Exploring nationally and regionally defined models for large area population mapping

Interactions between humans, diseases, and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation datasets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.     

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.     

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.     

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.     

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.     

Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince,Haiti

This research uses the most recent (2003) census data and a Landsat ETM+ image to build a population estimation model for Port-au-Prince, Haiti. The purpose of the study is to establish the linkage of population density with remotely sensed surface reflectance signals of an urban area, and use that to estimate population when census data are not available in a timely fashion. The research begins with deriving subpixel vegetation-impervious surface-soil (VIS) fractions derived from the Landsat ETM+ multispectral bands, and then uses the geographically weighted regression (GWR) model to examine how the variation of population density can be explained by the VIS variables and their derivatives. With comparison to the ordinary least square (OLS) model, the GWR model accounts for spatial non-stationarity in the relationship between population patterns and land characteristics in the study area. The study reveals that three VIS variables are significant in explaining population density: the mean value of houses fraction image, the mean value of vegetation fraction image, and the standard deviation of vegetation fraction image.     

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.     

Aerial remote sensing as a useful technique to obtain data on urban population

The aerial remote sensing data can be reliable source to obtain an estimate of urban population. A study was carried out in the city of Coimbatore (municipal area) using aerial photoghraphs of scale 1:4000 taken by a metric camera of 300 mm. focal length in the year 1979. The findings obtained by photo-interpretation compared very favourably with the interpolated population census figures for the year 1979, for the municipal area of the city Coimbatore. Dwelling units counted on the aerial photograph for different types of residential areas classified on the basis of size, shape and pattern are the physical indicators of population. Occupancy rate of dwelling units was determined by sampling survey carried out during field visit itself in this particular study.     

城市人口的遥感估算方法

常规人口估算方法耗时、费力、财物消耗大且周期长,况且,仅靠少数年份的人口统计资料也是难以准确揭示人口状况的。尽管应用遥感方法估算人口的历史并不长,精度也还不高,但已显示了其优越性。遥感估算人口的主要原理如下。 (1)美国国防卫星及NOAA卫星的热红外通道图像说明,人口分布及其数量与城市耗能量(人工热源)相关,而耗能量又与图像灰度相关;(2)城市人口的膨胀与城市面积的扩展相关;(3)像片上进行住宅计数,用人口普查资料或抽样调查方法确定每户平均人数则可进行人口估算;4)人口密度大小也反映在人工结构物及自然物体的波谱反射率差异上,据此可建立人口数量与反射率的回归方程。     

Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice

ABSTRACT

Data on global population distribution are a strategic resource currently in high demand in an age of new Development Agendas that call for universal inclusiveness of people. However, quality, detail, and age of census data varies significantly by country and suffers from shortcomings that propagate to derived population grids and their applications. In this work, the improved capabilities of recent remote sensing-derived global settlement data to detect and mitigate major discrepancies with census data is explored. Open layers mapping built-up presence were used to revise census units deemed as ‘unpopulated’ and to harmonize population distribution along coastlines. Automated procedures to detect and mitigate these anomalies, while minimizing changes to census geometry, preserving the regional distribution of population, and the overall counts were developed, tested, and applied. The two procedures employed for the detection of deficiencies in global census data obtained high rates of true positives, after verification and validation. Results also show that the targeted anomalies were significantly mitigated and are encouraging for further uses of free and open geospatial data derived from remote sensing in complementing and improving conventional sources of fundamental population statistics.     

Using Semi-variance Image Texture Statistics to Model Population Densities

This study presents a method to model population densities by using image texture statistics of semi-variance. In a case study of the City of Austin, Texas, we first selected sample census blocks of the same land use to build population models by land use. Regression analyses were conducted to infer the relationship between block population densities and image texture statistics of the semi-variance. We then applied the population models to an area of 251 blocks to estimate populations for within-blocks land-use areas while maintaining census block populations. To assess the proposed method, the same analysis was performed while census block-group populations were maintained, and the aggregated block populations were compared with original census block populations. We also tested a conventional land-use-based dasymetric mapping method with pre-calculated population densities for land uses. The results show that our approach, which is based on initial land-use stratification and further image-texture statistical modeling of population, has higher accuracy statistics than the conventional land-use-based dasymetric mapping method.     

A GIS Approach to Estimation of Building Population for Micro-spatial Analysis

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria.     

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。     

Modelling housing unit density from land cover metrics: a Midwestern US example

Geographic applications frequently require the gathering and analysis of socioeconomic data. For many nations, these data are normally collected through a census. However, during the intercensal period (5–10 years), these data lose their currency and must be updated. The objective of this project was to estimate housing unit density from Landsat ETM+ imagery in the Terre Haute, IN, USA, region. Modelling was done for 1945 census blocks in the study area containing 30 972 housing units. Landtype, as represented by six cluster classes, was used as the primary surrogate for housing unit density. The percentage of each landtype within the census blocks was calculated. Other landscape metrics representing landtype patch dominance and diversity were also calculated on a per-block basis. Housing unit density within the census block was then modelled as a function of those percentages and metrics using discriminant analysis and multiple regression. The simple correlation between the observed and modelled housing unit density was 0.79. The mean residual error produced by the model was 0.37 housing units per hectare.     

Instructions for Contributors to Cartography and Geographic Information Science

A number of areal interpolation methods have been developed to estimate population for overlapping, discontinuous, or fragmented areas. Previous studies examined the relative accuracy of various methods; this research advances those endeavors by comparing the effectiveness of seven different methods using a national random sample of census block groups and blocks. As the results show, the areal interpolation methods produce good population estimates for nested census blocks except in areas of heterogeneous land use or unusual contexts. In addition, estimation conducted in areas with small populations or low population density was vulnerable to high percentage error. Amongst the different methods, road network allocation and statistical regression (with area and roads as predictors) produced the best population estimates for the sample blocks.     

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.     

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.     

Generating population census data through aerial remote sensing

For urban planning and decision making, exact information about population of a specific area is very important. In India, the population census is carried out after every decade by field survey. But to know the inter-censual population, field survey is not very appropriate method because it is not cost and time-effective. Remote sensing can help significantly in generating quick information about housing typology and housing stock, from which population data can be extracted with the help of limited field checks. Therefore, two techniques of estimation of population using remote sensing data have been employed for Saharanpur city as a case study and the results are very encouraging.     

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.     

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.