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.

Analyzing Influencing Factors of Rural Poverty in Typical Poverty Areas of Hainan Province: A Case Study of Lingao County

Rural poverty and poverty reduction are not only the focal issues that have attracted worldwide attention, but also the vital issues on people's livelihood that has attached great importance and aimed to be solved by the central and local governments of China. Based on the survey data of 354 farming households, this paper, taking the national poverty county of Lingao County, Hainan Province for an example, examined the characteristics of rural poverty of the county. Moreover, this paper established the spatial lag model(SLM) from five dimensions, namely, status of the household head, household structure, health status, income composition and traffic accessibility, to analyze the main influencing factors of rural poverty according to the values of Moran's I and the diagnosis of spatial dependence of the OLS model. It is found that the poor farming households gathered mainly in five towns in the north and southwest of the county, and the rural poverty have the characteristics of low educational level of the heads, more minor children, high population of farming peasants, high incidence of disease and low proportion of household wage-equivalent income. The results also showed that the variables such as the number of minor children, the number of migrant worker, the number of farming peasants and the proportion of wage-equivalent income have significant effectiveness on rural poverty, while the status of the household head, health status and traffic accessibility have little influence. It is an important way to realize the goal of poverty alleviation by controlling the number of farmers' fertility, strengthening the vocational skills training of farmers, vigorously developing specialization and large-scale agriculture and increasing the employment opportunities of farmers.

Is the “One Province One Rate” premium policy reasonable for Chinese crop insurance? The case in Jilin Province

Crop insurance in China is currently adopting the premium pricing strategy of "One Province One Rate", which appears to be in line with the systematic risk characteristics within crop insurance. This research aims to investigate the theoretical rationalization of this pricing strategy and its implications using the spatial lag model and the county-level data from the 45 corn plant counties of Jilin Province, China. Results corroborate that:(1) the spatial spillover effect of the corn yield risk is significant in Jilin but decreases rapidly when the risk unit includes more than eight counties; and(2) separating Jilin Province into eight risk zones for corn insurance will significantly reduce the high cross-subsidy phenomenon arising from the "One Province One Rate" strategy and shall benefit poor peasants in the region as well. This paper not only proves the existence of a systematic risk of crop insurance but also reveals that the spatial correlation and systemic features of the crop yield risk do not create a solid foundation for the current pricing strategy of "One Province One Rate". These conclusions will undoubtedly provide important references and empirical evidence for the role of China’s crop insurance in poverty alleviation.

Assessment of river basin habitat quality and its relationship with disturbance factors: A case study of the Tarim River Basin in Northwest China

The status of regional biodiversity is determined by habitat quality.The effective assessment of habitat quality can help balance the relationship between economic development and biodiversity conservation.Therefore,this study used the InVEST model to conduct a dynamic evaluation of the spatial and temporal changes in habitat quality of the Tarim River Basin in southern Xinjiang Uygur Autonomous Region of China by calc ulating the degradation degree levels for habitat types that were caused by threat factors from 1990 to 2018(represented by four periods of 1990,2000,2010 and 2018).Specifically,we used spatial autocorrelation analysis and Getis-Ord Gi*analysis to divide the study area into three heterogeneous units in terms of habitat quality:cold spot areas,hot spot areas and random areas.Hemeroby index,population density,gross domestic product(GDP),altitude and distance from water source(DWS)were then chosen as the main disturbance factors.Linear correlation and spatial regression models were subsequently used to analyze the influences of disturbance factors on habitat quality.The results demonstrated that the overall level of habitat quality in the TRB was poor,showing a continuous degradation state.The intensity of the negative correlation between habitat quality and Hemeroby index was proven to be strongest in cold spot areas,hot spot areas and random areas.The spatial lag model(SLM)was better suited to spatial regression analysis due to the spatial dependence of habitat quality and disturbance factors in heterogeneous units.By analyzing the model,Hemeroby index was found to have the greatest impact on habitat quality in the studied four periods(1990,2000,2010 and2018).The research results have potential guiding significance for the formulation of reasonable management policies in the TRB as well as other river basins in arid areas.

Exploring metro vibrancy and its relationship with built environment:a cross-city comparison using multi-source urban data

Recent urban transformations have led to critical reflections on the blighted urban infrastruc-tures and called for re-stimulating vital urban places.Especially,the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration.To date,limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically.This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment.Massive smart card data is processed to extract metro ridership,which denotes the vibrancy around the metro station in physical space.Social media check-ins are crawled to measure the vitality of metros in virtual spaces.Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method.Certain built environment characteristics,including land use,transportation and buildings are modeled as independent variables.The significant influences of built environ-mental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model.With experiments conducted in Shenzhen,Singapore and London,this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated.The regression analysis suggests that in all the three cities,more affluent urban areas tend to have higher metro virbrancy,while the road density,land use and buildings tend to impact metro vibrancy in only one or two cities.These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts.These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future.