Area-class Maps Research Articles

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

A Linear Bayesian Updating Model for Probabilistic Spatial Classification

Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classification through an expert system. The main purpose of this paper is to present the solid theoretical foundations of the LBU approach. Since the LBU idea is originated from aggregating expert opinions and is not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, such as remote sensing images or area-class maps.

Harmonisation of the soil map of Africa at the continental scale

In the context of major global environmental challenges such as food security, climate change, fresh water scarcity and biodiversity loss, the protection and the sustainable management of soil resources in Africa are of paramount importance. To raise the awareness of the general public, stakeholders, policy makers and the science community to the importance of soil in Africa, the Joint Research Centre of the European Commission has produced the Soil Atlas of Africa. To that end, a new harmonised soil map at the continental scale has been produced. The steps of the construction of the new area-class map are presented, the basic information being derived from the Harmonized World Soil Database (HWSD). We show how the original data were updated and modified according to the World Reference Base for Soil Resources classification system. The corrections concerned boundary issues, areas with no information, soil patterns, river and drainage networks, and dynamic features such as sand dunes, water bodies and coastlines. In comparison to the initial map derived from HWSD, the new map represents a correction of 13% of the soil data for the continent. The map is available for downloading.

Daysimetric mapping of mercury emissions from contaminated sites

In this abstract we describe the Dasymetric Mapping (DM) technique applied to mercury contaminated sites. The DM is an intelligent disaggregation procedure that incorporates ancillary data to facilitate the areal interpolation process. It differs from choropleth mapping in that areas are defined based on the actual spatial distribution of the variable being mapped, rather than administrative or other arbitrary units. The disaggregation weight was derived from soil carbon content, as it was found that increases in total mercury (and methylmercury) concentrations are related to increasing fractions of organic carbon (OC) in the soil. The DM technique was implemented in ArcGis® by creating a model that uses emissions from contaminated sites, an area-class map, and a sampling strategy set out as binary.

Misclassification error propagation in land cover change categorization

It is important to describe misclassification errors in land cover maps and to quantify their propagation through geo-processing to resultant information products, such as land cover change maps. Geostatistical simulation is widely used in error modeling, as it can generate equal-probable realizations of the fields being considered, which can be summarized to facilitate error propagation analysis. To fix noninvariance in indicator simulation, discriminant space-based methods were proposed to enhance consistency in area-class mapping and replicability in uncertainty modeling, as the former is achieved by imposing means while the latter is ensured by projecting spatio-temporal correlated residuals in discriminant space to geographic space through a mapping process. This paper explores discriminant models for error propagation in land cover change detection, followed by experiments based on bi-temporal remote sensing images. It was found that misclassification error propagation is effectively characterized with discriminant covariate-based stochastic simulation, where spatio-temporal interdependence is taken into account.

Beyond the epsilon band: polygonal modeling of gradation/uncertainty in area-class maps

A spatial modeling technique is proposed to represent boundary uncertainty or gradation on area-class maps using a simple polygon tessellation with designated zones of indeterminacy or transition zones. The transition zone can be conceptualized as a dual of the epsilon band, but is more flexible and allows for a wide range of polygonal configurations, including polygons with sinuous boundaries, spurs, three-way transition zones, and null polygons. The model is specified using the medial axis to capture the general shape characteristics of a transition zone. Graph theoretic representation of an extended version of the medial axis captures key junctions in both shape and classification and is used to identify well-formed transition zones that can be logically and unambiguously handled by the model. A multivariate classification surface is specified by first defining degrees or probabilities of membership at every point on the medial axis and transition zone boundary. Degrees or probabilities of membership at all other points are defined by linear interpolation. The technique is illustrated with an example of a complex transition zone, and a simple isoline representation that can be derived from the model is presented. The proposed modeling technique promises to facilitate expert characterization of soil formations, ecological systems, and other types of areal units where gradation and/or boundary uncertainty are prevalent.

Uncertainty Comparison for Area-Class Maps Concerning Different Reference Data

As the main factor that influences classification quality, uncertainty characterization is analysis of area classes based on remotely sensed imagery and auxiliary data. This study focuses on uncertainty comparison between reference and classification maps. By referring to information classes and data classes respectively, experiment using real data sets was carried out to quantify uncertainty in area-class maps. Contingency tables and an information theory measure of shared information, percentage of average mutual information (%AMI), were applied to compare the uncertainty between pairs of area-class maps, where maximum likelihood classifier was used to classify the image into area-class map with different reference data on the discriminant space. Results show that there exist large impacts of semantic bias in different reference on classification uncertainty. Therefore, further improving upon the effect of bias in reference data will be studied to enable a more accurate assessment of the quality of classification.

Discriminant models for uncertainty characterization in area class change categorization

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps. Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling. As area classes are rarely completely separable in empirically realized discriminant space, where class inseparability becomes more complicated for change categorization, we seek to quantify uncertainty in area classes (and change classes) due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively. Experiments using real datasets were carried out, and a Bayesian method was used to obtain change maps. We found that there are large differences between uncertainty statistics referring to data classes and information classes. Therefore, uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis, enabling quantification of uncertainty due to partially random measurement errors, and systematic categorical discrepancies, respectively.

Quantification and Statistical Analysis of Structural Similarities in Dialectological Area-Class Maps

AbstractDialect atlases comprise considerable numbers of linguistic feature maps, i.e. dialect maps representing one linguistic feature each. Large amounts of data like these are often difficult to handle. This article presents a new quantitative method for the automatic analysis of such large corpora of linguistic feature maps. It makes use of geographical similarities between single maps to establish a system of criteria for structural relatedness. Furthermore, it employs statistical techniques to test whether given linguistic relations between the maps coincide significantly with structural relations. To achieve this, each underlying point-symbol map is converted into an area-class map (with all the original information still available). These area-class maps yield additional information regarding their structural composition. Cluster analysis is then employed to obtain groupings of similar maps. Such groupings facilitate the search for language-internal factors that influence the geographical distribution of linguistic variants, as the relevance of any given linguistic parameter for spatial patterns can be tested using statistical methods. Moreover, language-external factors, such as topographical conditions, can be tested in the same way. Thus, this new method allows for a profound and substantiated investigation of the regularities that can be found in the geographical distributions of linguistic variants.

Knowledge Discovery from Area–Class Resource Maps: Capturing Prototype Effects

This paper presents a knowledge discovery approach to extracting knowledge from area–class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non–uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area–class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area–class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes.

Gradation as a Communication Device in Area-Class Maps

This paper proposes a methodology to assess gradation as a cartographic tool for communicating information in area-class maps. The communication model is used as a theoretical foundation, suggesting distinction between errors that occur in encoding and decoding of geographic information. The proposed methodology begins with the determination of a target level of encoding error. Map alternatives are constrained to achieve this target, with gradation considered as one variable in the map production process. The result is a series of maps of equal encoding accuracy but varying in the degree of gradation represented. The individual maps of the series can then be evaluated in terms of decoding accuracy. The methodology is demonstrated by producing a series of alternative forest region maps of New York, Pennsylvania, and New Jersey based on U.S. Forest Service data on tree genus distributions. The series ranges from a 4-class graded area-class map to a 13-class crisp map. The results show gradation to be a viable alternative to the proliferation of map classes as a means of cartographic communication.

Representing uncertainty of area class maps with a correlated inter-map cell swapping heuristic

Decision makers are often forced to rely on maps too imprecise for their GIS applications. Monte Carlo simulation, a technique that generates many versions of possible application results, is one method for representing uncertainty for applications using overly generalized maps. For the purposes of this paper, an overly-generalized map contains an area class map too coarse to be useful for a particular application. A conflation technique combining the generalized map and samples of application quality data defines a probability distribution of map classes and the spatial statistics desired in the set of Monte Carlo input maps. This paper introduces a combination of model components to better construct area class maps for Monte Carlo simulation, including a correlated inter-map cell swapping algorithm, a class probability model, and a new spatial statistic. Together, these components allow for the generation of area class maps for the purpose of representing spatial application uncertainty. This paper focuses its discussion on the cell swapping heuristic. This heuristic allows for a modular approach to simulation modeling, where each model component can be chosen for its particular benefits. A modular approach potentially allows spatial data uncertainty models to be more easily constructed.

Development and test of an error model for categorical data

Abstract An error model for spatial databases is defined here as a stochastic process capable of generating a population of distorted versions of the same pattern of geographical variation. The differences between members of the population represent the uncertainties present in raw or interpreted data, or introduced during processing. Defined in this way, an error model can provide estimates of the uncertainty associated with the products of processing in geographical information systems. A new error model is defined in this paper for categorical data. Its application to soil and land cover maps is discussed in two examples: the measurement of area and the measurement of overlay. Specific details of implementation and use are reviewed. The model provides a powerful basis for visualizing error in area class maps, and for measuring the effects of its propagation through processes of geographical information systems.