Spatial Heterogeneity of Carbon Emissions and Low-Carbon Optimization for Small Towns Based on the GWR Model

Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborhood features is also detected at town level. GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market, which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present. The results provide references for local planning departments, helping to reveal the complicated relationship and spatial patterns between housing price and determinants over space.

ABSTRACTGeographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is generally used to calibrate a standard GWR model. However, variations in spatial relationships within a GWR model might also vary in intensity with respect to location and direction. This assertion has led to extensions of the standard GWR model to mixed (or semiparametric) GWR and to flexible bandwidth GWR models. In this article, we present a strongly related extension in fitting a GWR model with parameter-specific distance metrics (PSDM GWR). As with mixed and flexible bandwidth GWR models, a back-fitting algorithm is used for the calibration of the PSDM GWR model. The value of this new GWR model is demonstrated using a London house price data set as a case study. The results indicate that the PSDM GWR model can clearly improve the model calibration in terms of both goodness of fit and prediction accuracy, in contrast to the model fits when only one metric is singly used. Moreover, the PSDM GWR model provides added value in understanding how a regression model’s relationships may vary at different spatial scales, according to the bandwidths and distance metrics selected. PSDM GWR deals with spatial heterogeneities in data relationships in a general way, although questions remain on its model diagnostics, distance metric specification, and computational efficiency, providing options for further research.

This paper uses data from Chinese prefecture-level administrative unit to examine the extent of spatial variability of the impact that population, income, and climate have on urban residential carbon emissions. The residuals of OLS estimation of urban residential carbon emissions exhibit a significant spatial association according to the value of the Moran's I statistic. GWR model effectively reduces the spatial autocorrelation of residuals by considering spatial effect. Not only does it enhance the explanatory power of the model, but also gets local estimates of the parameters. Results show that, there is strong evidence of spatial heterogeneity for impacts of three independent variables: (1) local regression coefficients of population and income are both positive in the OLS and GWR models, but spatial variability of the effect of income is greater in the GWR model; (2) the coefficient estimate of the climate variable in the OLS model is negative, however, the direction is both positive and negative in the GWR model with the magnitude of the effect varying within and across the 302 prefecture-level administrative units in China; (3) one should carefully check the reasonableness of policy recommendations made based on global linear regression models that ignore or failed to properly assess the spatial dependence.

The paper examines the influence of various factors on carbon dioxide emissions from road transport. A GWR model has been developed that takes into account spatial heterogeneity and the impact of development on carbon dioxide emissions when driving cars. The research was con-ducted on the basis of Jinan city, the capital of Shandong Province. Five districts have been se-lected, which form the core of Jinan City. The study showed that the GWR model has a high degree of compliance and there is no multiple collinearity in it.

Purpose - This study examines and documents spatial heterogeneity in Istanbul housing market using Geographically Weighted Model (GWR). Methodology - A GWR model with a Gaussian kernel and an adaptive bandwidth based on cross-validation is employed on a cross-sectional housing listing data set. Additional analysis is provided using geographically weighted Spearman’s rank correlation measure between prices and variables. Findings- GWR model substantially boosts goodness of fit in our pricing model compared to a standard hedonic regression model. The variation within GWR coefficients is high and of micro nature. Median GWR coefficients often differ from standard hedonic regression coefficients. The variability of coefficients is plotted on map. Conclusion- Findings suggest the existence of spatial non-stationarity in standard hedonic regressions and favor the use of models appropriate for spatial heterogeneity. Findings encourage further research in hedonic models applications such as in quality adjustments to price indices.

Tuberculosis (TB), as a respiratory infectious disease, has damaged public health globally for decades, and mainland China has always been an area with high incidence of TB. Since the outbreak of COVID-19, it has seriously occupied medical resources and affected medical treatment of TB patients. Therefore, the authenticity and reliability of TB data during this period have also been questioned by many researchers. In response to this situation, this paper excludes the data from 2019 to the present, and collects the data of TB incidence in mainland China and the data of 11 influencing factors from 2014 to 2018. Using spatial autocorrelation methods and multiscale geographically weighted regression (MGWR) model to study the temporal and spatial distribution of TB incidence in mainland China and the influence of selected influencing factors on TB incidence. The experimental results show that the distribution of TB patients in mainland China shows spatial aggregation and spatial heterogeneity during this period. And the R2 and the adjusted R2 of MGWR model are 0.932 and 0.910, which are significantly better than OLS model (0.466, 0.429) and GWR model (0.836, 0.797). The fitting accuracy indicators MAE, MSE and MAPE of MGWR model reached 5.802075, 110.865107 and 0.088215 respectively, which also show that the overall fitting effect is significantly better than OLS model (19.987574, 869.181549, 0.314281) and GWR model (10.508819, 267.176741, 0.169292). Therefore, this model is based on real and reliable TB data, which provides decision-making references for the prevention and control of TB in mainland China and other countries.

The chosen of distance weights is needed to form an accurate Geographically Weighting Regression model. There are 3 types of distance weights namely Gaussian kernel, Bisquare kernel and Tricube kernel. The weighting in GWR describes the closeness relation between locations. For data that has spatial heterogeneity, GWR models are more suitable models than OLS models. This study was conducted to obtain the best distance weighting based on minimum cross-validation method. Using secondary data from the Health Department in East Java with 34 districts for observation, the dependent variable is stunting and five independent variables that influence stunting cases. Based on the result, GWR models with fixed gaussian models produces a better accuracy in higher R 2 values compared to OLS models. The predicted map of the spread stunting cases conducted by interpolation GWR Kriging using exponential semivariogram.

Food security has become one of the central issues drawing global attention. Currently, the global food security situation is becoming increasingly urgent, profoundly impacting both China’s economic development and social stability, while also bearing significant national strategic importance. This paper is based on county-level grain production data from Sichuan Province between 2000 and 2022. Firstly, it employs methods such as standard deviation ellipses, center of gravity shifts, and spatial autocorrelation analysis to explore the spatiotemporal evolution of grain production in Sichuan Province. Furthermore, the Geodetector and GWR models are applied to identify and quantify the key drivers affecting grain production in Sichuan Province, as well as their spatial heterogeneity. The study finds that: (1) grain production in Sichuan Province shows a fluctuating growth trend, with clear regional disparities in its spatial distribution; (2) The spatial distribution of grain production in Sichuan Province exhibits a positive correlation, with its spatial association gradually strengthening, while also displaying significant spatial differences and regional clustering; (3) In terms of detecting driving factors, actual cultivated land area of the year has a significant impact on grain production, with its influence becoming particularly prominent when interacting with other factors; (4) Regarding the spatial heterogeneity of driving factors, each driver shows distinct spatial differentiation characteristics. Cultivated land area, fertilizer usage, and rural electricity consumption all exert a significant positive effect on overall grain production, while other influencing factors generally have a negative impact. This study not only deepens the scientific understanding of the spatiotemporal evolution and driving mechanisms of grain production but also provides scientific evidence and policy recommendations for food security and sustainable agricultural development in Sichuan Province and similar regions.

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

In the context of developing low-carbon economy to cope with climate change, China, as an economic power and energy consuming country, undertakes an important task of emission reduction. However, many factors, such as unreasonable industrial structure and energy consumption structure, have brought great challenges to the successful completion of China's carbon emission reduction task. In this paper, the entropy weight method is used to quantitatively analyze the relationship between the driving factors of carbon emission of energy structure and industrial structure and the result factors of carbon emission of energy consumption, and then the grey prediction model based on carbon emission optimization is established. According to the relationship between the driving factors and the result factors, the evolution law is summarized. At last, the advantages and disadvantages of the model are evaluated and summarized objectively. At the same time, the model can also provide reliable conclusions for the pollution of other polluting gases.

Tools and procedures to support decision making for cost-effective energy and carbon emissions optimization in building renovation

Unemployment is one of the problems that hinders employment development programs. Based on East Java BPS data, the Open Unemployment Rate in East Java in 2019 is about 3.92 percent. In 2020, unemployment increased by 466.02 thousand people and OUR increased by 2.02 percent to 5.84 percent in August 2020. In addition to the indicators that affect OUR, each observation location has different characteristics, so multiple linear regression modeling is not appropriate. Geographically Weighted Regression is one of the spatial analysis developed from multiple linear regression for data containing spatial heterogeneity effects. The weighting functions used for this GWR model are Kernel Fixed and Adaptive functions (Gaussian, Bi-Square, Tricube, and Exponential). The analytical step carried out in estimating the parameters is to use WLS. In the test, the best weighting was obtained, namely the Adaptive Tricube. Based on the results of the study, the GWR model with Adaptive Tricube weighted resulted in the value of R-Squared = 84.88%. However, the best model is obtained from the GWR model with exponential weighting with the smallest Akaike Information Criterion (AIC) value compared to the others, namely AIC = 86.01264 with R-Squared = 91.67.

This paper mainly takes Cangzhou City as an example to study spatial distribution of influence factors of residential land price, in order to deeply understand the driving factors of the spatial heterogeneity of residential land price in Cangzhou City. Based on the GWR model, the analysis of the influence factors of residential land price shows that the urban center has a significant positive impact on land prices, that is, the closer to the city center, the higher the land price is. The main roads, schools, and parks have different positive impacts on residential land prices, and there are large spatial differences in the degree of influence of various factors. The canal has a negative impact on residential land prices, that is, the closer to the canal, the lower the land price, mainly due to the existence of villages in city on both sides of the canal, which has a certain inhibitory effect on land prices. The impact of hospitals on residential land prices is not regular in spatial distribution.

<p>The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is <em>geographically weighted regression</em> (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.</p><p><strong>Keywords</strong><strong>: </strong>human development index, ordinary least square,<strong> </strong>geographically weighted regression, kernel fixed Gaussian, kernel fixed bisquare</p>