Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data

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 distance that maximised collinearity in a geographically weighted regression was approximate to a Manhattan distance with (power = 0.70) with a rotation of 30°, and that which minimised collinearity was parameterised with power = 0.05 and a rotation of 70°. The results indicate that distance metric choice can provide a useful extra tuning component to address local collinearity issues in spatially varying coefficient modelling and that understanding the interaction of distance metric and collinearity can provide insight into the nature and structure of the data relationships. The discussion considers first, the exploration and selection of different distance metrics to minimise collinearity as an alternative to localised ridge regression, lasso and elastic net approaches. Second, it discusses the how distance metric choice could extend the methods that additionally optimise local model fit (lasso and elastic net) by selecting a distance metric that further helped minimise local collinearity. Third, it identifies the need to investigate the relationship between kernel bandwidth, distance metrics and collinearity as an area of further work.

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Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210917 ◽

2021 ◽

pp. 1-20

Author(s):

Chaojie Liu ◽

Jie Lu ◽

Wenjing Fu ◽

Zhuoyi Zhou

Keyword(s):

Real Estate ◽

Geographically Weighted Regression ◽

Euclidean Distance ◽

Evaluation Model ◽

Housing Prices ◽

Information Criterion ◽

Access Point ◽

Least Square ◽

Weighted Regression ◽

Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

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Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1521523 ◽

2019 ◽

Vol 33 (1) ◽

pp. 155-175 ◽

Cited By ~ 17

Author(s):

Li ◽

Fotheringham ◽

Li ◽

Oshan

Keyword(s):

Los Angeles ◽

Open Source ◽

Geographically Weighted Regression ◽

Message Passing Interface ◽

House Price ◽

Weighted Regression ◽

Spatial Process ◽

Parameter Estimates ◽

Single Family ◽

Scalable Algorithm

Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions – up to thousands of times faster – on a standard desktop.

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Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Land ◽

10.3390/land9120500 ◽

2020 ◽

Vol 9 (12) ◽

pp. 500

Author(s):

Chengjie Yang ◽

Ruren Li ◽

Zongyao Sha

Keyword(s):

Urban Planning ◽

Geographically Weighted Regression ◽

Driving Forces ◽

Vital Role ◽

Google Earth ◽

Weighted Regression ◽

Landscape Indices ◽

Aggregation Index ◽

Terrain Elevation

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

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Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100431 ◽

2019 ◽

Vol 8 (10) ◽

pp. 431 ◽

Cited By ~ 2

Author(s):

Shiwei Zhang ◽

Lin Wang ◽

Feng Lu

Keyword(s):

Real Estate ◽

Spatial Variability ◽

Geographically Weighted Regression ◽

Spatial Differentiation ◽

Scientific Basis ◽

Weighted Regression ◽

Utility Value ◽

Real Estate Management ◽

Group Pattern

In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.

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