Modeling of Spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

Association between Macroscopic-factors and Identified HIV/AIDS Cases among Injecting Drug Users: An Analysis Using Geographically Weighted Regression Model

Drug use （DU）, particularly injecting drug use （IDU） has been the main route of transmission and spread of Human Immunodeficiency Virus （HIV）/Acquired Immune Deficiency Syndrome （AIDSJ among injecting drug users （IDUs）[1]. Previous studies have proven that needles or cottons sharing during drug injection were major risk factors for HIV/AIDS transmission at the personal level[z4]. Being a social behavioral issue, HIV/AIDS related risk factors should be far beyond the personal level. Therefore, studies on HIV/AIDS related risk factors should focus not only on the individual factors, but also on the association between HIV/AIDS cases and macroscopic-factors, such as economic status, transportation, health care services, etc[1]. The impact of the macroscopic-factors on HIV/AIDS status might be either positive or negative, which are potentially reflected in promoting, delaying or detecting HIV/AIDS epidemics.

Spatial non-stationary characteristics between temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass,and their accuracy has important practical significance for the stable management of grasses and livestock.To this end,the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China(August 2020),combined with hydrometeorology,elevation,net primary productivity(NPP),and other auxiliary data over the same period.Accordingly,non-stationary characteristics of the spatial scale,and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression(MGWR)model.The results showed that the model was suitable for correlation analysis.The spatial scale of ratio resident-area index(PRI)was the largest,followed by the digital elevation model,NPP,distance from gully,distance from river,average July rainfall,and daily temperature range;whereas the spatial scales of night light,distance from roads,and relative humidity(RH)were the most limited.All influencing factors maintained positive and negative effects on grass yield,save for the strictly negative effect of RH.The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield.Regression parameters revealed that the results of Ordinary least squares(OLS)(Adjusted R^(2)=0.642)and geographically weighted regression(GWR)(Adjusted R^(2)=0.797)models were worse than those of MGWR(Adjusted R^(2)=0.889)models.Based on the results of the RMSE and radius index,the simulation effect also was MGWR>GWR>OLS models.Ultimately,the MGWR model held the strongest prediction performance(R^(2)=0.8306).Spatially,the grass yield was high in the south and west,and low in the north and east of the study area.The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.

Temporal and spatial responses of ecological resilience to climate change and human activities in the economic belt on the northern slope of the Tianshan Mountains, China

In the Anthropocene era,human activities have become increasingly complex and diversified.The natural ecosystems need higher ecological resilience to ensure regional sustainable development due to rapid urbanization and industrialization as well as other intensified human activities,especially in arid and semi-arid areas.In the study,we chose the economic belt on the northern slope of the Tianshan Mountains(EBNSTM)in Xinjiang Uygur Autonomous Region of China as a case study.By collecting geographic data and statistical data from 2010 and 2020,we constructed an ecological resilience assessment model based on the ecosystem habitat quality(EHQ),ecosystem landscape stability(ELS),and ecosystem service value(ESV).Further,we analyzed the temporal and spatial variation characteristics of ecological resilience in the EBNSTM from 2010 to 2020 by spatial autocorrelation analysis,and explored its responses to climate change and human activities using the geographically weighted regression(GWR)model.The results showed that the ecological resilience of the EBNSTM was at a low level and increased from 0.2732 to 0.2773 during 2010–2020.The spatial autocorrelation analysis of ecological resilience exhibited a spatial heterogeneity characteristic of"high in the western region and low in the eastern region",and the spatial clustering trend was enhanced during the study period.Desert,Gobi and rapidly urbanized areas showed low level of ecological resilience,and oasis and mountain areas exhibited high level of ecological resilience.Climate factors had an important impact on ecological resilience.Specifically,average annual temperature and annual precipitation were the key climate factors that improved ecological resilience,while average annual evapotranspiration was the main factor that blocked ecological resilience.Among the human activity factors,the distance from the main road showed a negative correlation with ecological resilience.Both night light index and PM2.5 concentration were negatively correlated with ecological resilience in the areas with better ecological conditions,whereas in the areas with poorer ecological conditions,the correlations were positive.The research findings could provide a scientific reference for protecting the ecological environment and promoting the harmony and stability of the human-land relationship in arid and semi-arid areas.

Measurement for coordinated development of＂four modernizations＂ and its efficiency ofprefecture level cities or above in China

The efficient and coordinated development of industrialization, urbanization, informatization and agricultural modernization(so called 'Sihua Tongbu' in China, and hereinafter referred to as 'four modernizations') is not only a practical need but also an important strategic direction of integrating urban-rural development and regional development in recent China. This paper evaluated the comprehensive, coupling and coordinated developmental indices of 'four modernizations' of China's 343 prefecture-level administrative units, and calculated their efficiency of 'four modernizations' in 2001 and 2011. The efficiency evaluation index system was established. The efficiencies and their changing trend during the period 2001–2011 were investigated using the data envelopment analysis(DEA) model. Spatial-temporal pattern of the efficiency of China's prefecture-level units was explored by using exploratory spatial data analysis(ESDA). Finally, the main influencing factors were revealed with the aid of geographically weighted regression(GWR) model. Results indicate that the comprehensive, coupling and coordinated developmental indices and efficiency of 'four modernizations' of China's prefecture-level administrative units have obvious spatial differences and show diverse regional patterns. Overall, the efficiency is relatively low, and only few units with small urban populations and economic scale are in DEA efficiencies. The efficiency changing trends were decreasing during 2001–2011, with a transfer of high efficiency areas from inland to eastern coastal areas. The difference between urban and rural per capita investment in fixed assets boasts the greatest influence on the efficiency.

Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males,unemployed females,percentage of poor households i.e.,poverty prevalence,night lights index,literacy rate,household food security,and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios(LLR),global Moran’s I,local indicator of spatial association-LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression(GWPR)and semi-parametric GWPR(s-GWPR)were used to determine the spatial association between HIV incidence and socio-economic factors.HIV incidence(number of cases per 1000)ranged from 0.6(Buhera district)to 13.30(Mangwe district).Spatial clustering of HIV incidence was observed(Global Moran’s I=-0.150;Z score 3.038;p-value 0.002).Significant clusters of HIV were observed at district level.HIV incidence and its association with socio-economic factors varied across the districts except percentage of females unemployed.Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.

Uncovering the CO_(2)emissions of vehicles:A well-to-wheel approach

Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effective emission reduction policies.Current emission inventories for vehicles have either low-resolution,or limited coverage,and they have not adequately focused on the CO_(2)emission produced by new energy vehicles(NEV)considering fuel life cycle.To fill the research gap,this paper proposed a framework of a high-resolution well-to-wheel(WTW)CO_(2)emission estimation for a full sample of vehicles and revealed the unique CO_(2)emission characteristics of different categories of vehicles combined with vehicle behavior.Based on this,the spatiotemporal characteristics and influencing factors of CO_(2)emissions were analyzed with the geographical and temporal weighted regression(GTWR)model.Finally,the CO_(2)emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies.The results show that the distribution of vehicle CO_(2)emissions shows obvious heterogeneity in time,space,and vehicle category.By simply adjusting the existing NEV promotion policy,the emission reduction effect can be improved by 6.5%-13.5%under the same NEV penetration.If combined with changes in power generation structure,it can further release the emission reduction potential of NEVs,which can reduce the current CO_(2)emissions by 78.1%in the optimal scenario.

Spatiotemporal analysis of the impact of urban landscape forms on PM_(2.5) in China from 2001 to 2020

Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) variations.Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence plots.The results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the northwest.PLAND-UGI is the landscape index with the largest contribution(30%)and interpretable range.The relationship between FRACT and PM_(2.5) was more complex than for other landscape indices.The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.