Generating Surface Models of Population Using Dasymetric Mapping*

Aggregated demographic datasets are associated with analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. This article describes a methodology for generating a surface‐based representation of population that mitigates these problems. This methodology uses dasymetric mapping and incorporates areal weighting and empirical sampling techniques to assess the relationship between categorical ancillary data and population distribution. As a demonstration, a 100‐meter‐resolution population surface is generated from U.S. Census block group data for the southeast Pennsylvania region. Remote‐sensing‐derived urban land‐cover data serve as ancillary data in the dasymetric mapping.     

Modeling Sensitivity to Accuracy in Classified Imagery: A Study of Areal Interpolation by Dasymetric Mapping*

Areal interpolation is the process by which data collected from one set of zonal units can be estimated for another zonal division of the same space that shares few or no boundaries with the first. In previous research, we outlined the use of dasymetric mapping for areal interpolation and showed it to be the most accurate method tested. There we used control information derived from classified satellite imagery to parameterize the dasymetric method, but because such data are rife with errors, here we extend the work to examine the sensitivity of the population estimates to error in the classified imagery. Results show the population estimates by dasymetric mapping to be largely insensitive to the errors of classification in the Landsat image when compared with the other methods tested. The dasymetric method deteriorates to the accuracy of the next worst estimate only when 40% error occurs in the classified image, a level of error that may easily be bettered within most remote sensing projects.     

Incorporating GIS Building Data and Census Housing Statistics for Sub-Block-Level Population Estimation

This article presents a deterministic model for sub-block-level population estimation based on the total building volumes derived from geographic information system (GIS) building data and three census block-level housing statistics. To assess the model, we generated artificial blocks by aggregating census block areas and calculating the respective housing statistics. We then applied the model to estimate populations for sub-artificial-block areas and assessed the estimates with census populations of the areas. Our analyses indicate that the average percent error of population estimation for sub-artificial-block areas is comparable to those for sub-census-block areas of the same size relative to associated blocks. The smaller the sub-block-level areas, the higher the population estimation errors. For example, the average percent error for residential areas is approximately 0.11 percent for 100 percent block areas and 35 percent for 5 percent block areas.     

A Point-Based Intelligent Approach to Areal Interpolation

Areal interpolation is the data transfer from one zonal system to another. A survey of previous literature on this subject points out that the most effective methods for areal interpolation are the intelligent approaches, which often take two-dimensional (2-D) land use or one-dimensional (1-D) road network information as ancillary data to give insight on the underlying distribution of a variable. However, the 2-D or 1-D ancillary information is not always applicable for the variable of interest in a specific study area. This article introduces a point-based intelligent approach to the areal interpolation problem by using zero-dimensional (0-D) points as ancillary data that are locationally associated with the variable of interest. The connection between zonal variables and point locations can be modeled with a linear or a nonlinear exponential function, which incorporates the distribution of the variables in the transferring of the information from the source zone to the target zone. An experimental study interpolating the population data at a suburbanized area suggests that the proposed method is an attractive alternative to other areal interpolation solutions based on the evaluation of its resulting accuracy and efficiency.     

Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses

To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries of the 2010 census (target zones) for five demographically distinct counties in the US. Three levels of dasymetric refinement of source and target zones are evaluated. First, residential parcels are used as a binary ancillary variable prior to regular areal interpolation methods. Second, Expectation Maximization (EM) and its data-extended version leverage housing types of residential parcels as a related ancillary variable. Finally, a third refinement strategy to mitigate the overestimation effect of large residential parcels in rural areas uses road buffers and developed land cover classes. Results suggest the effectiveness of all three levels of dasymetric refinement in reducing estimation errors. They provide a first insight into the potential accuracy improvement achievable in varying geographic and demographic settings but also through the combination of different refinement strategies in parts of a study area. Such improved consistent population estimates are the basis for advanced spatio-temporal demographic research.     

A Dasymetric Map of Maine Lobster Trap Distribution Using Local Knowledge

As coastlines face increased development pressure, it is important to have the best available spatial information on coastal activities, including commercial fishing. This article describes the creation of a fine-scale spatial representation of lobster fishing activity along the Maine coast of the United States using a combination of participatory geographic information systems (GIS) and dasymetric mapping methods. These methods are employed here to support data collection from a large number of active lobster harvesters while maintaining individual privacy. The resulting representation of the fishery is designed as a planning tool for identifying potential interactions between marine resources and human activities.     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

Using Dasymetric Mapping to Identify Communities at Risk from Hazardous Waste Generation in San Antonio,Texas

The primary objective of this study is to examine if minorities, the poor, and non-homeowners have a higher potential for exposure than the general population to large-quantity hazardous waste generation in Bexar County, Texas. Results indicate that this is indeed the case: people living near the generators are more often Black non-Hispanic or Hispanic, and more likely to live below the poverty level and not own their own home. Although previous studies conducted in the United States have used states, counties, zip codes, or census tracts as the unit of spatial analysis, we use dasymetric mapping to create population maps at a resolution of 30 m. This allows for the reconfiguration of the areal aggregations chosen for the analysis while preserving its findings. It also reduces, if not eliminates, the effects of the Modifiable Area Unit Problem (MAUP) on the results.     

Disaggregating population density of the European Union with CORINE land cover

This article describes and compares six disaggregation methods used to produce a dasymetric population density grid of the European Union at a 100 m resolution. Population data were initially available at commune level. The main ancillary information source was CORINE land cover, a land cover map distributed by the European Environment Agency. Information from the Eurostat point survey, land use/cover area frame survey, was also integrated in the parameter estimation of some of the approaches tested. Accurate population data for 1 km cell grids were provided by the Statistical Offices of Austria, Denmark, Finland, the Netherlands, Northern Ireland, Estonia and Sweden. These data provided the basic reference to quantify the accuracy of each method. The best results were obtained with a modified version of the limiting variable method (Eicher, C. and Brewer, C., 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science, 28, 125–138) that could be implemented, thanks to the national reference grids. For other methods the parameters could be estimated without using the reference grids; among them a method based on logit regression gave the best results. Compared with the traditional choropleth maps that represent a homogeneous density in each commune, the accuracy improvement of the disaggregated maps ranged between 20% and 67% (between 46% and 67% for the best method).     

吉林省人口老龄化时空分异特征及成因

本文基于地理信息软件ArcGIS 10.1,选取老年人口系数为人口老龄化指标,运用人口重心模型、空间自相关和半变异函数等分析方法,对吉林省第四次、第五次、第六次人口普查数据进行分析,探讨1990-2010年吉林省人口老龄化时空分异特征,并采用多元线性回归方法解释其形成原因。研究结果表明：①吉林省各县市人口老龄化差异明显,进程加快,重心由西北向东南方向移动,但移动速度减缓;②吉林省人口老龄化存在正的空间自相关性,在空间的集聚呈现先弱后强的变化趋势;③人口老龄化高—高区向东部集聚,低—低区向西部集聚的趋势比较明显;④总体上吉林省人口老龄化空间的结构化分异较为明显,随机性因子引起的空间异质性程度较弱,人口老龄化的空间差异主要体现在东—西方向上;⑤出生率始终是影响吉林省人口老龄化的主要因素,迁出率对人口老龄化发展起关键作用,人均GDP年对人口老龄化呈负相关,城市化率对人口老龄化的推动作用愈发显著。     

江苏省流动人口城—镇—乡分布的时空分异与影响因素

以江苏省为研究区,探索了2010—2020年间流动人口在城、镇和乡（即城区、镇区和乡村3类地域类型）分布的时空分异和影响因素。研究发现：① 江苏省城和镇流动人口占常住人口的比例普遍增加,但镇的增幅明显高于城,乡的流动人口占比却在近一半的市县出现减少。② 江苏省流动人口苏南—苏中—苏北分布的梯度格局尚未发生根本性改变,苏南仍是吸引流动人口的高地,但苏中和苏北的镇对省内流动人口的吸引显著增强。③ 苏南一些乡和苏中、苏北一些镇流动人口占比有明显提升,体现了乡镇对流动人口的集聚能力。④ 模型结果显示,政府社会投入的增加有利于增强城、镇、乡对流动人口的吸引力;城镇居民恩格尔系数在城和镇对两类流动人口占比的影响有所不同;产业的影响因城、镇和乡的不同而分异：工业在城、镇、乡均为正向影响;产业结构的高级化在城镇仅对省内流动人口占比具有正向作用;农林牧渔业在乡为负向影响。     

面插值的研究进展

空间插值从广义上讲包括点插值和面插值 ,本文从有无辅助数据的角度介绍了还不为多数人熟知的面插值 ,在无辅助数据的方法中介绍了基于点的面插值法、面域比重插值法、使用控制区的面域比重法、Pycnophylactic面插值法等的算法和特点 ,在有辅助数据的方法中介绍了 EM算法和遥感作为辅助数据的面插值法 ,并介绍了面插值中的特例 -统计数据的空间化方法。     

2000-2010年广东省流入人口时空差异特征及影响因素

广东省自改革开放以来一直是中国流入人口大省,2010 年全国第六次人口普查数据显示,广东省省外流入人口达21497787 人,占广东省总人口的20.61%,在全国省、自治区、直辖市中居第一位。本文以全国第五次、第六次人口普查数据为数据源,选取县级行政单位为分析单元,通过GIS空间分析方法,力图找出广东省流入人口的空间分布格局及时空变化特征,并结合社会经济统计数据和统计降维的技术手段,说明广东省流入人口规模与社会经济发展之间的响应关系,讨论广东省流入人口与当地产业政策调整之间的关系。研究表明:①流入人口数量增多,但分布格局总体稳定,流入人口的聚集度略有下降;②流入人口规模与经济发展关系密切,但分布格局与当地产业转移政策关系明显。     

Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

Journal of Geographical Sciences - Population migration, especially population inflow from epidemic areas, is a key source of the risk related to the coronavirus disease 2019 (COVID-19) epidemic....     

近25 a甘肃省人口分布的时空格局及影响因素

人口分布是在一定时间、一定地域范围人口在空间上的表现,它会受社会、经济、自然和政策等多种因素影响,甘肃省人口分布处于不平衡状态。以甘肃省86个县域及嘉峪关市共87个研究单元为对象,常住人口数据为基础,对研究区1990—2015年之间人口的分布特征、密度变化、分布结构指数、基尼系数、重心迁移等时空格局变化进行了分析。以县域为研究单元,选取12个自然和社会经济因素,利用偏最小二乘法(PLS)对影响甘肃省人口分布空间格局的因素进行分析。研究发现:(1)近25 a来甘肃省人口空间分布差异显著,形成"东南密集西北稀疏"格局;(2)从人口密度变化来看,相较于1990年,2015年甘肃省87个研究单元中,有67个县域属于增加类型,20个县域属于降低类型;(3)全省人口分布的均衡性发展态势在增强;(4)人口分布的重心逐渐向西北方向迁移;(5)甘肃省人口分布格局的形成受自然因素与社会经济因素的同时影响,但社会经济因素影响最大。     

利用空间插值法估算云覆盖像元地表温度的可行性研究

云覆盖是热红外遥感应用和地表温度遥感定量反演的重要障碍。实验选取河北衡水地区Landsat TM影像,借助GIS的空间分析功能,探索运用反距离加权(IDW)和普通克里金插值(OK)方法近似估算3种典型地表类型(水面、城镇、农田)下的云覆盖像元地表温度的可行性。实验结果显示:当云覆盖区域达15 000个像元(约等于13.5km2)时,两种空间插值方法对城镇的云覆盖像元地表温度估算误差都为1.6°C左右,而农田和水面的估算误差在1°C左右(且水面估算误差小于农田),这与3种典型地表类型的地表温度空间异质性密切相关。因此,当Landsat TM图像中有少块云覆盖而发生地表温度值缺失时,利用空间插值法估算云覆盖像元的地表温度是切实可行的。     

1736-1911年中国水灾多发区分布及空间迁移特征

重建历史时期极端气候灾害的时空格局,对于认识当前和未来的灾害演变趋势,辨识灾害高风险区,更好地应对气候变化的挑战具有重要意义。本文基于《清史·灾赈志》中的历史水灾记录,以县级政区为单元,逐年提取了1736-1911年间中国境内的8582个水灾发生地点;利用核密度估计法对这一时期及3个特征时段(1736-1795、1796-1850和1851-1911年)的水灾空间分布特征进行分析,并结合风险理论框架讨论水灾多发区的分布及迁移的影响因素。主要结论如下：①1736-1911年水灾多发区主要集中在华北平原北部的海河、黄河下游,淮河下游,长江中下游三大平原及沿江地带,与现代分布格局存在一定差异;②华北平原北部是清代水灾最为集中的区域,这与当时华北平原降水偏多有关,而进入19世纪后,当地社会经济系统的高脆弱度也大大加重了灾情;特别是1855年黄河改道后,因政府应对不力,使得1851-1911年间鲁西北地区沿黄河下游河道出现一个条带状水灾多发区;③19世纪长江中下游地区水灾频次激增,其原因除梅雨变化导致的极端降水事件增多外,人类不合理的农业开发活动大量挤占湿地和水体,也在相当程度上增大了当地面对水灾时的物理暴露度。     

土壤质量时空变异及其与环境因子的时空关系

土壤质量能体现自然因素与人类活动对土地的影响,深入认识土壤质量的时空变异与环境因子的关系对 土地质量改善与土地可持续利用具有重要意义。土壤质量的时空变异是指在一定的景观内,不同时间不同地点不 同土层的土壤特征存在明显的差异性和多样性,由确定性(Deterministic)和随机性(Stochastic)两大组分构成。土壤 质量时空变异是由多重尺度上的土地利用(植被)、气象(降雨)、地形、土壤、人为活动等多因子综合作用的结果,但 是就某一具体地区而言存在重点尺度和主控因子,土壤质量时空变异的重点尺度与主控因子的时空关系因时间、 空间和尺度而异。本文首先介绍了土壤质量时空变异的概念与内涵,重点综述了土壤理化特性(尤其是黄土高原地 区)的时空变异及其与环境因子时空关系的研究进展,探讨了土壤质量时空变异的发展趋势,以期对我国土壤质量 的时空变异研究有所启迪与帮助。     

Spatiotemporal data model for network time geographic analysis in the era of big data

There has been a resurgence of interest in time geography studies due to emerging spatiotemporal big data in urban environments. However, the rapid increase in the volume, diversity, and intensity of spatiotemporal data poses a significant challenge with respect to the representation and computation of time geographic entities and relations in road networks. To address this challenge, a spatiotemporal data model is proposed in this article. The proposed spatiotemporal data model is based on a compressed linear reference (CLR) technique to transform network time geographic entities in three-dimensional (3D) (x, y, t) space to two-dimensional (2D) CLR space. Using the proposed spatiotemporal data model, network time geographic entities can be stored and managed in classical spatial databases. Efficient spatial operations and index structures can be directly utilized to implement spatiotemporal operations and queries for network time geographic entities in CLR space. To validate the proposed spatiotemporal data model, a prototype system is developed using existing 2D GIS techniques. A case study is performed using large-scale datasets of space-time paths and prisms. The case study indicates that the proposed spatiotemporal data model is effective and efficient for storing, managing, and querying large-scale datasets of network time geographic entities.     

Measuring Potential Access to Primary Healthcare Services: The Influence of Alternative Spatial Representations of Population

Spatial accessibility measures are an important policy tool for managing healthcare provision and reducing health inequality. The two‐step floating catchment area technique, in common with many alternative methodologies, requires that demand‐side population be estimated using spatial interpolation techniques. This article studies the implications of adopting differing spatial representations of population on healthcare accessibility modeling outcomes. Results indicate that a dasymetric model yields lower accessibility scores than a standard pro rata model. More important, the difference is spatially disproportionate, suggesting that the degree of disadvantage experienced in rural areas may be greater than has previously been recognized.