scholarly journals Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 380
Author(s):  
Radosław Cellmer ◽  
Aneta Cichulska ◽  
Mirosław Bełej
Keyword(s):  
Spatial Heterogeneity ◽  
Housing Market ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Housing Prices ◽  
Spatial Diversity ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Market Activity ◽  
Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

scholarly journals Space-Time Analysis of Migrations, Employment, and Housing as A Basis for Municipal Sustainable Urban Planning

2020 ◽  
Vol 12 (6) ◽  
pp. 2473
Author(s):  
Patricia Abelairas-Etxebarria ◽  
Inma Astorkiza
Keyword(s):  
Land Use ◽  
Housing Market ◽  
Spatial Data ◽  
Local Level ◽  
Housing Prices ◽  
Space Time ◽  
Sustainable Land Use ◽  
Spatial Data Analysis ◽  
Spatial Homogeneity ◽  
Land Use Policies

A close relationship exists between population, the housing market and the level of employment at the local level. On the one hand, the housing market is influenced by local planning decisions and, on the other hand, that market is a significant factor in population and economic dynamization. Although there are studies on these variables, it is not common to include their spatial perspective by introducing Geographic Information System (GIS) tools in the analysis. The aim of this study is to analyse space-time associations among the variables migrations, housing prices, and employment prior to and during the economic crisis, in order to adapt sustainable land use policies to be used by land use planning authorities. Bivariate Exploratory Spatial Data Analysis (bivariate ESDA) has been used for this purpose. As our main results demonstrate, spatial positive autocorrelation was found between the variables employment in a village before the crisis and housing prices in neighbouring municipalities during it, indicating that people move to live in areas close to their workplace, but not necessarily to the same municipality. The analysis also shows spatial homogeneity of the variable housing prices, accompanied by temporal stability. The results indicate the need to implement sustainable control land use policies, not at the municipality level but at the county level.

scholarly journals Geographically Weighted Regression Analysis for Spatial Economics Data: A Bayesian Recourse

2020 ◽  
pp. 016001762095982 ◽  
Author(s):  
Zhihua Ma ◽  
Yishu Xue ◽  
Guanyu Hu
Keyword(s):  
Variable Selection ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Marginal Likelihood ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Bayesian Variable Selection ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Model Assessment

The geographically weighted regression (GWR) is a well-known statistical approach to explore spatial non-stationarity of the regression relationship in spatial data analysis. In this paper, we discuss a Bayesian recourse of GWR. Bayesian variable selection based on spike-and-slab prior, bandwidth selection based on range prior, and model assessment using a modified deviance information criterion and a modified logarithm of pseudo-marginal likelihood are fully discussed in this paper. Usage of the graph distance in modeling areal data is also introduced. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods with both small and large number of location scenarios, and comparison with the classical frequentist GWR is made. The performance of variable selection and estimation of the proposed methodology under different circumstances are satisfactory. We further apply the proposed methodology in analysis of a province-level macroeconomic data of thirty selected provinces in China. The estimation and variable selection results reveal insights about China’s economy that are convincing and agree with previous studies and facts.

Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model

10.1068/a3768 ◽  
2006 ◽  
Vol 38 (3) ◽  
pp. 587-598 ◽  
Author(s):  
Chang-Lin Mei ◽  
Ning Wang ◽  
Wen-Xiu Zhang
Keyword(s):  
Regression Model ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Statistical Modelling ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
P Value ◽  
Bootstrap Procedure ◽  
Explanatory Variables ◽  
Geographically Weighted Regression Model

A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory variables in the constant-coefficient part on the response of the model. This is useful in the selection of the variables and for the purpose of explanation. In this paper, a statistical inference framework for this issue is suggested and, besides the F-approximation, which has been frequently used in the literature of the geographically weighted regression technique, a bootstrap procedure for deriving the p-value of the test is also suggested. The performance of the test is investigated by extensive simulations. It is demonstrated that both the F-approximation and the bootstrap procedure work satisfactorily.

scholarly journals ROBUST GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN METODE MUTLAK SIMPANGAN TERKECIL PADA PEMODELAN KEJADIAN DIARE DI KOTA SEMARANG

2018 ◽  
Vol 7 (2) ◽  
pp. 143-152
Author(s):  
Ika Chandra Nurhayati ◽  
Agus Rusgiyono ◽  
Hasbi Yasin
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Least Square ◽  
Weighted Regression ◽  
Distribution Data ◽  
Health Issues ◽  
Least Absolute Deviation ◽  
Absolute Deviation ◽  
The People

Diarrhea is one of many health issues in developing country like Indonesia, because the sickness and the death number are still high. According to health profile of Semarang City, the people who suffer from diarrhea from 2010-2015 are decreasing. The lowest point happened at the year 2013 with the total case of 38.001, however there are an increasing number from 2014-2015. The distribution data of diarrhea is a spatial data. The differences between environment and sanitation could cause spatial heterogeneity. The spatial heterogeneity could cause the produced variant value no longer constant, but instead it is different on each region. Therefore, regression model that involves the effects of spatial heterogeneity is needed, which are Geographically Weighted Regression (GWR) that is built by Weighted Least Square (WLS) adjuster. Although, GWR parameter adjuster that used WLS is very sensitive with the existence of outliers. The existence of the outlier in the data will create a huge residual. Thus, more robust method is needed, which is Least Absolute Deviation (LAD) methods in order to estimate the parameter on model GWR. This model is called Robust GWR (RGWR). The result shows that the model events of diarrhea on each region in Semarang City are different. Furthermore, the model events of diarrhea with RGWR model generate MAPE 16,3396% which means the performance of RGWR is formed well. Keyword: Diarrhea, Robust, Geographically Weighted Regression, Least Absolute Deviation

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

scholarly journals Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3232
Author(s):  
Feili Wei ◽  
Shuang Li ◽  
Ze Liang ◽  
Aiqiong Huang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Spatial Heterogeneity ◽  
Foreign Investment ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Concentration Change ◽  
Regional Effects ◽  
Factors Influencing ◽  
The Sichuan Basin ◽  
Pm2.5 Concentration

Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.

scholarly journals A longitudinal study of e-commerce diversity in Europe

2021 ◽  
Author(s):  
Adam Sadowski ◽  
Karolina Lewandowska-Gwarda ◽  
Renata Pisarek-Bartoszewska ◽  
Per Engelseth
Keyword(s):  
Spatial Data ◽  
Online Shopping ◽  
Statistical Data ◽  
Spatial Diversity ◽  
Regional Level ◽  
Spatial Data Analysis ◽  
Household Preferences ◽  
Global And Local ◽  
Data Analysis Methods ◽  
Local Spatial Autocorrelation

AbstractOwing to increased access to the Internet and the development of electronic commerce, e-commerce has become a common method of shopping in all countries. The purpose of this study is more precisely to research e-commerce diversity in Europe at the regional level and develop the conception of “E-commerce Supply Chain Management”. Statistical data derived from the European Statistical Office were applied to analyse the spatial diversity of e-retailing. Assessments of the regional diversity of e-retailing applied geographic information systems and exploratory spatial data analysis methods such us global and local spatial autocorrelation statistics. Clusters of regions with similar household preferences related to online shopping were identified. A spatial visualisation of the e-retailing diversity phenomenon may be utilised for the reconfiguration of supply chains and to adapt them to actual household preferences related to shopping methods.

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

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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