Spatial and temporal modeling of parcel-level land dynamics

Tepe, Emre; Guldmann, Jean‐Michel

doi:10.1016/j.compenvurbsys.2017.02.005

Cited by 13 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bhat et al (2015), Deng andSrinivasan (2016), andHu andLo (2007) use spatial lag components to control for spatial dependency. Similarly, temporal lagged components are used by Bhat et al (2015), Huang et al (2009), and Tepe and Guldmann (2017. Alternatively, Carrión-Flores & Irwin (2004) specify a simultaneous autoregressive process in the error terms of their residential land conversion model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The literature review on LUC modeling suggests that: (1) LUC results from the complex interactions between human activities and the built environment (Verburg et al, 2004); (2) Land-use models mimic complex land development dynamics (Noszczyk, 2019); (3) Incorporating spatial dependencies in LUC models is necessary to account for feedback mechanisms among neighbors (Munroe and Müller, 2007); (4) Historical trends are also essential in LUC models because certain historical conditions impact current and future land developments (Kim et al, 2022; Tepe and Guldmann, 2017, 2020; Tepe and Safikhani, 2023). The focus of this review is on recent statistical-based LUC models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Recent studies have also incorporated spatial and temporal dependencies because neighborhood and historical effects play critical roles in land dynamics (Deng and Srinivasan, 2016). Tepe and Guldmann (2017, 2020) introduce an autologistic regression methods explicitly accounting for spatial and temporal dependencies. Including these lags resolves the omitted variable bias problem in OLS regression-based LUC models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Tepe

2023

Environment and Planning B: Urban Analytics and City Science

Self Cite

View full text Add to dashboard Cite

Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Tepe

2023

Environment and Planning B: Urban Analytics and City Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Campanella (2006, p. 142) notes that our system of transferring and owning property has created a means of organizing space that is virtually indestructible. Campanella asserts “even if a city is turned into nuclear ash, property lines can be recreated if the legal documents still exist.” Other studies indicate that plots or lots, as territories of ownership, affect building form and the tendency toward change, and can therefore be significant regulating mechanisms on both neighborhood form and inertia even under great stress (Moudon 1986; Tepe and Guldmann 2017; Sorensen 2018; Sharifi 2019). For example, small lot sizes often influence the footprint of buildings and their position relative to the street, and lot size also affects rebuilding within the urban fabric.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Dynamics and Long‐Term Change

Hallowell

Baran

2020

Geographical Analysis

View full text Add to dashboard Cite

Patterns of change in neighborhoods can be discordantly different, even within the same city district. A little understood factor in how urban neighborhoods form and grow is structural inertia, which is the tendency of an urban area to resist change due to its existing physical and socio-economic fabric. This study explores how patterns of buildings, plots, blocks, and streets affect change or inertia in neighborhoods over time. We integrate Conzenian morphology and space syntax approaches within a geographic information system (GIS) framework to study two historic neighborhoods in Charlotte and Raleigh, N.C. at four points in time over a 96-year span. Aerial images, historic maps, and GIS sources help to create spatial configuration and building data for each time period. We then analyze these data to identify statistical and map-pattern morphological and syntactic relationships both in the aggregate and in detail. Our research finds that most of the independent variables of block size, plot size, building footprint, global integration, local integration, and connectivity predicted long-term change measured in building inventory in almost every occurrence. Our study also suggests design implications and possible future tools and research for measuring change and its relation to the physical characteristics of our cities.

show abstract

“…Previous studies suggest that neighborhood conditions have great impacts on land use changes, especially for farmlands, since neighboring land parcels might have similar properties [31][32][33]. To estimate these regional land cover effects, we calculated the percentage of arable land area within a 500 m radius and the percentage of forest area within a 500 m radius in the land use map.…”

Section: Explanatory Variablesmentioning

confidence: 99%

Exploring the Patterns and Mechanisms of Reclaimed Arable Land Utilization under the Requisition-Compensation Balance Policy in Wenzhou, China

et al. 2017

View full text Add to dashboard Cite

Arable land in China is undergoing significant changes, with massive losses of arable land due to rapid urbanization and the reclamation of arable land from other lands to compensate for these losses. Many studies have analyzed arable land loss, but less attention has been paid to land reclamation, and the utilization of reclaimed land remains unclear. The goal of our study was to characterize the patterns and efficiency of the utilization of reclaimed land and to identify the factors influencing the land utilization process in Wenzhou using remote sensing, geographic information systems and logistic regression. Our results showed that only 37% of the total reclaimed land area was under cultivation, and other lands were still bare or had been covered by trees and grasses. The likelihood that reclaimed land was used for cultivation was highly correlated with the land use type of its neighboring or adjacent parcels. Reclaimed land utilization was also limited at high elevations in lands with poor soil fertility and in lands at a great distance from rural residential areas. In addition, parcels located in the ecological protection zone were less likely to be cultivated. Therefore, we suggest that the important determinants should be considered when identifying the most suitable land reclamation areas.

show abstract

Spatial and temporal modeling of parcel-level land dynamics

Cited by 13 publications

References 22 publications

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

History, neighborhood, and proximity as factors of land-use change: A dynamic spatial regression model

Neighborhood Dynamics and Long‐Term Change

Exploring the Patterns and Mechanisms of Reclaimed Arable Land Utilization under the Requisition-Compensation Balance Policy in Wenzhou, China

Contact Info

Product

Resources

About