Data-enriched interpolation for temporally consistent population compositions

This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18–65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990–2010 instead of 2000–2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections.     

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.     

Estimating Small-Area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age‐sex proportion surfaces are separately estimated. Age‐sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, and a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age‐sex proportion surfaces. To verify the model, age‐sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup‐count types of variables from information in existing administrative areas for custom‐defined areas used as the spatial basis of support in other applications.     

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.     

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 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 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.     

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.     

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。     

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。     

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。     

Modeling Ambiguity in Census Microdata Allocations to Improve Demographic Small Area Estimates

This article describes a methodology for allocating demographic microdata to small enumeration areas such as census tracts, in the presence of underlying ambiguities. Maximum Entropy methods impute population weights that are constrained to match a set of census tract‐level summary statistics. Once allocated, the household characteristics are summarized to revise estimates of tract‐level demographic summary statistics, and to derive measures of ambiguity. The revised summary statistics are compared with original tract summaries within a context of expected variation. Allocation ambiguity is quantified for each household as a function of the distribution of imputed sample weights over all census tracts, and by computed metrics of confusion and variety of allocation to any census tract. The process reported here allows differentiation of households with regard to inherent ambiguity in the allocation decision. Ambiguity assessment represents an important component that has been neglected in spatial allocation work to date but can be seen as important additional knowledge for demographers and users of small area estimates. For the majority of tested variables, the revised tract level summaries correlate highly with original tract summary statistics. In addition to assessments for individual households, it is also possible to compute average allocation ambiguity for individual tracts, and to associate this with demographic characteristics not utilized in the allocation process.     

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.     

Interpolating Spatial Interaction Data1

Spatial interpolation has been widely used to improve the spatial granularity of data, or to mediate between inconsistent zoning schemes of spatial data. Traditional areal interpolation methods translate values of source zones to those of target zones. These methods have difficulty in dealing with flow data, as each instance is associated with a pair of zones. This study develops a new concept, flow line interpolation, to fill the abovementioned gap. We also develop a first flow line interpolation method to estimate commuting flow data between spatial units in a target zoning scheme based on such data in a source zoning scheme. Three models (i.e., areal‐weighted, intelligent, and gravity‐type flow line interpolation) are presented. To test the estimation accuracy and the application potential of these models, a case study of Fulton County in Georgia is conducted. The results reveal that both the areal‐weighted and intelligent models are very promising flow line interpolation methods.     

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     

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.     

Inferring Spatial Scale Change in an Isopleth Map

This study reunites areal interpolation with the isopleth mapping process to construct an inferred larger scale isopleth map. Intelligent areal interpolation is used to construct two types of population density surfaces that are used as inputs for pycnophylactic interpolation of an isopleth surface. One is a target zone population density surface (TZPDS) and the other is a control zone population density surface (CZPDS). Results suggest that an inferred isopleth map with remote sensing control data is a better surface depiction than an isopleth map without any control data, and the quality of such an isopleth map is further improved by enhancing the remote sensing data with residential parcel information. A CZPDS-derived intelligent isopleth map also has more peaks and variations in population distribution patterns than does a TZPDS-derived one due to the larger scale of the control data.     

Analysis of population dynamics using satellite remote sensing and US census data

The population dynamics from 1991 to 2006 for the seven-county Twin Cities Metropolitan Area (TCMA), Minnesota, USA, was analysed in this study. Per cent impervious surface areas (%ISA) for 1991, 1999 and 2006 were derived from Landsat Thematic Mapper (TM) images and were modified using two different masking methods. The modified %ISA images of 1991 and 1999 were correlated with 1990 and 2000 census block group data of the ‘two highly developed counties’, ‘five suburban counties’ and ‘all seven counties.’ Populations of both years were then modelled, assessed and compared. Next, the statistical models based on the 1999 %ISA and 2000 census data were applied to the 2006 residential %ISA image to estimate the 2006 population. These 2006 estimates were compared with census county-level population projections for 2006. In comparison to Method A, which uses ‘adjusted %ISA images’ by masking out highway centrelines and areas that have greater than 75% imperviousness, Method B based on ‘pure residential %ISA image’ has higher coefficient of determination (R ²) and much lower, consistent mean absolute relative errors (MARE). For both methods, the strongest R ² and lowest MARE values between modelled population density and true density were found in the five-county model, followed by the seven-county model. The two-county model ranks last in terms of model performance for both years. In general, populations for the two highly developed counties were underestimated whereas the opposite was true for the five suburban counties. Population was most accurately estimated based on data from counties with the same or similar characteristics. By comparing the 1990/1991 and 1999/2000 models, we also found that the rate of population density per unit of impervious surface declined from 1991 to 1999. High accuracy was achieved when applying the 1999/2000 model to predict the 2006 population, suggesting that the relationship between per cent imperviousness and population density were relatively stable between 1999 and 2006.     

Automatic Region Building for Spatial Analysis

High‐resolution spatial data have become increasingly available with modern data collection techniques and efforts. However, it is often inappropriate to use the default geographic units to perform spatial analysis due to unstable estimates with small areas (e.g. cancer rates for census blocks or tracts). Regionalization is aggregating small units into relatively larger areas while optimizing a homogeneity measure (such as the sum of squared differences). For exploratory spatial analysis, regionalization may help remove spurious data variation through aggregation and discover hidden patterns in data (such as areas of unusually high cancer rates). Towards this goal, this research introduces several improvements to a recent group of regionalization methods – REDCAP ( Guo 2008 ) and conducts evaluation experiments with synthetic data sets to assess and compare the capability of regionalization methods for exploratory spatial analysis. One of the major improvements is the integration of a local empirical Bayes smoother (EBS) with the regionalization methods. We generate a large number of synthetic data sets with controlled spatial patterns to evaluate the performance of both new and existing methods. Evaluation results show that the new methods (integrated with EBS) perform significantly better than their original versions and other methods (including the EBS method on its own) in terms of detecting the true patterns in the synthetic data sets.     

Using remotely sensed data for census surveys and population estimation in developing countries: examples from Nigeria

"Using examples from Nigeria, this paper demonstrates how remotely sensed data can be used to acquire some of the basic data requirements for census surveys and to estimate population. The result obtained shows that visual identification of settlements on Landsat MSS and TM is more accurate and economical than equivalent digital classification techniques. Black and white aerial photographs were used to estimate the population of a model town and to establish EAs [enumeration areas]. The population estimation method employed can be used to obtain intercensal population estimates for the rapidly growing central places, while the established EAs for the study area have created a permanent base for future census surveys and census cross-validation, population estimation and other social surveys."