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Spatial and temporal modeling of parcel-level land dynamics
AbstractNeighborhood and historical conditions are important factors in land dynamics. However, models that explicitly incorporate spatial and temporal dependencies face challenges in data availability, methodology and computation. In this research, parcel-level dynamics are investigated using the geocoded Auditor's tax database for Delaware County, Ohio, including 73,560 parcels over the period 1990–2012. A binary spatio-temporal autologistic model (STARM), incorporating space and time and their interactions, is used to investigate parcel-level dynamics. The results show that the model is able capture the impacts of contemporaneous and historical neighborhood conditions around parcels, as well as the effects of other variables such as distances to various facilities and infrastructures, agricultural and residential land-use shares within a half mile radius circle, and population density and growth expectation at the census tract level.
HighlightsThe primary data are on 73,560 parcels over 1990–2012 for Delaware County, Ohio.The available construction-year information on each parcel is used to derive measures of land-use dynamics.Both contemporaneous and historical dependencies are incorporated in a spatio-temporal autologistic model.The model is estimated with sparse matrix, parallel processing, and Monte Carlo simulation techniques.The model is validated over the period 2005–2012, with a 97.2% accuracy rate.
Spatial and temporal modeling of parcel-level land dynamics
AbstractNeighborhood and historical conditions are important factors in land dynamics. However, models that explicitly incorporate spatial and temporal dependencies face challenges in data availability, methodology and computation. In this research, parcel-level dynamics are investigated using the geocoded Auditor's tax database for Delaware County, Ohio, including 73,560 parcels over the period 1990–2012. A binary spatio-temporal autologistic model (STARM), incorporating space and time and their interactions, is used to investigate parcel-level dynamics. The results show that the model is able capture the impacts of contemporaneous and historical neighborhood conditions around parcels, as well as the effects of other variables such as distances to various facilities and infrastructures, agricultural and residential land-use shares within a half mile radius circle, and population density and growth expectation at the census tract level.
HighlightsThe primary data are on 73,560 parcels over 1990–2012 for Delaware County, Ohio.The available construction-year information on each parcel is used to derive measures of land-use dynamics.Both contemporaneous and historical dependencies are incorporated in a spatio-temporal autologistic model.The model is estimated with sparse matrix, parallel processing, and Monte Carlo simulation techniques.The model is validated over the period 2005–2012, with a 97.2% accuracy rate.
Spatial and temporal modeling of parcel-level land dynamics
Tepe, Emre (author) / Guldmann, Jean-Michel (author)
Computers, Environments and Urban Systems ; 64 ; 204-214
2017-02-25
11 pages
Article (Journal)
Electronic Resource
English
Spatial and temporal modeling of parcel-level land dynamics
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