Gridded Population Distribution Map for the Hebei Province of China

Abstract

Mapping the distribution of populations has become an important issue in geographical and relative researchers. Combining population and spatial data allows for socio-graphic information to be visualized, in order to evaluate the total numbers of people at risk of environmental health hazards, who have died in natural disasters etc. Therefore, spatial distribution of population data is an effective way to integrate statistical and spatial data. This paper presents a multi-factor data fusion modeling method for population estimation, which is based on spatial relationships that determine the factors affecting population distribution. The factors that have a strong correlation with population distribution in the Hebei Province were extracted using Geographic Information Systems (GIS). Their standardized weight coefficients were factored as weight coefficients of population distribution in a given spatial unit. The unit (1 km × 1 km) population database was established, allowing for the computation of the relevant population data error. The accuracy of the map was then assessed by comparing predicted population data with that collected from the local government. The results show that the population correlated with geographical factors. The population of the Hebei Province was distributed heterogeneously, increasing from the northwest to southeast. There was relatively low population density in the Taihang Mountains in the west and in the Yanshan Mountains in the northeast, with less than 100 people per square kilometer. The population density in the central Hebei Province was higher, with about 2,000 people per square kilometer, which was higher and denser than that in Handan, Shijiazhuang, Langfang, and Tangshan. These findings may be important for data mining (DM), Decision-making Support Systems (DSS), and regional sustainable development.

The relationship between terrain and rural migration (1965–2013) on the north of Turkey (the case of Kastamonu)

“…Despite the difficulties faced by people living in the rural regions during the migration process and its aftermath, their migration can be understood as a reaction to the economic, social, political, ecological, and topographical diversity they faced. In this study, ignoring the other factors, the migration phenomenon experienced during the past 48 years was analyzed in relation to the topographical factors, and as a result of the statistical evaluation, topography was determined to be a definitive factor in the population (Cohen and Small 1998 ; Telbisz et al 2014 ; Milan and Ho 2014 ; Telbisz et al 2015 ; Telbisz et al 2016 ; Zhang et al 2015 ).…”

“…Although rarely indicated in the literature, topography is an important factor contributing to the decision to move. Topography is also an important factor that directly affects the distribution of forest, the environment, and human interactions (Cohen and Small 1998 ; Small and Cohen 2004 ; Wu and Yao 2010 ; Bajat et al 2011 ; Milan and Ho 2014 ; Telbisz et al 2014 ; Telbisz et al 2015 ; Zhang et al 2015 ; Kummu et al 2016 ; Telbisz et al 2016 ). Therefore, it is considered to be the main factor behind long-term migration patterns and to nurture the potential for future migration.…”

Spatial Distribution of Lead in the Soil of Urban Areas Under Different Land-Use Types

“…Although the total concentration of heavy metals is a useful indicator for assessing soil pollution, it does not provide sufficient information on the adsorption and toxicity of heavy metals. The mobility, adsorption, and toxicity of heavy metals to plants depend significantly on their chemical forms in the soil (36,37). Heavy metals are present in the soil in the form of soluble, exchange, carbonate, bound to iron and manganese oxides, organic, and residual.…”

Disaggregating County-Level Census Data for Population Mapping Using Residential Geo-Objects With Multisource Geo-Spatial Data

“…Second, multifactor integration fuses of different schemes for each single factor and obtains population distribution weights of each unit for disaggregating the total population census of administrative unit. The most typical application of this idea is LandScan, a global population distribution database developed by Oak Ridge National Laboratory in the United States [13]- [15]. Third, multivariate regression constructs the relationship between multiple factors and population statistics by using spatial data containing population distribution information.…”