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.     

Measuring Demographic Change: The Split Tract Problem

Census tracts in rapidly growing areas often are split into two or more tracts between decennial censuses. The mismatch of tract boundaries between decades poses difficult problems for those researchers interested in measuring demographic change in small areas. The most common solution is to make tracts in both years conform to the earlier boundaries-data from the split tracts are aggregated. An alternative is to apportion data for the earlier year on the basis of land area. A third method estimates the distribution of data for the earlier year by the proportion of population later living in the various split tracts. Comparison of these three methods suggests that, in typical situations, the latter method results in less overall error.     

Error‐sensitive historical GIS: Identifying areal interpolation errors in time‐series data

Historical GIS has the potential to re‐invigorate our use of statistics from historical censuses and related sources. In particular, areal interpolation can be used to create long‐run time‐series of spatially detailed data that will enable us to enhance significantly our understanding of geographical change over periods of a century or more. The difficulty with areal interpolation, however, is that the data that it generates are estimates which will inevitably contain some error. This paper describes a technique that allows the automated identification of possible errors at the level of the individual data values.     

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.     

Mapping urban risk: Flood hazards,race, & environmental justice in New York

This paper demonstrates the importance of disaggregating population data aggregated by census tracts or other units, for more realistic population distribution/location. A newly developed mapping method, the Cadastral-based Expert Dasymetric System (CEDS), calculates population in hyper-heterogeneous urban areas better than traditional mapping techniques. A case study estimating population potentially impacted by flood hazard in New York City compares the impacted population determined by CEDS with that derived by centroid-containment method and filtered areal-weighting interpolation. Compared to CEDS, 37% and 72% fewer people are estimated to be at risk from floods city-wide, using conventional areal weighting of census data, and centroid-containment selection, respectively. Undercounting of impacted population could have serious implications for emergency management and disaster planning. Ethnic/racial populations are also spatially disaggregated to determine any environmental justice impacts with flood risk. Minorities are disproportionately undercounted using traditional methods. Underestimating more vulnerable sub-populations impairs preparedness and relief efforts.     

Research Note—A New Method for Mapping Population and Understanding the Spatial Dynamics of Disease in Urban Areas: Asthma in the Bronx,New York

Determining an accurate depiction of population distribution for urban areas in order to develop an improved "denominator" is important for the calculation of higher-precision rates in GIS analyses, particularly when exploring the spatial dynamics of disease. Rather than using data aggregated by arbitrary administrative boundaries such as census tracts, we developed the Cadastral-Based Expert Dasymetric System (CEDS), an interpolation method using ancillary information to delineate areas of homogeneous values. This method uses cadastral data, land-use filters, modeling by expert system routines, and validation against various census enumeration units and other data. The CEDS method is presented through a case study of asthma hospitalizations in the borough of the Bronx in New York City, in relation to proximity buffers constructed around major sources of air pollution. The analysis using CEDS shows that asthma hospitalization risk due to proximity to pollution sources is greater than previously calculated using traditional disaggregation methods.     

圣保罗大都市区社会空间分异研究——多元统计方法在城市连绵区的应用

空间数据和地理信息系统在城市规划和决策中应用的重要性日见凸显。主要原因在于：重要的人口数据和社会变动经常表现出一定的空间特性,这种特性可以通过空间分析和统计方法被认识和解释。应用多元分析的空间分类方法编制圣保罗大都市区社会分异地图并进行相关分析。研究的主要数据来自2000年巴西全国人口普查,其中包括了圣保罗大都市的所有行政区和39个自治市的21774个人口普查区。为了把都市连绵区从数据全集中分离出来,我们采用混合技术进行互补分析,即在2000年4月30日的陆地卫星7号图像中绘制一个个多边形,这些被识别出来的多边形就是人口普查区。然后,通过目视解译出假彩色多边形集合。应用空间分类评分程序将这些多边形分成五类,并建立人口普查区的数目、覆盖的面积和都市连绵区之间的关系。这种多元分析方法是基于变量的均衡化来生成易于用分级统计图描述平均值,以促进可视化和后续的空间分布分析。基于多元分析的空间分类方法研究,清楚地展现了圣保罗大都市最重要的社会特征,也说明城市社会地图方法和多元分析的空间分类方法在大都市区的管理、公共政策规划和复杂决策中具有重要的应用价值。     

Assessing housing growth when census boundaries change

The US Census provides the primary source of spatially explicit social data, but changing block boundaries complicate analyses of housing growth over time. We compared procedures for reconciling housing density data between 1990 and 2000 census block boundaries in order to assess the sensitivity of analytical methods to estimates of housing growth in Oregon. Estimates of housing growth varied substantially and were sensitive to the method of interpolation. With no processing and areal‐weighted interpolation, more than 35% of the landscape changed; 75–80% of this change was due to decline in housing density. This decline was implausible, however, because housing structures generally persist over time. Based on aggregated boundaries, 11% of the landscape changed, but only 4% experienced a decline in housing density. Nevertheless, the housing density change map was almost twice as coarse spatially as the 2000 housing density data. We also applied a dasymetric approach to redistribute 1990 housing data into 2000 census boundaries under the assumption that the distribution of housing in 2000 reflected the same distribution as in 1990. The dasymetric approach resulted in conservative change estimates at a fine resolution. All methods involved some type of trade‐off (e.g. analytical difficulty, data resolution, magnitude or bias in direction of change). However, our dasymetric procedure is a novel approach for assessing housing growth over changing census boundaries that may be particularly useful because it accounts for the uniquely persistent nature of housing over time.     

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.     

人口统计数据的空间转换

在经济和社会研究中,所要研究的区域之上经常没有数据,而这些数据需要由已知区域的数据求得,即统计数据需要空间转换,这就通常涉及到面积内插。本文从GIS的角度研究如何解决人口内插问题,认为面积内插和GIS中的叠加分析是一致的。在传统的面积内插方法的基础上是提出了基于人口真实分布的面积内插方法,并推导出了公式。同时提出了人口密度的递归算法,即把居住区分为人口稀疏地区和人口稠密地区,估计出人口稀疏地区的人口密度,就可以求出人口密集地区的人口密度;再把人口密集区分为新的人口稀疏区和密集区,此过程反复直至求出接近于人口真实分布的人口模型。     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

Scale effects in food environment research: Implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of South Carolina

Choice of neighborhood scale affects associations between environmental attributes and health-related outcomes. This phenomenon, a part of the modifiable areal unit problem, has been described fully in geography but not as it relates to food environment research. Using two administrative-based geographic boundaries (census tracts and block groups), supermarket geographic measures (density, cumulative opportunity and distance to nearest) were created to examine differences by scale and associations between three common U.S. Census–based socioeconomic status (SES) characteristics (median household income, percentage of population living below poverty and percentage of population with at least a high school education) and a summary neighborhood SES z-score in an eight-county region of South Carolina. General linear mixed-models were used. Overall, both supermarket density and cumulative opportunity were higher when using census tract boundaries compared to block groups. In analytic models, higher median household income was significantly associated with lower neighborhood supermarket density and lower cumulative opportunity using either the census tract or block group boundaries, and neighborhood poverty was positively associated with supermarket density and cumulative opportunity. Both median household income and percent high school education were positively associated with distance to nearest supermarket using either boundary definition, whereas neighborhood poverty had an inverse association. Findings from this study support the premise that supermarket measures can differ by choice of geographic scale and can influence associations between measures. Researchers should consider the most appropriate geographic scale carefully when conducting food environment studies.     

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.     

Guest editorial: the 2001 UK census: remarkable resource or bygone legacy of the 'pencil and paper era'?

National censuses are expensive. They are conducted infrequently. They collect information that some feel infringes their human rights, and people are required by law to complete them. The outputs are not perfect, and in some situations may be misleading. Some suggest that censuses hark back to a period when regularly collected administrative data were not available. These are some of the views held about national censuses. Why, then, would others argue that they are an essential resource? In this paper, we consider some of the pros and cons of conducting national censuses, before introducing a series of papers that draw on early data available from the 2001 UK census. We argue that these papers, and the wealth of research that will be conducted in the future with 2001 census data, make a strong case for supporting the compulsory collection of personal information about the 'entire' population every ten years.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

Neighbourhoods and area statistics in the post 2001 census era

This paper examines the current interest in neighbourhood-based policy in England and considers the extent to which the output geography developed for the 2001 census helps to meet the need for neighbourhood-based data. The characteristics of the new census output geography are presented, and the difficulties of creating meaningful neighbourhood geographies from statistical reporting units reviewed. The new geography will provide a much improved basis for the consolidation of data from official administrative sources, but additional challenges are also discussed in relation to the future of census data collection and publication.     

Estimating the Accuracy of Geographic Variation in Indigenous Population Counts

The ABS 2006 Post-Enumeration Survey was extended to include a sample of localities from the whole of Australia, thereby providing an estimate of census net undercount reflective of the enumeration in remote Indigenous settlements for the first time. The results revealed substantial undercounting of the Indigenous population in certain jurisdictions. At the same time, census counts in many locations were substantially higher than demographic factors could account for. The analytical and policy issues that arise from this revolve around a simple question: what credence can we give to observed spatial patterns of Indigenous population change? This paper seeks to provide an answer by establishing the spatial relationship between population change and net migration at the small area level. This reveals the detailed geography of census undercount and ‘overcount’ with the former common in remote areas and the latter most evident in regional towns and cities. The findings raise important policy issues about the proper interpretation of Indigenous population change data and the nature of estimates used to determine fiscal resourcing for Indigenous policies and programs.     

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).     

Evaluating representation and scale error in the maximal covering location problem using GIS and intelligent areal interpolation

A common problem in location-allocation modeling is the error associated with the representation and scale of demand. Numerous researchers have investigated aggregation errors associated with using different scaled data, and more recently, error associated with the geographic representation of model objects has also been studied. For covering problems, the validity of using polygon centroid representations of demand has been questioned by researchers, but the alternative has been to assume that demand is uniformly distributed within areal units. The spatial heterogeneity of demand within areal units thus has been modeled using one of two extremes – demand is completely concentrated at one location or demand is uniformly distributed. This article proposes using intelligent areal interpolation and geographic information systems to model the spatial heterogeneity of demand within spatial units when solving the maximal covering location problem. The results are compared against representations that assume demand is either concentrated at centroids or uniformly distributed. Using measures of scale and representation error, preliminary results from the test study indicate that for smaller scale data, representation has a substantial impact on model error whereas at larger scales, model error is not that different for the alternative representations of the distribution of demand within areal units.     

High resolution dasymetric model of U.S demographics with application to spatial distribution of racial diversity

Population and demographic data at high spatial resolution is a valuable resource for supporting planning and management decisions as well as an important input to socio-economic academic studies. Dasymetric modeling has been a standard technique to disaggregate census-aggregated units into raster-based data of higher spatial resolution. Although utility of dasymetric mapping has been demonstrated on local and regional scales, few high resolution large-scale models exist due to their high computational cost. In particular, no publicly available high resolution dasymetric model of population distribution over the entire United States is presently available. In this paper we introduce a 3″ (∼90 m) resolution dasymetric model of demographics over the entire conterminous United States. The major innovation is to disaggregate already existing 30″ (∼1 km) and 7.5″ (∼250 m) SEDAC (Socioeconomic Data and Applications Center) Census 2000 grids instead of the original census block-level data. National Land Cover Dataset (NLCD) 2001 is used as ancillary information. This allows for rapid development of a U.S.-wide model for distribution of population and sixteen other demographic variables. The new model is demonstrated to markedly improve spatial accuracy of SEDAC model. To underscore importance of high spatial resolution demographic information other than total population count we demonstrate how maps of several population characteristics can be fused into a “product” map that illustrates complex social issues. Specifically, we introduce a “diversity” categorical map that informs (at nominal 3″ resolution) about spatial distribution of racial diversity, dominant race, and population density simultaneously. Diversity map is compared to a similar map based on census tracts. High resolution raster map allows study of race-diversity phenomenon on smaller scale, and, outside of major metropolitan areas, revels existence of patterns that cannot be deduced from a tract-based map. The new high resolution population and diversity maps can be explored online using our GeoWeb application DataEye available at http://sil.uc.edu/. Both datasets can be also downloaded from the same website.