@article{
 title = {An event-based spatiotemporal datamodel (ESTDM) for temporal analysis of geographical data},
 type = {article},
 year = {1995},
 keywords = {data model,events,gis,time},
 volume = {9},
 id = {f2afa372-ecc5-3a54-92f7-c8f99f479621},
 created = {2018-05-29T14:06:56.838Z},
 file_attached = {false},
 profile_id = {6d8d7993-9618-3f6c-983a-9f6761313797},
 group_id = {4f1d95d1-59ee-3ce8-85ce-055cfae2da74},
 last_modified = {2018-05-29T14:06:56.838Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {13468},
 source_type = {article},
 notes = {Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analyzing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation. In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the \textquoterightsnapshot\textquoteright model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.},
 private_publication = {false},
 bibtype = {article},
 author = {Donna, J Peuquet and Duan, Niu},
 journal = {International Journal of Geographical Information Systems}
}

{"_id":"kMt4CseqS7XHubDAW","bibbaseid":"donna-duan-aneventbasedspatiotemporaldatamodelestdmfortemporalanalysisofgeographicaldata-1995","downloads":0,"creationDate":"2018-05-29T14:13:24.329Z","title":"An event-based spatiotemporal datamodel (ESTDM) for temporal analysis of geographical data","author_short":["Donna, J., P.","Duan, N."],"year":1995,"bibtype":"article","biburl":null,"bibdata":{"title":"An event-based spatiotemporal datamodel (ESTDM) for temporal analysis of geographical data","type":"article","year":"1995","keywords":"data model,events,gis,time","volume":"9","id":"f2afa372-ecc5-3a54-92f7-c8f99f479621","created":"2018-05-29T14:06:56.838Z","file_attached":false,"profile_id":"6d8d7993-9618-3f6c-983a-9f6761313797","group_id":"4f1d95d1-59ee-3ce8-85ce-055cfae2da74","last_modified":"2018-05-29T14:06:56.838Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"13468","source_type":"article","notes":"Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analyzing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation. In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the \\textquoterightsnapshot\\textquoteright model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.","private_publication":false,"bibtype":"article","author":"Donna, J Peuquet and Duan, Niu","journal":"International Journal of Geographical Information Systems","bibtex":"@article{\n title = {An event-based spatiotemporal datamodel (ESTDM) for temporal analysis of geographical data},\n type = {article},\n year = {1995},\n keywords = {data model,events,gis,time},\n volume = {9},\n id = {f2afa372-ecc5-3a54-92f7-c8f99f479621},\n created = {2018-05-29T14:06:56.838Z},\n file_attached = {false},\n profile_id = {6d8d7993-9618-3f6c-983a-9f6761313797},\n group_id = {4f1d95d1-59ee-3ce8-85ce-055cfae2da74},\n last_modified = {2018-05-29T14:06:56.838Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {13468},\n source_type = {article},\n notes = {Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analyzing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation. In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the \\textquoterightsnapshot\\textquoteright model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.},\n private_publication = {false},\n bibtype = {article},\n author = {Donna, J Peuquet and Duan, Niu},\n journal = {International Journal of Geographical Information Systems}\n}","author_short":["Donna, J., P.","Duan, N."],"bibbaseid":"donna-duan-aneventbasedspatiotemporaldatamodelestdmfortemporalanalysisofgeographicaldata-1995","role":"author","urls":{},"keyword":["data model","events","gis","time"],"downloads":0},"search_terms":["event","based","spatiotemporal","datamodel","estdm","temporal","analysis","geographical","data","donna","duan"],"keywords":["data model","events","gis","time"],"authorIDs":[]}