Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009

ABSTRACT

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.     

Improving Large Area Population Mapping Using Geotweet Densities

Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo‐located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo‐located tweets in 1x1 km grid cells over a 2‐month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests‐based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media‐derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.     

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.     

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。     

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

ABSTRACT

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods. These advances in urban feature extraction and built-area detection can refine the mapping of human population densities, especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data. However, in these contexts it is unclear how best to use built-area data to disaggregate areal, count-based census data. Here we tested two methods using remotely sensed, built-area land cover data to disaggregate population data. These included simple, areal weighting and more complex statistical models with other ancillary information. Outcomes were assessed across eleven countries, representing different world regions varying in population densities, types of built infrastructure, and environmental characteristics. We found that for seven of 11 countries a Random Forest-based, machine learning approach outperforms simple, binary dasymetric disaggregation into remotely-sensed built areas. For these more complex models there was little evidence to support using any single built land cover input over the rest, and in most cases using more than one built-area data product resulted in higher predictive capacity. We discuss these results and implications for future population modeling approaches.     

From auxiliary data to research prospects,a review of gridded population mapping

Population has significant application value and scientific significance in resource use, public health, public transportation, disaster assessment, and environmental management. However, traditional census data can not show the population density difference within census units. Furthermore, census data are not uniform across countries, and reconciling these differences when using data from multiple countries require considerable effort. Finally, there are scale differences between census and geospatial data (e.g., land use/cover), making data analysis and needed research difficult. These challenges significantly limit the applications of census data. The advent of gridded population mapping (GPM) technology has overcome these challenges. GPM technology has developed rapidly in recent years. The research data and models are rich and diverse, and many achievements have been made. A systematic review of the current state of GPM research will help relevant researchers and data users. This article begins by summarizing the core elements of GPM research in four aspects: auxiliary data, models, accuracy, and products. It will then go on to four problems prevalent in GPM research that have direct or indirect effects on the accuracy of GPM. Finally, the article prospects GPM research from four different aspects based on the current state of research.     

朝鲜人口统计数据空间化模拟及影响因子分析

朝鲜人口统计数据空间化是解决朝鲜统计数据与自然要素数据融合分析的重要途径。基于朝鲜市郡级人口普查数据,将GIS空间分析技术与统计学方法相结合,分析了朝鲜人口密度与空间因子的关系,采用多元回归的方法建立了朝鲜人口密度空间化模型,在GIS平台中实现了朝鲜人口密度的空间格网化模拟,并利用地理探测器对影响朝鲜人口密度空间分布因素的决定力进行了有效探测。结果表明,多元回归模型拟合精度达到0.769,生成的栅格人口密度数据与朝鲜三级行政区人口统计数据保持一致。同时,影响该地区人口密度的因子依次为道路网密度、居民点密度、居民地指数、海拔、坡度和耕地指数。     

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.     

基于多源空间数据的山区人口分布模拟

人口资源是制约山区发展的关键因素,其分布状况在一定程度上反映和决定了山区的资源环境安全状况和社会经济发展水平.在分析川滇黔接壤地区山区人口分布影响因素的基础上结合已有方法,构建了适合山区人口数据空间化的模型.以2007年人口统计数据为基础,以居民点作为人口分布指示因子,利用GIS软件工具,分析了居民点分布与地貌形态、土地利用、道路以及水系间的关系.基于多源空间数据融合的思想,引进了居民点缓冲区的概念,以较客观的赋权方式确定影响因子权重,实现山区人口统计数据的空间化.结果表明,通过融合产生的人口密度与乡镇级人口密度的相关性均在0.80以上,结果可靠,为进一步分析山区人口分布格局提供了重要的基础数据.     

武汉市人口增长与空间分布格局演变

为揭示城镇化背景下武汉市人口时序增长趋势和人口空间分布格局演变特征,本文基于1990年第四次、2000年第五次、2010年第六次全国人口普查和2014年武汉市人口普查数据,运用统计学原理和GIS空间分析方法,分析了武汉市人口增长的时空变化规律;应用人口密度空间分布的数学模型,得到武汉市人口空间分布格局的演变趋势。人口增长研究表明:1990年以后,武汉市人口数量迅速增长,总体上形成了中心聚集、外围扩散的空间发展格局。空间分布研究表明:1)武汉市的人口密度分布更加符合Clark模型,主城区Smeed模型拟合效果更好。近25年来,武汉市城镇化进程迅速,人口向主城区聚集。随着时间发展,主城区逐步呈现初级阶段的郊区化。2)武汉市主城区人口空间分布已经出现多中心化的趋势,并且已经有两个街道发育成为人口次中心。研究结果可为武汉市人口相关决策的制定和城市结构调整提供参考依据,有助于认识我国中部崛起城市的发展规律,为其他城市的发展和规划提供参考和借鉴。     

Spatiotemporal patterns of outdoor artificial nighttime lights exposure in the Republic of Korea between 1995 and 2010

Outdoor light at night (LAN) would be a public concern potentially associated with adverse health outcomes mainly in developed countries, but it might also be an environmental health issue in developing countries. However, there have been few studies reporting the spatial and temporal aspects of LAN level changes incorporated with population exposure in such nations. Therefore, we analyzed the spatiotemporal patterns of outdoor LAN exposure changes across the Republic of Korea (ROK), a developing country in Asia, between 1995 and 2010. In addition, we incorporated the population and its demographic characteristics of light exposure in this research. We utilized Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) observations as a spatial proxy of LAN and the ROK census data. This research observed 57.6% (≈57,800 km²) of land areas in the country to experience increases in outdoor artificial illumination at nights. The highest levels of LAN were found to affect about 58% of the total ROK population with major impacts on young and middle-aged people in recent years. Taking this research into account, it would require taking appropriate measures in controlling and abating outdoor artificial lights at night for both developing and developed countries. We anticipate that those efforts could prevent adverse health outcomes, for example, female breast cancer, potentially associated with LAN exposure.     

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

Accounting for Error When Modelling with Time Series Data: Estimating the Development of Crop Pests Throughout the Year

In order to attach some statement of reliability to mesoscale maps of how pest risk may develop over time, methods were developed to enable the detection and evaluation of errors in predictions that arise from the use of input data series from remote point sources. Firstly, we investigated how predicted model results may differ as a result of the ordering of the spatial interpolation and the model procedures. Principles of logic were used to detect errors occurring in the daily sequences of predicted pest development. Analyses of spatial autocorrelation within the gridded results showed that areas where a pest was predicted to reach a certain stage of development become more fragmented as a model run progressed over time. We identified that the less intensive approach of running a model only at data points and subsequently interpolating these to a grid can, in some cases, result in errors of logic and unrealistic degrees of autocorrelation. These errors occurred particularly when mapping a non-indigenous, marginal, pest at the later stages of its development. As a strategy for error evaluation, deterministic process models were run using point-based estimates of interpolated daily temperature to give RMS data errors at the sample points. This enabled us to investigate how the component of error related to sparsely distributed point data contributed to errors in the gridded estimates of pest development over time. The error detection and evaluation methods outlined are tractable and applicable to a wide variety of cases where point based models running over multiple time steps are extended to provide spatially continuous, landscape-wide, mappable results.     

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.     

山东省人口空间分布模式及其影响因素分析

基于2010年《山东统计年鉴》和全国第六次人口普查数据,本文以县域为研究单元,通过空间自相关分析人口的空间分布模式,运用因子分析和Pearson相关分析探究人口空间分布模式的影响因素。研究结果表明:山东省人口分布呈现"东北-西南"的空间分布模式,人口空间分布整体上呈现显著的空间集聚,主要集中在青岛市的中心城区;经济增长因素和地形条件是山东省人口空间分布模式形成的主要推动力。     

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说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。     

1952年-2010年中国人口分布与社会经济发展的时空耦合特征分析

人口经济的时空耦合态势是中国区域社会经济发展空间均衡过程的重要体现,可有效地指导中国经济布局、民政建设、交通发展和环境管理等相关政策和措施的制定和实施。在地理信息系统（GIS）的支持下,采用人口分布结构指数、人口密度基尼系数、人口经济重心迁移、空间自相关分析等方法,对1935年-2010年6期县级行政单元人口普查统计数据进行空间插值,实现人口密度分界线的空间刻画及其变化趋势分析,结合1952年-2010年省级行政单元人口、经济统计数据,从人口与经济重心空间耦合及其空间一致性两个角度来揭示社会经济与人口分布的时空差异,剖析人口分布与社会经济的联系。得出以下结论：（1）由于社会历史、自然环境和经济条件的影响,中国人口空间分布的时空演变特征不尽相同,东西部差异较大,且不均衡态势日益明显。（2）胡焕庸线至今仍能很好的概括中国人口东南地狭人稠、西北地广人稀的空间分布格局,但是甘肃、宁夏、内蒙古地区的人口分布已经在局部范围内突破了胡焕庸线的限制,中国人口分布在一定程度上呈现向西部转移的趋势。（3）中国人口分布与经济发展空间一致性的地域分异特征十分明显,但省际一致性在不断缩小,人口集聚具有较强的经济导向性。（4）全国和东部、中部、西部和东北部四大区域在1952年-2010年的人口经济一致性程度及其变化趋势各异：全国人口经济空间一致性程度相对较低,东北地区人口经济一致性程度较高且还在改善,东部与中部保持稳定,西部较差且呈减弱趋势特征。     

论空间信息多级格网及其典型应用

探求适合网格计算环境下空间信息多级格网SIMG(spatialinformationmulti grid)的新概念及其表达的新方法 ,阐述了SIMG潜在的三大职能 ,剖析了SIMG面临的诸多挑战 ,研究了SIMG的技术体系结构 ,并以国家人口统计和经济数据统计为目标 ,设计一个带空间信息编码的人口普查多级格网 ,用于研究SIMG的应用设计方案 ,力求为空间信息的共享和服务找到一种有效的新途径     

人口信息空间可视化系统设计研究

人口问题始终是我国乃至世界关注的重大社会发展问题,因此将人口信息进行空间化总结和表达,并对这些信息进行深层次开发和应用就显得尤为重要。本文探讨了人口信息空间可视化的必要性和重要性及人口信息空间可视化系统设计中的关键科学与技术问题,提出了人口信息空间可视化系统设计框架、系统功能模块和数据库结构。结合第五次人口普查信息设计和制作了《中华人民共和国人口电子地图集》,图集提供1 0个图组共计近2 0 0幅地图和近1 0 0 0个分省、分地区和分县人口指标以及强大的系统功能,形成交互的人口空间认知的可视化环境,形象地表达了人口的地区差异、分布特征;揭示了人口要素间的相互关系以及与区域各种要素制约关系,提高对我国人口问题的空间认知水平。     

顾及城乡差异的大区域人口密度估算——以山东省为例

现有大区域人口密度估算结果大多是在千米级尺度上,仅能宏观地反映城乡人口分布的范围,无法准确地刻画城乡人口空间分布的细节特征。本文将首套30m全球地表覆盖数据(GlobeLand30)引入城乡人口密度估算中,基于实现城乡划分的GlobeLand30人造地表数据,在城镇区域运用夜间灯光强度与人口的相关性将城镇人口细划到30m尺度上来估算城镇人口密度;在乡村区域引入样方估算的方法修正乡村居民地面积以估算乡村人口密度。以山东省为试验区的研究表明,本文方法无论在城乡居民地刻画还是人口空间分布的表达上均优于参考数据,所使用的GlobeLand30的全球性也保证了该方法推广的可行性。