Areal Interpolation Research Articles

Dasymetric Mapping for Estimating Population in Small Areas

AbstractA dasymetric map depicts a statistical surface, most commonly population density, as a set of simply connected regions, such that variation within each region is minimized and the region boundaries approximate the steepest escarpments of the surface. Dasymetric mapping has its roots in early thematic mapping of population, but has recently been taken up by researchers focusing on areal interpolation and population estimation using remote sensing. The process of dasymetric mapping typically involves the disaggregation of population data encoded in choropleth map form using an ancillary spatial data set, most commonly either an area‐class map or satellite image. The functional relationship between the ancillary data and the statistical surface being mapped may be specified a priori by the researcher or estimated using a variety of statistical techniques. Challenges facing dasymetric mapping research include handling spatio‐temporal data and the development of standardized and accessible methods.

A Neural Network-based Method for Solving “Nested Hierarchy” Areal Interpolation Problems

This study proposes a neural network approach to solving areal interpolation scenarios, specifically the “nested hierarchy” problem. The neural network method presented adopts the approach taken by intelligent interpolation methods where ancillary spatial information is presented to assist in achieving more accurate results. For this study, the data to be estimated are total populations for census tracts and block groups in Hartford County, Connecticut. A number of neural network models are generated containing various combinations of ancillary spatial information. The neural-network-derived predictions are compared with the predicted populations derived from three existing interpolation methods: areal weighting, a dasymetric areal weighting approach using remote sensing data, and ordinary least squares (OLS) regression. For each scenario presented, the proposed neural network approach outperforms each of the existing methods.

The accuracy assessment in areal interpolation: An empirical investigation

Areal interpolation is the process of transferring data from source zones to target zones. While method development remains a top research priority in areal interpolation, the accuracy assessment aspect also begs for attention. This paper reports an empirical experience on probing an areal interpolation method to highlight the power and potential pitfalls in accuracy assessment. A kriging-based interpolation algorithm is evaluated by several approaches. It is found that accuracy assessment is a powerful tool to understand an interpolation method, e.g. the utility of ancillary data and semi-variogram modeling in kriging in our case study. However, different assessment methods and spatial units on which assessment is conducted can lead to rather different results. The typical practice to assess accuracy at the source zone level may overestimate interpolation accuracy. Assessment at the target zone level is suggested as a supplement.

Population‐density estimation using regression and area‐to‐point residual kriging

Census population data are associated with several analytical and cartographic problems. Regression models using remote‐sensing covariates have been examined to estimate urban population density, but the performance may not be satisfactory. This paper describes a kriging‐based areal interpolation method, namely area‐to‐point residual kriging, which can be used to disaggregate the residuals remaining from regression. Compared with conventional cokriging, the area‐to‐point residual kriging is much simpler in that only a semivariogram model for the point residuals is required, as opposed to a set of auto‐ and cross‐semivariogram models involving the dependent variable and all the covariates. In addition, area‐to‐point residual kriging explicitly accounts for any scale differences between source data and target values. The method is illustrated by disaggregating population from census units to the land‐use zones within them. Comparative results for regression with and without area‐to‐point residual kriging show that area‐to‐point residual kriging can substantially improve interpolation accuracy.

Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices

AbstractDrought monitoring is an essential component of drought risk management. Drought indices—functions of precipitation showing the severity of dryness during a particular time period—are often used for monitoring purposes. These indices may only be calculated originally at a limited number of sites where observations on climate variables are available. However, what is required for monitoring is to estimate the spatial distribution of drought severity over larger areas in the form of maps. In this article, several geostatistical methods including kriging, co‐kriging and thin plate smoothing splines with and without secondary variables, as well as Thiessen polygons and weighted moving average were assessed for the derivation of maps of drought indices. The techniques are evaluated using 1‐month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI) at 43 climatic stations in the Tehran province of Iran. The results indicate that although kriging is the most accurate method, weighted moving average provides a reasonable accuracy combined with the simplicity and speed of the procedure, and thus, can be recommended for operational drought monitoring system in the region. Copyright © 2008 Royal Meteorological Society

Areal Interpolation of Population Counts Using Pre-classified Land Cover Data

The need to combine spatial data representing sociodemographic information across incompatible spatial units is a common problem for demographers. A particular concern is computing small area trends when aggregation zone boundaries change during the trend interval. To that end, this study provides an example of dasymetric areal interpolation using the pre-classified land cover data available through the US Geological Survey’s National Land Cover Dataset (NLCD) program. Areal interpolation of population estimates is preferable to traditional reaggregation techniques, and the use of land cover data as a weighting factor in interpolated estimation has been shown in earlier studies to be highly accurate. In this study, the NLCD data set performs well and, because it requires no classification, it compares favorably with other land cover data sets for areal interpolation when considered on the basis of accuracy, precision and ease of use.

Integrating the maximum capture problem into a GIS framework

This paper presents a methodology for reformulating the maximal capture problem by using the data representation and manipulation capabilities of GIS to define: (1) the coverage region captured by each potential facility, and (2) each unique demand region covered by a specific combination of potential facilities. The formulation is modeled on the maximum covering problem although the integer restriction on the demand capture variables is relaxed. Because demand regions are not exogenously given, areal interpolation is used to estimate the demand associated with each of these regions The model is used to determine the location on a network for a set of home improvement stores that are hypothetically in competition with existing Home Depot stores in Southeastern New Hampshire.

Target‐Density Weighting Interpolation and Uncertainty Evaluation for Temporal Analysis of Census Data

Conducting temporal analysis of census data often requires applying areal interpolation to integrate data that have been spatially aggregated using incompatible zoning systems. This article introduces a method of areal interpolation, target‐density weighting (TDW), that is useful for long‐term temporal analysis because it requires only readily available historical data and basic geographic information system operations. Then, through regression analysis of a large sample of U.S. census tract data, a model is produced that relates the error in TDW estimates of tract population to four basic properties of tracts. An analysis of model residuals combined with theorized absolute limits on interpolation error yields formulas with which we can compute upper and lower prediction bounds on the population in a tract of one census at the time of a different census. These prediction intervals enable the interpretation of different interpolated estimates with appropriately varying degrees of uncertainty.

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.

Rapid facilitation of dasymetric-based population interpolation by means of raster pixel maps

Areal interpolation between one partitioning of geographical space and another remains an important topic, particular in terms of population counts and related statistics which are often required in order to compute an incidence ratio. Despite numerous recent developments in intelligent areal interpolation methods, and studies that have demonstrated their clear advantage over simple areal weighting, there is little evidence to suggest widespread usage amongst the GIS user community. It is argued that to encourage greater uptake such methods must offer simplicity and convenience. Areal interpolation based on binary dasymetric mapping is conceptually simple, but examples to date tend to use information extracted from multi-spectral satellite imagery which limits its perceived convenience. This paper examines a simple method to extract equivalent information from a raster pixel map. It is shown to offer comparable areal interpolation performance at considerably less cost in terms of both time and complexity.

A Framework for Interpolating the Population Surface at the Residential-Housing-Unit Level

Population surface information is essential for a broad array of geographical studies. Many areal interpolation methods have been developed for creating population surface data from decennial census data. This paper describes a new framework to interpolate population to the lowest spatial level, i.e., housing units, based on high-resolution geospatial imagery. The framework is fundamentally a dasymetric mapping process, comprising: (1) determination of the basic dasymetric unit; (2) extracting the basic units; and (3) allocating population counts to each unit. An example was carried out to illustrate the promises and challenges of implementing such a framework using DOQQ, LIDAR, and parcel data.

Character and causes of population distribution in Shenyang city, China

Character of population distribution is one of the focuses studied by urban geography. Using the fifth national census data as basic data and using areal interpolation method, this paper analyzes character of urban population distribution of Shenyang City, Northeast China, in terms of three aspects of statistical character, spatial auto-correlation and spatial structure. Furthermore, this research analyzes the factors affecting the population distribution of the city. The main conclusions include: 1) There is an obvious structure character of population distribution in the grid with a grain of 300m, which is appropriate scale when researching population distribution in Shenyang City. 2) Urban population distribution has the character of assembling while population density distribution takes on variability in Shenyang City. 3) Population density distribution shows spatial auto-correlation within 7.36km. Spatial heterogeneity of population density is low. 4) Urban center, population distribution barycenter and population density maximum points separate each other. Population density distribution has multi-cores character. 5) Layout of governments, primary schools, middle schools, colleges, hospitals and marketplaces affects population distribution directly. With the increase of distance to these factors, population density decreases as logarithm.

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.

Spatial Database Development for Russian Urban Areas: A New Conceptual Framework

Existing data and conceptual frameworks for investigating spatial patterns of urban growth within and among Russia's largest cities obscure contemporary growth. We propose a new conceptual framework for urban spatial data development that utilizes remote sensing, GIS, and external data to infer socioeconomic, ecological, and infrastructure data. Capturing urban growth patterns requires using multispectral (MS) land-use classifications, dasymetric mapping, areal interpolation, and visual interpretation of MS and panchromatic images. Employing these same methods to analyze rural and urban districts (rayons) allows development of the first compatible datasets for all urban administrative units, improving investigative capabilities because all data are aggregated at the same level.

Population Estimation Methods in GIS and Remote Sensing: A Review

This paper reviews existing population estimation methods in the GIS and remote sensing literatures. The methods can be grouped into two categories: areal interpolation methods and statistical modeling methods. Areal interpolation methods can be further separated into two categories depending on whether ancillary information is used. Statistical modeling methods can be further grouped into five categories based on the relationship between populations and urban areas, land uses, dwelling units, image pixel characteristics, or other physical or socioeconomic characteristics.

Breaking the Boundaries: Geographical Approaches to Integrating 200 Years of the Census

SummaryThe census and similar sources of data have been published for two centuries so the information that they contain should provide an unparalleled insight into the changing population of Britain over this time period. To date, however, the seemingly trivial problem of changes in boundaries has seriously hampered the use of these sources as they make it impossible to create long run time series of spatially detailed data. The paper reviews methodologies that attempt to resolve this problem by using geographical information systems and areal inter-polation to allow the reallocation of data from one set of administrative units onto another. This makes it possible to examine change over time for a standard geography and thus it becomes possible to unlock the spatial detail and the temporal depth that are held in the census and in related sources.

Benthic carbon mineralization on a global scale

In this study we present a global distribution pattern and budget of the minimum flux of particulate organic carbon to the sea floor (JPOCα). The estimations are based on regionally specific correlations between the diffusive oxygen flux across the sediment‐water interface, the total organic carbon content in surface sediments, and the oxygen concentration in bottom waters. For this, we modified the principal equation of Cai and Reimers [1995] as a basic monod reaction rate, applied within 11 regions where in situ measurements of diffusive oxygen uptake exist. By application of the resulting transfer functions to other regions with similar sedimentary conditions and areal interpolation, we calculated a minimum global budget of particulate organic carbon that actually reaches the sea floor of ∼0.5 GtC yr−1 (>1000 m water depth (wd)), whereas approximately 0.002–0.12 GtC yr−1 is buried in the sediments (0.01–0.4% of surface primary production). Despite the fact that our global budget is in good agreement with previous studies, we found conspicuous differences among the distribution patterns of primary production, calculations based on particle trap collections of the POC flux, and JPOCα of this study. These deviations, especially located at the southeastern and southwestern Atlantic Ocean, the Greenland and Norwegian Sea and the entire equatorial Pacific Ocean, strongly indicate a considerable influence of lateral particle transport on the vertical link between surface waters and underlying sediments. This observation is supported by sediment trap data. Furthermore, local differences in the availability and quality of the organic matter as well as different transport mechanisms through the water column are discussed.

Street-Weighted Interpolation Techniques for Demographic Count Estimation in Incompatible Zone Systems

Data processing for the spatial analysis of small-area social, demographic, and economic data often requires the combination of data spatially aggregated to two or more incompatible zone systems in a region, such as a set of enumeration districts that changes over time. Such situations can be addressed by areal interpolation—the transfer of data between zonal systems according to spatial algorithms. The authors test a technique of areal interpolation using geographic information systems (GIS) that employs a digital map layer representing streets and roads to derive varying density weights for small areas within aggregation zones. The technique reduces errors in estimation compared with estimates derived using the commonly applied area-weighting technique, with its assumption of uniform density. The street-weighting technique is much easier to use than other interpolation techniques that have also been shown to reduce error compared with area-based weighting.

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.

Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method

Interpolating population data between incompatible spatial zones is an important task in many GIS applications. This paper investigates whether regional regression models between population and land cover outperform a global approach, and whether the 3-class dasymetric method improves upon the binary dasymetric approach. In the experiments conducted, regional regressions resulted in better areal interpolation, but also highlighted spatial non-stationarity in the relationship between population and land cover. The benefits of a 3-class dasymetric model over a binary model were inconclusive. However, it is suggested that greater flexibility in model calibration to more fully incorporate spatial non-stationarity could improve 3-class dasymetric performance. Accurate urban residential density mapping is also important since the 3-class dasymetric method seems less robust than the binary approach to land cover classification error.