Areal Interpolation Techniques Research Articles

Possible application of elementary geochemical landscapes map for mapping heavy metals and metalloids content in urban soils

Mapping heavy metals and metalloids (HMM) content in urban soils is necessary for the public health risk assessment. The sampling step is usually large in comparison with typical variability intervals due to the complexity and expensiveness of analysis. This paper considers an attempt to map HMM concentration coefficients based on landscape-geochemical positions (LGP). The case study area is Darkhan, Mongolia, a large industrial and transport hub. 126 soil samples were taken for analysis of contaminants As, Cd, Cr, Cu, Pb, Sb, W; the distance between sampling points was 500–700 m. For each point, the concentration coefficient (DF) of each pollutant was calculated. The LGP map was derived from SRTM digital elevation model, with supplement of hydrographic network data. The final maps of the concentration coefficients were created using areal interpolation technique with the Voronoi diagram of sampling points as an input data and the LGP polygons as a target dataset. The relatively low sampling points density, as well as the relatively large DEM cell size limit the possibility to harmonize datasets. This leads to the noticeable difference between the parameter distribution obtained from areal interpolation and the distribution obtained from deterministic method. Besides, some resulting features should be considered as interpolation artifacts. Nevertheless, the potential suitability of LGP maps as a basis for mapping pollution of urban areas is shown.

Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data.

Country-level census data are typically collected once every 10 years. However, conflicts, migration, urbanization, and natural disasters can rapidly shift local population patterns. This study demonstrates the feasibility of a “bottom-up”-method to estimate local population density in the between-census years by combining household surveys with contemporaneous geo-spatial data, including village-area and satellite imagery-based indicators. We apply this technique to the case of Sri Lanka using Poisson regression models based on variables selected using the Least Absolute Shrinkage and Selection Operator (LASSO). The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey, and is employed to obtain out-of-sample density estimates in the non-surveyed villages. These estimates approximate the census density accurately and are more precise than other bottom-up studies using similar geo-spatial data. While most open-source population products redistribute census population “top-down” from higher to lower spatial units using areal interpolation and dasymetric mapping techniques, these products become less accurate as the census itself ages. Our method circumvents the problem of the aging census by relying instead on more up-to-date household surveys. The collective evidence suggests that our method is cost effective in tracking local population density with greater frequency in the between-census years.

Developing an “intelligent” high-fidelity GIS-based travel demand model framework for improved network-wide traffic estimation

The four-step travel demand model (FSTDM) is based on coarse rural traffic analysis zones (TAZs) which tends to exaggerate the intrazonal trips resulting in biased and unbalanced trip distribution over the roadway network with high estimation errors. These limitations have necessitated developing a geographic information systems (GIS)-based high-fidelity travel demand model framework (HFTDMF) capable of achieving network-wide traffic volume estimation with improved model accuracy. This requires using an all functional class roadway network and enhancing the census-based coarse TAZ structure with finer-grained spatial resolution TAZs by integrating the travel demand modeling software platform, remotely-sensed images, parcel-based digital property maps, the AZTool aggregation algorithm, and areal interpolation technique. Preliminary results from the Greater Fredericton Area (GFA) showed that increasing the GFA spatial resolution from the coarsest TAZ structure at census tract (CT) level (27 CT TAZs) to the finest TAZ structure at 4252 “fine” TAZs resulted in an improvement to modeling accuracy of R2, by 0.4092 (from 0.2490 to 0.6582) and an improvement in traffic assignment coverage by 46 percentage points (from 29% to 75%).

A spatial dynamic model of population changes in a vulnerable coastal environment

ABSTRACTThis study developed a spatial dynamic model to examine the coupled natural–human responses in the form of changes in population and associated developed land area in the Lower Mississippi River Basin region. The goal was to identify key socioeconomic factors (utility) and environmental factors (hazard damages, elevation, and subsidence rate) that affected population changes, as well as to examine how population changes affected the local utility and the local environment reciprocally. We first applied areal interpolation techniques with the volume-preserving property to transform all the data at Year 2000 into a unified 3 km by 3 km cellular space. We then built an Elastic Net model to extract 12 variables from a set of 33 for the spatial dynamic model. Afterward, we calibrated the neighborhood effects with a genetic algorithm and use the spatial dynamic model to simulate population and developed land area in 2010. Furthermore, we took a Monte Carlo approach for analyzing the uncertainty of the model outcome. Our accuracy assessment shows that the model on average slightly overpredicts the number of population and the developed land percentage at 2010, as indicated by the low values of mean absolute deviation (MAD) due to quantity. On the other hand, the MADs due to allocation are larger than the MADs due to quantity, with most outliers found in the New Orleans region where population and urban development declined significantly during 2000–2010 after Hurricane Katrina. The proposed model sheds light on the complex relationships between coastal hazards and human responses and provides useful insights to strategic development for coastal sustainability.

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

ABSTRACTPopulation at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

A Comparative Study on the Spatial Statistical Models for the Estimation of Population Distribution

This study aims to accurately estimate population distribution more specifically than administrative unites using a RK (Regression-Kriging) model. The RK model is the areal interpolation technique that involves linear regression and the Kriging model. In order to estimate a population’s distribution using a sample region, four different models were used, namely; a regression model, RK model, OK (Ordinary Kriging) model and CK (Co-Kriging) model. The results were then compared with each other. Evaluation of the accuracy and validity of evaluation analysis results were the basis RMSE (Root Mean Square Error), MAE (Mean Absolute Error), G statistic and correlation coefficient (ρ). In the sample regions, every statistic value of the RK model showed better results than other models. The results of this comparative study will be useful to estimate a population distribution of the metropolitan areas with high population density

Modeling the distribution of the North Atlantic right whale Eubalaena glacialis off coastal Maine by areal co-kriging

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 24:21-31 (2014) - DOI: https://doi.org/10.3354/esr00579 Modeling the distribution of the North Atlantic right whale Eubalaena glacialis off coastal Maine by areal co-kriging Brooke Wikgren1,*, Hauke Kite-Powell2, Scott Kraus1 1New England Aquarium, John H. Prescott Marine Laboratory, Central Wharf, Boston, Massachusetts 02110, USA 2Woods Hole Oceanographic Institution, Marine Policy Center, MS 41, Woods Hole, Massachusetts 02543, USA *Corresponding author: bwikgren@neaq.org ABSTRACT: To enhance recovery of the Endangered North Atlantic right whale Eubalaena glacialis, mitigation strategies are needed to reduce the leading causes of injury and mortality of these animals, which include ship strikes and entanglement in fishing gear. Such efforts require information on the spatial and temporal distribution of right whales that can be analyzed against risk factors to identify solutions to protect whales while minimizing economic impacts to industry. Currently, no methods adequately make use of all available data to characterize right whale distribution, often leaving data gaps or misrepresenting whale activity in areas where we have limited systematic survey data. In response to this, an areal co-kriging interpolation technique was developed using ArcGIS 10.1 Geostatistical Analyst, utilizing all available right whale location data including systematic survey data, opportunistic sightings data, and satellite tag data. With many management plans shaped around specific geographic zones, this methodology was developed with the option to summarize information within user-defined polygons depending on the management regime. For this paper, whale distribution predictions were summarized to 524 irregularly shaped polygons off coastal Maine, representing lobster fishing zones at a 2 mo temporal scale. Results indicate that the predicted values fall within reasonable ranges, appropriately represent seasonal differences, and better represent right whale distribution patterns compared to other methods. KEY WORDS: Right whale · Spatial distribution · Conservation · Cetacean abundance · Endangered species · Habitat-based density model · Marine mammal · Modeling · Wildlife management Full text in pdf format Supplementary material PreviousNextCite this article as: Wikgren B, Kite-Powell H, Kraus S (2014) Modeling the distribution of the North Atlantic right whale Eubalaena glacialis off coastal Maine by areal co-kriging. Endang Species Res 24:21-31. https://doi.org/10.3354/esr00579 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 24, No. 1. Online publication date: April 09, 2014 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2014 Inter-Research.

The effect of administrative boundaries and geocoding error on cancer rates in California

Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods.

A Process Oriented Areal Interpolation Technique: A Coastal County Example

The Modifiable Areal Unit Problem (MAUP) is the classic term for describing different totals observed from spatially different aggregation units. In a typical analytical problem (e.g. estimating total population within a watershed from census unit totals) the spatial distribution of populations within the census units are modeled. To minimize MAUP errors, areal interpolation techniques are used to model such sub-unit population distributions. Areal interpolation techniques are highly dependent on ancillary data (e.g. land use/cover data) and typically do not include “intelligent” relations about where people choose to live, other than a weighted association between nominal land cover/use and population density. The purpose of this research was to design and implement an “intelligent” areal interpolation method for housing data in coastal environments, validate the accuracy, and compare to other techniques. This study was conducted for Miami-Dade County in Florida at census scales from county to block. Parcel boundary data was used as a reference layer to validate each technique. Not surprisingly, all techniques perform best at finer spatial resolutions (e.g. block level) with error increasing at coarser resolutions. The accuracy of the dasymetric technique is directly related to the accuracy of ancillary data. The new intelligent technique, (referred to as the process-oriented technique from here onwards) models the relationship between housing unit density distribution and proximity to the coast. This process-oriented technique performed better than the areal weighting and the dasymetric mapping technique. Combining the 'process-oriented' technique with a dasymetric technique provided the least amount of error.

A New Areal Interpolation Method Based on Spatial Statistics

Differences in spatial units among spatial data often complicate analyses. Spatial unit convergence, called areal interpolation, is often applied to address this problem. Of the many proposed areal interpolation methods, few consider spatial autocorrelation, which is the general property of spatial data. In this paper, by employing a spatial process model, a new areal interpolation method that considers spatial autocorrelation is presented. First, we briefly survey previous areal interpolation techniques and demonstrate that the stochastic method is superior to the deterministic method in archiving accurate interpolations. Next, after a discussion on the spatial process model, a new areal interpolation method is suggested. In this method, both spatial autocorrelation and the volume preserving property, a property that should be considered in areal interpolation, are considered using a combination of a linear regression based areal interpolation method, and the spatial process model. Finally, a case study on the areal interpolation of a population is provided to demonstrate that the suggested method succeeds in improving the predictive accuracy. This case study indicates that the consideration of spatial autocorrelation is important for accurate areal interpolation.

Disease, Death, and the Body Politic

More than a trillion dollars of public money is spent annually on health care in the United States. In order to inform policymakers, health advocacy groups, tax-paying constituents, and beneficiaries, it would be useful to present and analyze health outcome and health-related data at the U.S. congressional district level. Presently, health event data are not reported at this political unit; however, recent interest and advances in areal interpolation techniques are beginning to transcend the inherent limitations imposed by legacy data collection and analyses systems. In this paper, the authors use the dasymetric approach to illustrate how this areal interpolation technique can be used to transfer county-level mortality rate data from several causes of death to the U.S. congressional district level. The study’s primary goal is to promote areal interpolation techniques in the absence of a systematic and comprehensive national program for geocoding health events.

From Beats to Tracts

In this article, the author addresses the spatial incompatibility between different types of data that is commonly faced in crime analysis research. Socioeconomic variables have been proved valuable in explaining crime behaviors and in predicting crime activities. However, socioeconomic data and crime statistics are usually collected and aggregated at different spatial zonations of geographical space, making the integration and analysis of these data difficult. Simple areal weighting interpolation technique, although frequently employed, often leads to unsatisfactory results due to the fact that most types of crime do not distributed evenly across space. Using 2007 burglary crime in Houston, Texas, as an example, the author illustrates a remote sensing approach to interpolating crime statistics from police beat enumeration district used by Houston Police Department to census tract defined by the U.S. Bureau of the Census.

The accuracy of areal interpolation techniques: standardising 19th and 20th century census data to allow long-term comparisons

The census has been collected in Britain since 1801 and has published detailed attribute data at district-level about the population since 1851. Comparing censuses over the long-term is, however, highly problematic due to the problem of boundary changes. The only traditional response to this has been to aggregate data; however, this is highly unsatisfactory. The Great Britain Historical Geographical Information Systems (GIS) allows census data to be linked to the boundaries that were used to publish them, and areal interpolation techniques allow data from many dates to be standardised onto a single administrative geography to allow long-term comparison. Areal interpolation, however, inevitably introduces error and quantifying this error with real-world data is usually impossible. In this paper the accuracy of several areal interpolation techniques suitable for use with historical data are compared using “synthetic” districts and parishes created from Enumeration District-level data from the 1991 census. The errors introduced are analysed for several different variables and show that the effectiveness of the technique depends on the variable to be interpolated and the choice of target geography. While this paper is mainly applied to historical data, these results have relevance to any areal interpolation problem.

Parameterization and Visualization of the Errors in Areal Interpolation

Areal interpolation involves the transfer of data from one zonation of a region to another, where the two zonations of space are geographically incompatible. By its very nature this process is fraught with errors. However, only recently have there been specific attempts to quantify these errors. Fisher and Langford (1995) employed Monte Carlo simulation methods, based on modifiable areal units, to compare the errors resulting from selected areal interpolation techniques. This paper builds on their work by parameterizing and visualizing the errors resulting from the areal weighting and dasymetric methods of areal interpolation. It provides the basis for further research by developing the methodology to produce predictive models of the errors in areal interpolation. Random aggregation techniques are employed to generate multiple sets of source zones and interpolation takes place from these units onto a fixed set of randomly generated target zones. Analysis takes place at the polygon, or target zone level, which enables detailed analysis of the error distributions, basic visualization of the spatial nature of the errors and predictive modeling of the errors based on parameters of the target zones. Correlation and regression analysis revealed that errors from the areal weighting technique were related to the geometric parameters of the target zones. The dasymetric errors, however, demonstrated more association with the population or attribute characteristics of the zones. The perimeter, total population, and population density of the target zones were shown to be the strongest predictive parameters.

Quantifying the fine scale (1km � 1km) exposure, dose and effects of ozone: Part 2 estimating yield losses for agricultural crops

In Britain wheat is an important crop accounting for 41% of the total cereal production. In this study ozone concentrations for 1989 estimated as described in Part 1 of the paper are integrated with the estimated wheat distribution to derive a detailed estimate of the impact of ozone on wheat yields at a fine spatial scale (1km × 1km). These data provide estimates for calculating regional and national yield losses. The methodology can be applied to other crop species. Recent research on a range of crops has established relationships between the economic yield loss for certain crops, including wheat, and ozone exposure. Exposure is described as the accumulated exposure above a threshold experienced during the daylight hours (AOT). Critical AOT values are derived from yield exposure relationships which show linear reductions of yield loss with increasing ozone concentrations. This study has made use of land cover data from remotely sensed imagery at 25m resolution and nationally collected agricultural statistics for counties. These data were combined using an areal interpolation technique to provide more spatially articulate estimates of the location and intensity of wheat production. The results demonstrate the economic importance of ozone as a pollutant. Wheat yield losses attributed to ozone vary between different parts of the country but, for years when ozone levels are high, yield losses are likely to be significant in some areas.