Temporal GIS

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Visualization of Temporal Data

Temporal Geographic Information System (GIS) is an emerging capability in GIS for integrating temporal data with location and attribute data. Temporal data specifically refers to times or dates, enabling temporal visualization and ultimately temporal analysis. Temporal data may refer to discrete events, such as lightning strikes; moving objects, such as trains; or repeated observations, such as counts from traffic sensors.[1] Temporal changes occur at different instants or periods of time and are recognized by changes in spatial properties and/or locations. A temporal GIS process, manages, and analyzes spatio-temporal data; [2] spatial data that changes across time and is part of the geographic movement.

Trends in Temporal GIS Modeling

evolution in temporal modeling

The study of modeling temporal information in GIS started in the mid-1980s.[3] The field of computer science's development of temporal data models strongly influenced temporal modeling in the GIS field. Temporal modeling in computer sciences began with the integration of time with relational databases and later extended into object oriented modeling.[4] GIS followed this trend by extending the practice of time stamping layers to include time stamping events and processes within the layers.

Applications of Temporal GIS


Temporal GIS can be used to assess the management and prevention of infectious diseases and other epidemiological phenomena. The study of epidemiology led to one of the early uses of cartographic data to analyze disease outbreak and distribution. In 1854, John Snow studied an outbreak of cholera in London and plotted the locations of individual cases on a map of the city. By examining spatial concentrations of data, he was able to trace the source of the disease to a contaminated water pump.[5]

A more recent study found that spatio-temporal patterns of violent injury in an urban setting were revealed by GIS analysis of ambulance data. During the day, locations of injury and locations of residences are similar. However, later at night, the injury location of highest density shifted to a "nightlife" district, whereas the residence locations of those most at risk of injury did not change.[6]

Spatio-temporal modeling in a GIS has also been used to study the outbreak of "Sudden Oak Death" in California, an infectious plant disease thought to be caused by a non-native, invasive pathogen, Phytophthora ramorum.[7]

GIS, remote sensing and global positioning systems (GPS) have enabled the development of spatio-temporal models that can characterize the distribution patterns of infectious disease epidemics, identify the mechanisms of diffusion and evaluate the effectiveness of disease containment and control techniques. Moreover, these same tools have been used to study the occurrence of non-infectious diseases such as cancer and sudden infant death syndrome (SIDS),[8] and they can also be used to study the implications of biological terror attacks to help public health agencies and emergency response organizations develop and employ effective countermeasures.

Disaster management

Temporal GIS is an important tool for disaster management and mitigation and the development of recovery strategies. Analysis of spatio-temporal data can be a useful technique for the prediction of the aftereffects of natural disaster, such as flooding, fires, structural damage and disease outbreak. By evaluating historical and observational data, patterns can emerge that may help to evaluate the risk of events associated with natural disasters.

For the most part natural disasters cannot be predicted, but often the associated effects of those disasters can be assessed in terms of space and time. Following the 1906 earthquake in San Francisco, it has been estimated that 90% of the damage in the city was caused by subsequent fires and not by structural damage from the quake itself.[9] Today, spatial-temporal GIS systems have been proposed and implemented to improve the efficiency of disaster recovery operations, and provide predictive models to evaluate the possible implications of disaster events.[10][11]

Some disasters are predictable. Periodic monitoring of weather data from spatially distributed observation stations can reveal patterns that enable the development of predictive spatio-temporal models that can be used to estimate the extent, intensity and time of occurrence of storms, floods, drought and wildfires. These models can then be used to develop emergency response plans and allocate appropriate resources.[12]

Environmental science

Environmental science is one of the most important scientific disciplines that can derive benefit from temporal GIS. Wide-ranging applications of this technology may include hazard assessment of localized chemical spills, characterization of the regional resource use, or the evaluation of the global effects of climate change; spatio-temporal analysis has the potential to advance the understanding of environmental trends and the impact of change. Temporal GIS has been used to study:


Temporal GIS can be an important tool within transportation planning. Public transportation planning is an example where a temporal GIS can be used to model the changes in a public transit network throughout different time periods. In most cases public transit routes change depending on the time of day and day of the week. In a temporal GIS this data can be stored and analyzed.

See also


  1. http://www.esri.com/news/arcnews/winter0910articles/visualizing-time.html
  2. M. Yuan,Temporal GIS and Spatio-Temporal Modeling , Published on CD-ROM by the National Center for Geographical Information and Analysis, 1996.
  3. Siabato, W., Claramunt, C., Ilarri, S. & Manso-Callejo, M. Á. (2018) "A Survey of Modelling Trends in Temporal GIS" ACM Computing Surveys 51 (2):30 doi:10.1145/3141772 [1]
  4. Nadi, S. Delavar, M.R. (2003) "Spatio-Temporal Modeling of Dynamic Phenomena in GIS" ScanGIS 2003 Proceeding, pp. 215-225
  5. "John Snow's Cholera Map". York University. http://www.york.ac.uk/depts/maths/histstat/snow_map.htm. Retrieved 2007-06-09. 
  6. Patterns of Urban Violent Injury: A Spatio-Temporal Analysis. PLoS ONE online journal of scientific research, 13 January 2010.
  7. Hunter, Richard D.; Meentemeyer, Ross K.; Rizzo, David M.; Gilligan, Christopher A. Predicting the Spread of Sudden Oak Death in California: Spatial-Temporal Modeling of Susceptible-Infectious Transitions. Treesearch, USFS Research Publications. 2008.
  8. JIA Zhong-wei, LI Xiao-wen, WANG Wei, CHENG Shi-ming. Promising of spatial-temporal model in public health. Chinese Medical Journal, 2009, Vol. 122, No. 3:349-350. Accessed 11 August 2010.
  9. Wikipedia contributors, 1906 San Francisco Earthquake. Wikipedia, The Free Encyclopedia. Accessed 12 August 2010.
  10. Application of Spatial Temporal GIS for Earthquake Disaster Recovery Service. Disaster Recovery Hyperbase Web site. Accessed 12 August 2010.
  11. Zhao, S.J.; Xiong, L.Y.; Ren, A.Z. A Spatial–Temporal Stochastic Simulation of Fire Outbreaks Following Earthquake Based on GIS. Journal of Fire Sciences, vol. 24 no. 4 p.313-339, July 2006.
  12. Web-enabled GIS in Disaster Management. In GIM International, the global magazine for Geomatics. 5 December 2005.
  13. Multitemporal monitoring of water resources degradation at Al-Azraq Oasis, Jordan, using Remote Sensing and GIS techniques.International Journal of Global Warming 2010 - Vol. 2, No.1 pp. 1 - 16
  14. Sanja Veleva, Kosta Mitreski; Data Analysis of Spatio-Temporal Sensor Data as a Contribution to the Model Analysis for Water Resources. BALWOIS Project: Balkan Water Observation and Information System for Balkan countries, May 2010.
  15. Heng Ma, Tsueng-Fang Tsai, Chia-Cheng Liu; Real-time monitoring of water quality using temporal trajectory of live fish. Expert Systems with Applications: An International Journal, Volume 37, Issue 7 (July 2010), p.5158-5171
  16. Madan Sigdel, M. Ikeda; Spatial and temporal analysis of drought and summer precipitation in Nepal under climate change. IOP Conference Series: Earth and Environmental Science
  17. Gerald A Corzo P, Marjolein H.J. van Huijgevoort, Henny A.J. van Lanen; Methodology for Spatial and Temporal Analysis of Drought using Large-scale Gridded Data. Geophysical Research Abstracts. Vol. 12, EGU2010-12703-1, 2010
  18. S. Kawagoe, S. Kazama, P. R. Sarukkalige; Probabilistic Modelling of Rainfall Induced Landslide Hazard Assessment Hydrology and Earth System Sciences, 14, 1047–1061, 2010.
  19. Nazzareno Diodatoa, Michele Ceccarelli, Gianni Bellocchi.GIS-aided evaluation of evapotranspiration at multiple spatial and temporal climate patterns using geoindicators. Ecological Indicators. Volume 10, Issue 5, September 2010, Pages 1009-1016
  20. Cui, Guishan; Lee, Woo-Kyun; Lee, Sangchul; Chung, Jiwoong. Vulnerability Assessment of Water Resources to Climate Change Using GIS. 2010 Esri User Conference, San Diego, CA.
  21. Mahmoud Reza Delavara and Nasser Najibi; Monitoring GIS Analysis and Simulations of Natural and Anthropogenic Digital Terrain Change Impacts on Water and Sediment Transport in the Agricultural Farms. ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of Climate Change on Agriculture