Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Regionality,comprehensiveness,and complexity are regarded as the basic characteristics of geography.The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era.This paper focuses on the important method in geographic research:Geographic modeling and simulation.First,we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era.Then,the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives:Model resources,modeling processes,and operational architecture.Finally,we discern avenues for future research of geographic modeling and simulation systems for the study of regional,comprehensive and complex characteristics of geography.Based on these analyses,we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization,which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

Grid systems for geographic modelling and simulation:A review

Geography requires a comprehensive understanding of both natural and human factors,as well as their interactions.Due to the complexity and multiplicity of geographic problems,various theories and methods for geographic modelling and simulation have been proposed.Currently,geography has entered an era in which quantitative analysis and modelling are essential for understanding the mechanisms of geographic processes.As the basic idea of quantitative spatial analysis,the specified space often needs to be partitioned by a series of small computational units(cells),i.e.,grids.Thus,there is a close relationship between the grids and geographic modelling.This article reviews the mainstream and typical grids used for modelling and simulation.In addition to classification,the derived theories and technologies,including grid generation methods,data organization strategies,multi-dimensional querying methods,and grid adaptation techniques,are discussed.For integrated geographic simulation to explore comprehensive geographic problems,we argued that it is reasonable to build bridges among different types of grids(e.g.,transformation strategies),and more powerful grids that can support multi-type of numerical computation are urgently needed.

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.

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.

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.

Exploring a Typical Regional Model for Rural Industrial Revitalization:Using Shimen Township as a Case Study

Implementing the strategy of rural revitalization and embarking on the path of socialist rural revitalization with distinctive Chinese characteristics are essential steps in addressing existing gaps in agricultural and rural modernization.These steps aim to swiftly increase farmers’incomes,enhance agricultural production capabilities,foster high-quality rural economic development,and rectify issues related to uneven development.Revitalizing rural areas and alleviating poverty among the majority of farmers represent major decisions made by the state regarding the“three rural issues.”This paper conducts an analysis and discussion centered on Shimen Township in She County within the context of the agricultural and rural development revitalization strategy.The objective is to identify prevailing challenges and propose relevant solutions.

Relocation Selection for Poverty Alleviation:Factor Analysis and GIS Modeling

Resettlement is considered a major policy measure in two major Chinese policy programs,the "Great Development of the West" and poverty alleviation in the new century,and the "New Countryside Development".The selection of the target location of resettlement sites for poverty-stricken villages is of critical importance to the success of resettlement projects,yet the selection process is challenged by the need for analyzing a variety of contributing factors,and the need for many rounds of tedious data processing.So in this paper we present an in-depth analysis of the major factors and data processing model concerning mountainous povertystricken villages,which also takes a major part of China's poor villages.Our analysis shows the following factors bear the most importance in resettlement selection:1) topography:candidate areas should have slope less than 25 degrees and altitude less than 2400 meters.2) accessibility:close to market conventions places and transportation facilities.3) farming resources:with abundant land and water resources.4) non-intrusiveness:interests of receiving villages should be considered and negative impact minimized.A simple measure could be having the candidate area 1000 m away from the receiving residents.5) Minimal ecological and political footprint:candidate areas shall not conflict with nature conservation areas or nationally planned key land use projects.6) Social and cultural compatibility:residents will better off if relocated in the same county,considering language,religion,ethnic culture and other factors.Taking Makuadi,Lushui County of Nujiang Prefecture as a case study,we demonstrate how GIS analysis and modeling tools can be used in the selection process of resettlement projects in mountainous areas.

Factors affecting land dissection density in geomorphological regions of China

Land dissection density(LDD)provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections.A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters,soil erosion,and other related domains.Land dissection phenomena in China affects large areas with different morphological,pedological,and climatic characteristics.Prior studies have focused on the potential factors influencing the LDD at a watershed scale.However,these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale.LDD’s spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained.This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China’s six first-order geomorphological regions(GR1~GR6).The results yield the following findings:(1)LDD in China ranges from 0~4.55 km/km^(2),which is larger in central and eastern regions than in other regions of China;(2)dominant factors and their dominant risk subcategories vary with each geomorphological region’s primary internal and external forces;(3)the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors;relief degree of land surface(RDLS)is dominant in GR1,GR2,and GR5;the slope is dominant in GR6,soil type is dominant in GR3 and GR4,and lithology plays a critical role in the dominant interactions of GR3,GR4,and GR6;(4)the interactions between factors on LDD’s spatial pattern have a more significant effect than individual factors.

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.

Driving forces and their interactions of soil erosion in soil and water conservation regionalization at the county scale with a high cultivation rate

Soil erosion control based on county scale Soil and Water Conservation Regionalization(SWCR)is an essential component of China's ecological civilization construction.In SWCR,the quantitative analysis of the spatial heterogeneity and driving factors of soil erosion among different regions is still lacking.It is of great significance for soil erosion control to deeply examine the factors contributing to soil erosion(natural,land use,and socioeconomic factors)and their interaction at the county and regional levels.This study focused on a highly cultivated area,Hechuan District of Chongqing in the Sichuan Basin.The district(with 30 townships)was divided into four soil and water conservation regions(Ⅰ-Ⅳ)using principal component and hierarchical cluster analysis.The driving factors of soil erosion were identified using the geographical detector model.The results showed thatⅰ)the high cultivation rate was a prominent factor of soil erosion,and the sloping farmland accounted for 78.4%of the soil erosion in the study area;ⅱ)land use factors demonstrated the highest explanatory power in soil erosion,and the average interaction of land use factors explained 60.1%of soil erosion in the study area;ⅲ)the interaction between natural factors,socioeconomic factors,and land use factors greatly contributes to regional soil erosion through nonlinear-enhancement of double-factor enhancement.This study highlights the importance of giving special attention to the effects of land use factors on soil erosion at the county scale,particularly in mountainous and hilly areas with extensive sloping farmland and a high cultivation rate.

Factors driving surface deformations in plain area of eastern Zhengzhou City,China

With the rapid socio-economic development and urban expansion,land subsidence has emerged as a major environmental issue,impeding the high-quality development of the plain area in eastern Zhengzhou City,Henan Province,China.However,effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations.In order to accurately identify the dominant factor driving surface deformations in the study area,this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar(PS-InSAR)technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020.The acquired data was verified using leveling data.Subsequently,GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors.The findings are as follows:Finally,the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions.The findings are as follows:(1)Surface deformations in the study area are dominated by land subsidence,concentrated mainly in Zhongmu County,with a deformation rate of−12.5–−37.1 mm/a.In contrast,areas experiencing surface uplift are primarily located downtown,with deformation rates ranging from 0 mm to 8 mm;(2)Groundwater level,lithology,and urban construction exhibit strong spatial correlations with cumulative deformation amplitude;(3)Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations,with a contributive degree of 0.5328.The contributive degrees of driving factors are significantly enhanced through interactions.Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area.Their total contributive degree increases to 0.5722 after interactions,establishing them as the primary factors influencing surface deformation patterns in the study area.The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area,as well as contributing to research on the underlying mechanisms.

Prototyping an open environment for sharing geographical analysis models on cloud computing platform

The sharing of geographical analysis models is of crucial importance for simulating geographic processes and phenomena in the current geographical information systems(e.g.Digital Earth),but there remain some issues that have not been completely resolved.The challenges include,eliminating model heterogeneity and searching for suitable infrastructures to support the open sharing and effective execution of models.Taking advantage of cloud computing,this article aims to address the above issues and develop an open environment for geographical analysis model sharing.On the basis of the analysis of the applicability of cloud computing,the architecture of the open environment is proposed.More importantly,key strategies designed for heterogeneous model description,model encapsulating as well as model deploying and transparent accessing in the cloud are discussed in detail to establish such an environment.Finally,the prototype environment is implemented,and experiments were conducted to verify the environment’s feasibility to support the sharing of geographical analysis models.

Spatiotemporal Trends and Ecological Determinants in Population by Elevation in China Since 1990

It is very important to understand the ecological and socio-economic factors in population distribution and their changes over time for the compilation of regional development planning and the guidance of rational population flow.Using surface-based population data for China from 1990 to 2015,the national distribution and dynamics of the human population by elevation are quantified based on 1-km cell-size gridded distribution datasets and 1-km cell-size DEM(digital elevation model).A geographical detector model is used to quantitatively analyze the dominant role of natural geographical factors,such as topography and climate,on the spatial distribution of population.Results show that:1)the population size and density decrease rapidly with elevation below 1000 m above the sea level,and the gap in population density between low-altitude areas and high-altitude areas increases with time because of the continuous growth of population density in low-altitude areas;2)the distribution of the population can be divided into five steps according to integrated population density(IPD),in proportions of 43∶35∶21∶1∶0,and that these proportions have remained stable over the last 25 yr;3)the basic pattern of population spatial distribution is determined by natural geographical environment factors,such as topography,climate,geomorphology,and their interactions;and 4)the development of society and the economy are the driving forces for the dynamic change in the population distribution during the study period,with the distribution pattern and dynamics of population by altitude in China providing a comprehensive reflection of various geographical elements on different spatial scales.

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.

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.

Point-of-Interest Recommendation in LocationBased Social Networks with Personalized Geo-Social Influence

Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

Computational implementation of a GIS developed tool for prediction of dynamic ground movement and deformation due to underground extraction sequence

In the last century, there has been a significant development in the evaluation of methods to predict ground movement due to underground extraction. Some remarkable developments in three-dimensional computational methods have been supported in civil engineering, subsidence engineering and mining engineering practice. However, ground movement problem due to mining extraction sequence is effectively four dimensional （4D）. A rational prediction is getting more and more important for long-term underground mining planning. Hence, computer-based analytical methods that realistically simulate spatially distributed time-dependent ground movement process are needed for the reliable long-term underground mining planning to minimize the surface environmental damages. In this research, a new computational system is developed to simulate four-dimensional （4D） ground movement by combining a stochastic medium theory, Knothe time-delay model and geographic information system （GIS） technology. All the calculations are implemented by a computational program, in which the components of GIS are used to fulfill the spatial-temporal analysis model. In this paper a tight coupling strategy based on component object model of GIS technology is used to overcome the problems of complex three-dimensional extraction model and spatial data integration. Moreover, the implementation of computational of the interfaces of the developed tool is described. The GIS based developed tool is validated by two study cases. The developed computational tool and models are achieved within the GIS system so the effective and efficient calculation methodology can be obtained, so the simulation problems of 4D ground movement due to underground mining extraction sequence can be solved by implementation of the developed tool in GIS.

Spatio-temporal Distribution Characteristics and Environmental Impact Factors of Lung Cancer Mortality: A Case Study of Yuhui District in Bengbu City, China

Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter;2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River;3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment;4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.

A new academic impact metric for evaluating geographic simulation models

Geographic simulation models can be used to explore and better understand the geographical environment. Recent advances in geographic and socio-environmental research have led to a dramatic increase in the number of models used for this purpose. Some model repositories provide opportunities for users to explore and apply models,but few provide a general evaluation method for assessing the applicability and recognition of models. In this study,an academic impact evaluation method for models is proposed. Five indices are designed based on their pertinence. The analytical hierarchy process is used to calculate the index weights,and the academic impacts of models are quantified with the weighted sum method. The time range is controlled to evaluate the life-term and annual academic impacts of the models. Some models that met the evaluation criteria from different domains are then evaluated. The results show that the academic impact of a model can be quantified with the proposed method,and the major research areas that models impact are identified.