Emre Tepe
Dados Biográficos
| ID | 90015 |
|---|---|
| NOME | Emre Tepe |
| PRENOMES | Emre |
| SOBRENOME | Tepe |
| ASSINATURA | TEPE E |
| AFILIAÇÕES | University of Florida |
| ORCID | 0000-0001-8575-2401 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 7 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 7 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2020 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Enhancing transparency in land use change modeling
Estimating the impacts of climate change risk perception on local housing market
History, neighborhood, and proximity as factors of land-use change
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 …
Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
Machine learning application to spatio-temporal modeling of urban growth
Identification of determinants during the registration process of industrial heritage using a regression analysis
Spatio-temporal multinomial autologistic modeling of land-use change
Land-use change models that accurately replicate the complex dynamics of land development provide vital information for urban planning and policy. These models require both detailed data and advanced statistical methods. Many factors influence land-use change decisions, such as parcel characteristics, accessibility to activities, and current and historical neighborhood conditions. Therefore, spatial and temporal components must be incorporated in…
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Spatio-temporal multinomial autologistic modeling of land-use change
Land-use change models that accurately replicate the complex dynamics of land development provide vital information for urban planning and policy. These models require both detailed data and advanced statistical methods. Many factors influence land-use change decisions, such as parcel characteristics, accessibility to activities, and current and historical neighborhood conditions. Therefore, spatial and temporal components must be incorporated in…
Machine learning application to spatio-temporal modeling of urban growth
Identification of determinants during the registration process of industrial heritage using a regression analysis
Spatio-temporal modeling of parcel-level land-use changes using machine learning methods
History, neighborhood, and proximity as factors of land-use change
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 …
Enhancing transparency in land use change modeling
Estimating the impacts of climate change risk perception on local housing market
Computer Science (6 obras) · Mathematics (5 obras) · Land Use and Ecosystem Services (4 obras) · Spatial and Panel Data Analysis (4 obras) · Statistics (4 obras) · Data mining (3 obras) · Econometrics (3 obras) · Geography (3 obras) · Housing Market and Economics (3 obras) · Land use (3 obras)