuses using tally counters. Traffic censuses using tally counters are still commonly used to survey the number of pedestrians despite their cost and limitations in area and time. The proposed approach can replace the traffic census by using smartphone users' location data accumulated on Yahoo! Japan. Moreover, it is low cost because it uses location data collaterally acquired from smartphone users, and it has no limits in terms of area or time. This means pedestrian population trends in arbitrary and times about which we want to know can be estimated. The proposed technique is based on the assumption that the number of location data in an area is proportional to the population volume, but it also eliminates some data to increase pedestrian accuracy. In the elimination step, some location data that should not be counted as pedestrians are excluded by estimating transport modes from anteroposterior location data. The supplement step tackles the problem of data shortage when a target area is a small region by using a Gaussian kernel. The Gaussian kernel smoother is also used to deal with data interpolation in the time direction, and it enables us to estimate time-continuous pedestrian volumes in arbitrary areas. To evaluate the approach, a manual traffic survey was conducted in five areas on 11 days and the ground truth data are acquired. Experimental result shows the approach successfully estimate pedestrian population trends in areas. The proposed method makes less than one-tenth the mean squared errors of hourly pedestrian number estimation than the conventional approach.
{"title":"Hourly pedestrian population trends estimation using location data from smartphones dealing with temporal and spatial sparsity","authors":"Kentaro Nishi, K. Tsubouchi, M. Shimosaka","doi":"10.1145/2666310.2666391","DOIUrl":"https://doi.org/10.1145/2666310.2666391","url":null,"abstract":"This paper describes a pedestrian population trend estimation method using location data of smartphone users. This technique is intended to be an alternative to traffic censuses using tally counters. Traffic censuses using tally counters are still commonly used to survey the number of pedestrians despite their cost and limitations in area and time. The proposed approach can replace the traffic census by using smartphone users' location data accumulated on Yahoo! Japan. Moreover, it is low cost because it uses location data collaterally acquired from smartphone users, and it has no limits in terms of area or time. This means pedestrian population trends in arbitrary and times about which we want to know can be estimated. The proposed technique is based on the assumption that the number of location data in an area is proportional to the population volume, but it also eliminates some data to increase pedestrian accuracy. In the elimination step, some location data that should not be counted as pedestrians are excluded by estimating transport modes from anteroposterior location data. The supplement step tackles the problem of data shortage when a target area is a small region by using a Gaussian kernel. The Gaussian kernel smoother is also used to deal with data interpolation in the time direction, and it enables us to estimate time-continuous pedestrian volumes in arbitrary areas. To evaluate the approach, a manual traffic survey was conducted in five areas on 11 days and the ground truth data are acquired. Experimental result shows the approach successfully estimate pedestrian population trends in areas. The proposed method makes less than one-tenth the mean squared errors of hourly pedestrian number estimation than the conventional approach.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129215944","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}
Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the "building blocks of public service society". Despite this, difficulties in data collection combined with the concept's subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names.
{"title":"A data driven approach to mapping urban neighbourhoods","authors":"P. Brindley, James Goulding, Max L. Wilson","doi":"10.1145/2666310.2666473","DOIUrl":"https://doi.org/10.1145/2666310.2666473","url":null,"abstract":"Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the \"building blocks of public service society\". Despite this, difficulties in data collection combined with the concept's subjective nature have left most countries lacking official neighbourhood definitions. This issue has implications not only for policy, but for the field of computational social science as a whole (with many studies being forced to use administrative units as proxies despite the fact that these bear little connection to resident perceptions of social boundaries). In this paper we illustrate that the mass linguistic datasets now available on the internet need only be combined with relatively simple linguistic computational models to produce definitions that are not only probabilistic and dynamic, but do not require a priori knowledge of neighbourhood names.","PeriodicalId":153031,"journal":{"name":"Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128059337","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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