Most existing algorithms assume that public transit networks are static. However in reality, a bus may be delayed or canceled, which causes routing algorithms to generate non-optimal journeys. We propose CrowdRoute, an algorithm that exploits real-time information contributed by the crowd, to solve the earliest arrival problem in public transit networks.
{"title":"CrowdRoute: a crowd-sourced routing algorithm in public transit networks","authors":"To Tu Cuong","doi":"10.1145/2534732.2534738","DOIUrl":"https://doi.org/10.1145/2534732.2534738","url":null,"abstract":"Most existing algorithms assume that public transit networks are static. However in reality, a bus may be delayed or canceled, which causes routing algorithms to generate non-optimal journeys. We propose CrowdRoute, an algorithm that exploits real-time information contributed by the crowd, to solve the earliest arrival problem in public transit networks.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"72 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130172385","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, D. Deng, Chun-Chen Hsu, R. Lemmens
OpenStreetMap is an open and collaborative project with thousands of people contributing GPS traces and other data into the making of a global map of places and networks. It is open in the sense that everyone can contribute to the project, and results from the project are free for everyone to reuse. This is contrary to traditional cartography where often a central authority controls the making of the map and its release. Is OpenStreetMap more democratic, and in what sense? Is OpenStreetMap more relevant to the mass, and how can we judge? We define and use several metrics to measure temporal properties of defined areas in OpenStreetMap, and to sample modes of participation in these areas. These metrics are used to graph the datasets representing the current OpenStreetMap so as to reveal unevenness in user participation and data temporality. We use the dataset about Taiwan as a test case to observe participatory and temporal diversities among different areas of Taiwan in OpenStreetMap.
{"title":"The one and many maps: participatory and temporal diversities in OpenStreetMap","authors":"Tyng-Ruey Chuang, D. Deng, Chun-Chen Hsu, R. Lemmens","doi":"10.1145/2534732.2534737","DOIUrl":"https://doi.org/10.1145/2534732.2534737","url":null,"abstract":"OpenStreetMap is an open and collaborative project with thousands of people contributing GPS traces and other data into the making of a global map of places and networks. It is open in the sense that everyone can contribute to the project, and results from the project are free for everyone to reuse. This is contrary to traditional cartography where often a central authority controls the making of the map and its release. Is OpenStreetMap more democratic, and in what sense? Is OpenStreetMap more relevant to the mass, and how can we judge?\u0000 We define and use several metrics to measure temporal properties of defined areas in OpenStreetMap, and to sample modes of participation in these areas. These metrics are used to graph the datasets representing the current OpenStreetMap so as to reveal unevenness in user participation and data temporality. We use the dataset about Taiwan as a test case to observe participatory and temporal diversities among different areas of Taiwan in OpenStreetMap.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525246","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}
The widespread availability of Internet access and location-acquisition technologies, such as the global positioning system (GPS), has given rise to the growing phenomenon of Volunteered Geographic Information (VGI). Our work presents the use of VGI in bathymetry and hydrographic surveying and demonstrates that crowdsourced bathymetry data (CSB) can yield valuable knowledge for the maritime community. In this study, CSB data collected from 2012 to 2013 within the Baltimore Inner Harbor was used to locate anomalous depth measurements that could indicate the presence of submerged debris. To this end, we explored two approaches for detecting spatio-temporal outliers in the CSB data. In the first approach, we combined Local Outlier Factor and DBSCAN in an ensemble method to find spatio-temporal clusters of anomalous measurements that could indicate the presence of submerged debris. In the second approach, we calculated a measure of local spatial autocorrelation over time to identify "hotspots" or specific areas that consistently have low depth measurements compared to their immediate neighbors (i.e. "low-high" outliers). Results from both approaches revealed locations within the Fort McHenry Channel whose depth measurements may be indicative of the presence of submerged marine debris and, as such, may pose a threat to the safety of mariners operating in that region. Our results indicate that CSB data can not only help to improve the safety of mariners, but also serve to alert authorities in a timely manner that channel maintenance, a re-survey, and/or changes to the nautical chart may be needed.
{"title":"Detecting spatio-temporal outliers in crowdsourced bathymetry data","authors":"Leela Sedaghat, J. Hersey, M. P. McGuire","doi":"10.1145/2534732.2534739","DOIUrl":"https://doi.org/10.1145/2534732.2534739","url":null,"abstract":"The widespread availability of Internet access and location-acquisition technologies, such as the global positioning system (GPS), has given rise to the growing phenomenon of Volunteered Geographic Information (VGI). Our work presents the use of VGI in bathymetry and hydrographic surveying and demonstrates that crowdsourced bathymetry data (CSB) can yield valuable knowledge for the maritime community. In this study, CSB data collected from 2012 to 2013 within the Baltimore Inner Harbor was used to locate anomalous depth measurements that could indicate the presence of submerged debris. To this end, we explored two approaches for detecting spatio-temporal outliers in the CSB data. In the first approach, we combined Local Outlier Factor and DBSCAN in an ensemble method to find spatio-temporal clusters of anomalous measurements that could indicate the presence of submerged debris. In the second approach, we calculated a measure of local spatial autocorrelation over time to identify \"hotspots\" or specific areas that consistently have low depth measurements compared to their immediate neighbors (i.e. \"low-high\" outliers). Results from both approaches revealed locations within the Fort McHenry Channel whose depth measurements may be indicative of the presence of submerged marine debris and, as such, may pose a threat to the safety of mariners operating in that region. Our results indicate that CSB data can not only help to improve the safety of mariners, but also serve to alert authorities in a timely manner that channel maintenance, a re-survey, and/or changes to the nautical chart may be needed.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130697060","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}
S. Verstockt, Viktor Slavkovikj, Olivier Janssens, P. D. Potter, Jürgen Slowack, R. Walle
In this paper, we describe a multi-modal bike sensing setup for automatic geo-annotation of terrain types using web-based data enrichment. The proposed road/terrain classification system is mainly based on the analysis of volunteered geographic information gathered by bikers. By using participatory accelerometer and GPS sensor data collected from cyclists' smartphones, which is enriched with data from geographic web services, the proposed system is able to distinguish between 6 different terrain types. For the classification of the web-based enriched sensor data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies every instance of road (over a 5 seconds interval) and maps the results onto the user collected GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types and create more advanced route statistics. The accuracy of the bike sensing system is 92% for 6-class terrain classification and 97% for 2-class on-road/off-road classification.
{"title":"Web-based enrichment of bike sensor data for automatic geo-annotation","authors":"S. Verstockt, Viktor Slavkovikj, Olivier Janssens, P. D. Potter, Jürgen Slowack, R. Walle","doi":"10.1145/2534732.2534744","DOIUrl":"https://doi.org/10.1145/2534732.2534744","url":null,"abstract":"In this paper, we describe a multi-modal bike sensing setup for automatic geo-annotation of terrain types using web-based data enrichment. The proposed road/terrain classification system is mainly based on the analysis of volunteered geographic information gathered by bikers. By using participatory accelerometer and GPS sensor data collected from cyclists' smartphones, which is enriched with data from geographic web services, the proposed system is able to distinguish between 6 different terrain types. For the classification of the web-based enriched sensor data, the system employs a random decision forest (RDF), which compared favorably for the geo-annotation task against different classification algorithms. The system classifies every instance of road (over a 5 seconds interval) and maps the results onto the user collected GPS coordinates. Finally, based on all the collected instances, we can annotate geographic maps with the terrain types and create more advanced route statistics. The accuracy of the bike sensing system is 92% for 6-class terrain classification and 97% for 2-class on-road/off-road classification.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125366054","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}
G. Fuchs, N. Andrienko, G. Andrienko, Sebastian Bothe, Hendrik Stange
Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results confirm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions.
{"title":"Tracing the German centennial flood in the stream of tweets: first lessons learned","authors":"G. Fuchs, N. Andrienko, G. Andrienko, Sebastian Bothe, Hendrik Stange","doi":"10.1145/2534732.2534741","DOIUrl":"https://doi.org/10.1145/2534732.2534741","url":null,"abstract":"Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results confirm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126101712","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}
Disasters, whether natural or man-made, can occur in an unexpected and unanticipated manner causing damage and disruptions. In the event of sudden onset of a hazard, private and public transport users and pedestrians need to be informed and guided to safety. Targeted alerting in early warning systems involves the communication of personalized information to a variety of communities based on their different needs and situations to improve alert usability and compliance. In this paper, we present MoveSafe, a generic and extensible framework for transportation mode-based dynamic partitioning of a population for targeted alerting and for better transport management in hazard occurrence scenarios. We infer the transportation mode of the users dynamically using their location traces through continuous feature extraction and maintenance. In combination with the hazard location, we use the transportation mode information to find clusters of people at potentially different levels of risk and with different information needs. The framework also supports a variety of classification features, classifiers, clustering dimensions, and clustering algorithms. We evaluate its performance in different settings and present the results.
{"title":"MoveSafe: a framework for transportation mode-based targeted alerting in disaster response","authors":"Paras Mehta, S. Müller, A. Voisard","doi":"10.1145/2534732.2534735","DOIUrl":"https://doi.org/10.1145/2534732.2534735","url":null,"abstract":"Disasters, whether natural or man-made, can occur in an unexpected and unanticipated manner causing damage and disruptions. In the event of sudden onset of a hazard, private and public transport users and pedestrians need to be informed and guided to safety. Targeted alerting in early warning systems involves the communication of personalized information to a variety of communities based on their different needs and situations to improve alert usability and compliance. In this paper, we present MoveSafe, a generic and extensible framework for transportation mode-based dynamic partitioning of a population for targeted alerting and for better transport management in hazard occurrence scenarios. We infer the transportation mode of the users dynamically using their location traces through continuous feature extraction and maintenance. In combination with the hazard location, we use the transportation mode information to find clusters of people at potentially different levels of risk and with different information needs. The framework also supports a variety of classification features, classifiers, clustering dimensions, and clustering algorithms. We evaluate its performance in different settings and present the results.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126844820","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}
J. Gelernter, Gautam Ganesh, Hamsini Krishnakumar, Wei Zhang
Geographical knowledge resources or gazetteers that are enriched with local information have the potential to add geographic precision to information retrieval. We have identified sources of novel local gazetteer entries in crowd-sourced OpenStreetMap and Wikimapia geotags that include geo-coordinates. We created a fuzzy match algorithm using machine learning (SVM) that checks both for approximate spelling and approximate geocoding in order to find duplicates between the crowd-sourced tags and the gazetteer in effort to absorb those tags that are novel. For each crowd-sourced tag, our algorithm generates candidate matches from the gazetteer and then ranks those candidates based on word form or geographical relations between each tag and gazetteer candidate. We compared a baseline of edit distance for candidate ranking to an SVM-trained candidate ranking model on a city level location tag match task. Experiment results show that the SVM greatly outperforms the baseline.
{"title":"Automatic gazetteer enrichment with user-geocoded data","authors":"J. Gelernter, Gautam Ganesh, Hamsini Krishnakumar, Wei Zhang","doi":"10.1145/2534732.2534736","DOIUrl":"https://doi.org/10.1145/2534732.2534736","url":null,"abstract":"Geographical knowledge resources or gazetteers that are enriched with local information have the potential to add geographic precision to information retrieval. We have identified sources of novel local gazetteer entries in crowd-sourced OpenStreetMap and Wikimapia geotags that include geo-coordinates. We created a fuzzy match algorithm using machine learning (SVM) that checks both for approximate spelling and approximate geocoding in order to find duplicates between the crowd-sourced tags and the gazetteer in effort to absorb those tags that are novel. For each crowd-sourced tag, our algorithm generates candidate matches from the gazetteer and then ranks those candidates based on word form or geographical relations between each tag and gazetteer candidate. We compared a baseline of edit distance for candidate ranking to an SVM-trained candidate ranking model on a city level location tag match task. Experiment results show that the SVM greatly outperforms the baseline.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123416098","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}
Georgios Skoumas, D. Pfoser, Anastasios Kyrillidis
Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based "maps" visualizing the spatial extent of the various relations.
{"title":"On quantifying qualitative geospatial data: a probabilistic approach","authors":"Georgios Skoumas, D. Pfoser, Anastasios Kyrillidis","doi":"10.1145/2534732.2534742","DOIUrl":"https://doi.org/10.1145/2534732.2534742","url":null,"abstract":"Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships.\u0000 To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based \"maps\" visualizing the spatial extent of the various relations.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122748557","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 the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.
{"title":"Exploratory analysis of OpenStreetMap for land use classification","authors":"J. Estima, M. Painho","doi":"10.1145/2534732.2534734","DOIUrl":"https://doi.org/10.1145/2534732.2534734","url":null,"abstract":"In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas' values are remarkable and promising results that encourages us for future research on this topic.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129651534","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}
Nowadays mobile phones and especially smart phones are common technical communication devices. Most of them are equipped with a huge number of sensors that detect environment and user interaction. The processing of the sensor measurements is a big challenge as the data is very heterogeneous and frequently available. Usage of these low cost sensors and combining them with other data sources is one of the most promising tasks and will speed up the crowd sourced (geo-)data in the future. This vision will only become reality if we are able to establish techniques to integrate different devices and test many varied situations. In this work, we present a framework for storing, fusioning and processing of mobile smartphone sensor data. Further we give an outlook on possible applications and our future work.
{"title":"A mobile sensor data acquisition and evaluation framework for crowd sourcing data","authors":"Nicolas Billen, Johannes Lauer, A. Zipf","doi":"10.1145/2534732.2534740","DOIUrl":"https://doi.org/10.1145/2534732.2534740","url":null,"abstract":"Nowadays mobile phones and especially smart phones are common technical communication devices. Most of them are equipped with a huge number of sensors that detect environment and user interaction. The processing of the sensor measurements is a big challenge as the data is very heterogeneous and frequently available. Usage of these low cost sensors and combining them with other data sources is one of the most promising tasks and will speed up the crowd sourced (geo-)data in the future. This vision will only become reality if we are able to establish techniques to integrate different devices and test many varied situations. In this work, we present a framework for storing, fusioning and processing of mobile smartphone sensor data. Further we give an outlook on possible applications and our future work.","PeriodicalId":314116,"journal":{"name":"GEOCROWD '13","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133264493","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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