Principal component analysis on spatial data: an overview
This article considers critically how one of the oldest and most widely applied statistical
methods, principal components analysis (PCA), is employed with spatial data. We first …
methods, principal components analysis (PCA), is employed with spatial data. We first …
Real estate price estimation in French cities using geocoding and machine learning
D Tchuente, S Nyawa - Annals of operations research, 2022 - Springer
This paper reviews real estate price estimation in France, a market that has received little
attention. We compare seven popular machine learning techniques by proposing a different …
attention. We compare seven popular machine learning techniques by proposing a different …
Geographical and temporal weighted regression (GTWR)
Both space and time are fundamental in human activities as well as in various physical
processes. Spatiotemporal analysis and modeling has long been a major concern of …
processes. Spatiotemporal analysis and modeling has long been a major concern of …
GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models
Spatial statistics is a growing discipline providing important analytical techniques in a wide
range of disciplines in the natural and social sciences. In the R package GWmodel, we …
range of disciplines in the natural and social sciences. In the R package GWmodel, we …
Monitoring housing rental prices based on social media: An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing …
National land use policies and strategies worldwide have attempted to establish a healthy
housing rental market towards urban sustainability. Monitoring fine-scale housing rental …
housing rental market towards urban sustainability. Monitoring fine-scale housing rental …
Geographically weighted regression
AS Fotheringham, C Brunsdon… - The Sage handbook of …, 2009 - torrossa.com
Spatial data contain locational information as well as attribute information. It is increasingly
recognized that most data sets are spatial in that the attribute being measured is typically …
recognized that most data sets are spatial in that the attribute being measured is typically …
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data
Geographically weighted regression (GWR) is an important local technique for exploring
spatial heterogeneity in data relationships. In fitting with Tobler's first law of geography, each …
spatial heterogeneity in data relationships. In fitting with Tobler's first law of geography, each …
A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in …
S Sisman, AC Aydinoglu - Land use policy, 2022 - Elsevier
Determining real estate market dynamics has become an important issue in the city
economy for achieving sustainable urban land management and investment planning. This …
economy for achieving sustainable urban land management and investment planning. This …
A geographically weighted artificial neural network
J Hagenauer, M Helbich - International Journal of Geographical …, 2022 - Taylor & Francis
While recent developments have extended geographically weighted regression (GWR) in
many directions, it is usually assumed that the relationships between the dependent and the …
many directions, it is usually assumed that the relationships between the dependent and the …
Risky development: Increasing exposure to natural hazards in the United States
Losses from natural hazards are escalating dramatically, with more properties and critical
infrastructure affected each year. Although the magnitude, intensity, and/or frequency of …
infrastructure affected each year. Although the magnitude, intensity, and/or frequency of …