Binbin Lu
Biographic Data
| ID | 6010004 |
|---|---|
| NAME | Binbin Lu |
| GIVEN NAMES | Binbin |
| FAMILY NAME | Lu |
| SIGNATURE | LU B |
| AFFILIATIONS | Wuhan University |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Spatially heterogeneous responses of urban vegetation phenology to urban morphology and climate in Chinese cities
Beyond global metrics: A geographically weighted framework for exploring multi-source spatial data validation
Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression
Spatial heterogeneity is important for exploring data relationships between real estate price and its influential factors. The geographically weighted regression (GWR) technique has been frequently adopted for this purpose. In this study, we collected a second-hand real estate house price data set of Wuhan, in which each property is located the same as the community it belongs to. Thus, this data set possesses a typical characteristic, that is, d…
Spatial disparities of self-reported Covid-19 cases and influencing factors in Wuhan, China
Distance metric choice can both reduce and induce collinearity in geographically weighted regression
This paper explores the impact of different distance metrics on collinearity in local regression models such as geographically weighted regression. Using a case study of house price data collected in Hà Nội, Vietnam, and by fully varying both power and rotation parameters to create different Minkowski distances, the analysis shows that local collinearity can be both negatively and positively affected by distance metric choice. The Minkowski dista…
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 present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration pro…
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 local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than…
The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models
In this study, we present a collection of local models, termed geographically weighted \n(GW) models, that can be found within the GWmodel R package. A GW model suits \nsituations when spatial data are poorly described by the global form, and for some \nregions the localised fit provides a better description. The approach uses a moving \nwindow weighting technique, where a collection of local models are estimated at target \nlocations. Commonly, …
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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 local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than…
The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models
In this study, we present a collection of local models, termed geographically weighted \n(GW) models, that can be found within the GWmodel R package. A GW model suits \nsituations when spatial data are poorly described by the global form, and for some \nregions the localised fit provides a better description. The approach uses a moving \nwindow weighting technique, where a collection of local models are estimated at target \nlocations. Commonly, …
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 present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration pro…
Distance metric choice can both reduce and induce collinearity in geographically weighted regression
This paper explores the impact of different distance metrics on collinearity in local regression models such as geographically weighted regression. Using a case study of house price data collected in Hà Nội, Vietnam, and by fully varying both power and rotation parameters to create different Minkowski distances, the analysis shows that local collinearity can be both negatively and positively affected by distance metric choice. The Minkowski dista…
Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression
Spatial heterogeneity is important for exploring data relationships between real estate price and its influential factors. The geographically weighted regression (GWR) technique has been frequently adopted for this purpose. In this study, we collected a second-hand real estate house price data set of Wuhan, in which each property is located the same as the community it belongs to. Thus, this data set possesses a typical characteristic, that is, d…
Spatial disparities of self-reported Covid-19 cases and influencing factors in Wuhan, China
Spatially heterogeneous responses of urban vegetation phenology to urban morphology and climate in Chinese cities
Beyond global metrics: A geographically weighted framework for exploring multi-source spatial data validation
Mathematics (6 works) · Spatial and Panel Data Analysis (6 works) · Statistics (6 works) · Computer Science (5 works) · Econometrics (4 works) · Economic and Environmental Valuation (4 works) · Geographically Weighted Regression (4 works) · Spatial heterogeneity (4 works) · Data mining (3 works) · Geography (2 works)