一种人口连续分布模型的研究

分析了常用的表示人口分布的方法及其不足,提出了将人口统计数据空间分布化的方法,将研究区域划分为一定分辨率的格网,用距离衰减函数将人口密度估计值分配到每个格网上,每个格网上的人口是均匀分布的,随着格网分辨率的提高,就可以模拟出符合人口详细分布的人口密度空间连续分布模型,并通过实验说明该方法是可行的.     

人口密度的空间连续分布模型

分析了常用的表示人口分布的方法,并指出了其不足的地方,提出了模拟人口空间分布的必要性,同时提出了模拟人口空间分布的思路。将研究区域划分为一定分辨率的格网,用核心估计方法将人口密度估计值分配到每个格网上,每个格网上的人口是均匀分布的,随着格网分辨率的提高,就可以模拟出符合人口详细分布的人口密度空间连续分布模型,并通过实验说明该方法是可行的。     

人口数据空间分布化模型研究

分析人口数据地理空间分布化的必要性，针对常用的表示人口分布方法的不足，提出将人口数据分布到规则格网上的数字人口模型(Digital Population Model，简称DPM)，并详细阐述了DPM建立的原理及实现的技术方法，以及目标区域格网划分的原则、方法。通过样区实验，证实该方法表示分布人口的准确性、合理性。     

基于土地利用类型提高人口格网数据空间精度的方法

高空间精度的人口格网数据具有空间分辨率高、人口空间分布特征准确的特点,在受灾人群估计、城市规划建设等领域有广泛的应用。针对已有的公开人口格网数据集(如WorldPop世界人口格网数据集)存在人口空间分布特征在小尺度上刻画不准确、空间分辨率较低的问题,本文使用东营市土地利用类型数据,结合地类权重和面积权重对WorldPop数据进行空间精度优化,获得东营市东营区黄河路街道25 m人口格网数据。相较于WorldPop数据,经过本文方法处理后的数据集空间分辨率更优,在可视化对比中,能够更准确地刻画人口分布特征,并在各地类人口占比统计中人口空间分布与用地单元分布一致性更高。该方法为获取小区域高空间精度人口格网数据提供了一种新思路。     

2000—2020年东营市人口分布的时空特征演变规律及影响因素分析

本文基于2000—2020年人口格网数据对东营市乡镇尺度人口分布的时空特征及演变规律进行了分析,并利用地理探测器,探讨了东营市人口分布的影响机制.时序特征表明:东营市整体人口呈增长状态,但各区县及乡镇的人口数量差异较大;空间特征表明:人口密度总体上呈现南密北稀的状态,人口密度分布不均衡;时空演变规律表明:人口重心呈西北...     

利用泰森多边形和格网平滑的人口密度空间化研究——以徐州市为例

在运用泰森多边形作边界替代得到徐州市区人口密度的分布图后,采用GCAWI法实现了徐州市区人口数据的空间化,提出了采用大小两种格网错置的平滑方法,并运用此方法得到徐州市2000年和2005年的500 m×500 m的格网人口密度分布图。结果表明:①通过对比两个年份的人口密度分布图可以发现,徐州市区人口密度在2000年~2005年期间具有南移东扩的趋势;②利用大小两种格网错置平滑的方法实现了对人口密度空间化与平滑两个功能,平滑后的结果表明,徐州市区人口密度的空间分布具有块状集聚、轴状延伸的格局,整体来看呈现出"大"字形的态势;③通过与直接运用核函数内插得到的人口密度分布图的对比可以发现,前者对反映人口密度分布的细部差异比较敏感,而后者仅能反映出人口密度空间分布的整体格局。     

基于GIS的区域人口密度空间分布模拟——以张家界市永定区为例

人口统计数据空间化是解决统计数据与自然要素数据融合分析的有效途径。本文以张家界市永定区为研究单元,对小尺度区域的人口密度空间分布模拟进行了初步研究。文章根据2005年《永定区统计年鉴》所统计的人口数据,分析了永定区各乡镇平均人口密度与土地利用类型指数及地形指数(平均高程和平均坡度)之间的相关性。基于相关性分析,以GIS软件与SPSS统计软件为工具,运用多元回归的思想建立了人口数据空间化模型,同时生成了永定区250m×250m空间分辨率的栅格人口密度图。结果表明,模型模拟的精度较高,模拟过程具有较强的可操作性,可为县域尺度人口空间分布的应用研究提供借鉴。同时,研究结果为永定区推进城市化进程,提高人口、资源和环境的科学管理提供了理论基础。     

基于GIS的宝鸡市人口空间格局分析

搜集整理了宝鸡市2003~2012年人口统计数据,并结合GIS技术探讨了宝鸡市人口空间分布的基本规律和特征。研究结果表明,(1)宝鸡市人口密度总体较低,在空间分布上,沿渭河谷地人口密度较高,南北山区人口密度较低;(2)宝鸡市人口重心不断发生迁移,迁移速度也存在差异;(3)宝鸡市人口呈现缓慢集中趋势,集中指数较低,说明人口空间分布较分散;(4)宝鸡市人口聚集度总体较低,空间分布差异明显,渭河谷地各区县人口聚集度高于南北山区。     

基于特征分区的人口数据格网化研究

针对人口数据格网化分析不足的问题,可借助夜间灯光信息、地形信息、土地利用信息进行特征分区,在不同分区范围采用不同的方式构建人口空间格网化模型,反映人口在格网上的分布状态,提供了良好的小尺度统计人口方法。本文以四川省县区作为研究区域,对其进行特征分区,分为中心区(强灯光区)、一般灯光区和弱灯光区,分别采用圈层距离法、改进的夜间灯光建模法与土地利用建模法分析,并利用小一级的单位进行精度分析,研究表明:在灯光强度差距较大的一般灯光区,采用夜间灯光数据可以较好地模拟人口格网化数据,结果精度较高;在灯光强度差距较小的弱灯光区,土地利用模型可以有效地模拟人口空间分布;但灯光强度几乎饱和的中心区,采用圈层距离法,存在较大误差。     

集成地理探测器与随机森林模型的城市人口分布格网模拟

精细尺度的城市人口分布信息是城市资源配置和综合管理的重要依据。本文以广州市越秀区、荔湾区、天河区、海珠区、白云区及黄埔区作为研究区域，基于人口统计、夜间灯光、兴趣点及土地利用等多源数据，利用地理探测器识别人口分布的影响因子，运用随机森林模型开展人口分布空间格网模拟研究。研究结果表明，与传统的相关分析相比，地理探测器能够更为准确地识别人口空间分布的重要影响因子。基于随机森林模型的人口分布格网模拟结果与街道（镇）实际人口的相关系数为0.774，平均相对误差约为30%。相比基于线性回归模型的模拟结果，随机森林模型的精度有明显提高。     

人口统计数据的空间分布化研究

分析了传统的人口空间分布密度衰减函数－指数型和Gauss型，指出了其应用的局限性，对于有两个中心以上的城市，提出了将人口统计数据空间分布化的思路和方法。     

POPULATION CENSUS WITH THE AID OF AERIAL PHOTOGRAPHS: AN EXPERIMENT IN THE CITY OF LEEDS

This study shows how aerial photographs can be of value in a population census. The census and the enumeration district maps were used initially to obtain population data and the housing stock was derived from the aerial photographs. From these the population densities were determined of a number of sample enumeration districts containing a single type of house. Another set of enumeration districts was selected and the housing stock again derived from the aerial photographs. By considering the type and quantity of housing stock and the population density of each housing type, the population figures were estimated for each enumeration district. The values of these population estimates were then compared with the values recorded in the census. The overall population estimate had an error of only 2%, but the estimates for some of the individual enumeration districts showed greater errors. These errors are assessed and analysed and some suggestions are made to improve the methodology used in this study.     

城市空间信息规则网格与不规则网格的数据转换

对从不规则网格向规则网格进行社会经济信息转换的方法进行了比较和研究，并采用蒙特卡罗和GIS相结合的方法，以人口数据为例，利用土地利用分类信息将以行政区为基础采集的人口数据转换到规则网格中。实验证明，转换后的人口分布数据较好地体现了人口在空间的分布情况，并可根据不同的需要，聚合成不同大小的网格，能较好地满足城市微观建模和宏观社会经济信息统计分析的需要。     

Dasymetric Estimation of Population Density and Areal Interpolation of Census Data

This paper describes techniques to compute and map dasymetric population densities and to areally interpolate census data using dasymetrically derived population weights. These techniques are demonstrated with 1980-2000 census data from the 13-county Atlanta metropolitan area. Land-use/land-cover data derived from remotely sensed satellite imagery were used to determine the areal extent of populated areas, which in turn served as the denominator for dasymetric population density computations at the census tract level. The dasymetric method accounts for the spatial distribution of population within administrative areas, yielding more precise population density estimates than the choroplethic method, while graphically representing the geographic distribution of populations. In order to areally interpolate census data from one set of census tract boundaries to another, the percentages of populated areas affected by boundary changes in each affected tract were used as adjustment weights for census data at the census tract level, where census tract boundary shifts made temporal data comparisons difficult. This method of areal interpolation made it possible to represent three years of census data (1980, 1990, and 2000) in one set of common census tracts (1990). Accuracy assessment of the dasymetrically derived adjustment weights indicated a satisfactory level of accuracy. Dasymetrically derived areal interpolation weights can be applied to any type of geographic boundary re-aggregation, such as from census tracts to zip code tabulation areas, from census tracts to local school districts, from zip code areas to telephone exchange prefix areas, and for electoral redistricting.     

Accuracy of areal interpolation: A comparison of alternative methods

This paper discusses the accuracy of spatial data estimated by areal interpolation, a process of transferring data from one zonal system to another. A stochastic model is proposed which represents areal interpolations in diverse geographic situations. The model is used to examine the relationship between estimation accuracy and the spatial distribution of estimation error from a theoretical viewpoint. The analysis shows that the uniformity in error distribution improves the accuracy of areal interpolation. Four areal interpolation methods are then assessed through numerical examinations. From this it is found that the accuracy of simple interpolation methods heavily depends on the appropriateness of their hypothetical distributions, whereas the accuracy of intelligent methods depends on the fitness of the range of supplementary data for that of true distribution. Received: 19 February 1999/Accepted 17 September 1999     

Mapping Population Distribution in the Urban Environment: The Cadastral-based Expert Dasymetric System (CEDS)

This paper discusses the importance of determining an accurate depiction of total population and specific sub-population distribution for urban areas in order to develop an improved "denominator," which would enable the calculation of more correct rates in GIS analyses involving public health, crime, and urban environmental planning. Rather than using data aggregated by arbitrary administrative boundaries such as census tracts, we use dasymetric mapping, an areal interpolation method using ancillary information to delineate areas of homogeneous values. We review previous dasymetric mapping techniques (which often use remotely sensed land-cover data) and contrast them with our technique, Cadastral-based Expert Dasymetric System (CEDS), which is particularly suitable for urban areas. The CEDS method uses specific cadastral data, land-use filters, modeling by expert system routines, and validation against various census enumeration units and other data. The CEDS dasymetric mapping technique is presented through a case study of asthma hospitalizations in the Bronx, New York City, in relation to proximity buffers constructed around major sources of air pollution. The case study shows the impact that a more accurate estimation of population distribution has on a current environmental justice and health disparities research project, and the potential of CEDS for other GIS applications.     

Dasymetric Mapping Using High Resolution Address Point Datasets

Mismatching sets of boundaries present a persistent problem in spatial analysis for many different applications. Dasymetric mapping techniques can be employed to estimate population characteristics of small areas that do not correspond to census enumeration boundaries. Several types of ancillary data have been used in dasymetric mapping but performance is often limited by their relatively coarse resolution and moderate correspondence to actual population counts. The current research examines the performance of using high resolution ancillary data in the form of individual address point datasets which represent the locations of all addressable units within a jurisdiction. The performance of address points was compared with several other techniques, including areal weighting, land cover, imperviousness, road density and nighttime lights. Datasets from 16 counties in Ohio were used in the analysis, reflecting a range of different population densities. For each technique the ancillary data sources were employed to estimate census block group population counts using census tracts as source zones, and the results were compared with the known block group population counts. Results indicate that address points perform significantly better compared with other types of ancillary data. The overall error for all block groups (n = 683) using address points is 4.9% compared with 10.8% for imperviousness, 11.6% for land cover, 13.3% for road density, 18.6% for nighttime lights and 21.2% for areal weighting. Using only residential address points rather than all types of locations further reduces this error to 4.2%. Analysis of the spatial patterns in the relative performance of the various techniques revealed that address points perform particularly well in low density rural areas, which typically present challenges for traditional dasymetric mapping techniques using land cover datasets. These results provide very strong support for the use of address points for small area population estimates. Current developments in the growing availability of address point datasets and the implications for spatial demographic analyses are discussed.     

Testing the Effects of Thematic Uncertainty on Spatial Decision-making

Dasymetric mapping techniques can be employed to estimate population characteristics of small areas that do not correspond to census enumeration areas. Land cover has been the most widely used source of ancillary data in dasymetric mapping. The current research examines the performance of alternative sources of ancillary data, including imperviousness, road networks, and nighttime lights. Nationally available datasets were used in the analysis to allow for replicability. The performance of the techniques used to examine these sources was compared to areal weighting and traditional land cover techniques. Four states were used in the analysis, representing a range of different geographic regions: Connecticut, New Mexico, Oregon, and South Carolina. Ancillary data sources were used to estimate census block group population counts using census tracts as source zones, and the results were compared to the known block group population counts. Results indicate that the performance of dasymetric methods varies substantially among study areas, and no single technique consistently outperforms all others. The three best techniques are imperviousness with values greater than 75 percent removed, imperviousness with values greater than 60 percent removed, and land cover. Total imperviousness and roads perform slightly worse, with nighttime lights performing the worst compared to all other ancillary data types. All techniques performed better than areal weighting.     

A Comparative Analysis of Areal Interpolation Methods

Over the years many approaches to areal interpolation have been developed and utilized. They range from the simple 2-D areal weighing method which uses only the spatial Z variable being processed, to more sophisticated approaches which use auxiliary variable(s) to provide more specificity to the results. In the research reported here, four promising approaches are implemented and comparatively tested. These approaches have widely varying conceptual foundations, and different auxiliary variables, if used. The areal weighing reflects many earlier methods which assumes uniform distributions of the spatial Z variable, and does not use any auxiliary variable. Tobler's pycnophylactic method uses a volumetric preservation approach, which assumes spatial Z variable is heterogeneously distributed, but does not use any auxiliary variable. The traditional dasymetric method of Wright is used with remote sensing spectral data of land use. Xie's road network hierarchically weighted interpolation uses the road network as the auxiliary variable, and assumes that population density is related to the class of the road, and to the density of the road network. The research design implemented here uses Census population distributions at different levels in the hierarchy as the source and target variables analyzed. The source zone population is taken at the Census Tract level, and the target zones are specified at the Census Block Group level in the hierarchy. The first two tests use only the Census population Z data, and no auxiliary variables, whereas the next uses remotely sensed land use data as the auxiliary data variable, and the fourth test utilizes the road network hierarchy as the auxiliary variable. The paper reports on the findings from these tests, and then interprets them in a spatial setting in terms of accuracy and effectiveness. This research points to the network method as the most accurate of the areal interpolation methods tested in this research.