A Geostatistical Framework for Area‐to‐Point Spatial Interpolation

Kyriakidis, Phaedon C.

doi:10.1111/j.1538-4632.2004.tb01135.x

Cited by 259 publications

(157 citation statements)

References 33 publications

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“…An incomplete list would comprehend the following: binary allocation (Langford, Maguire, & Unwin, 1991); limiting variable estimation (Eicher & Brewer, 2001;Gallego et al, 2011); empirical estimation of local densities (Mennis, 2003;Mennis & Hultgren, 2006); regressions (Briggs, Gulliver, Fecht, & Vienneau, 2007;Flowerdew & Green, 1989); kernel functions applied to point data (Bracken & Martin, 1995;Martin, 1989); estimation-expectation algorithm (Dempster, Laird, & Rubin, 1977, and its applications such as in Flowerdew, Green, & Kehris, 1991;Gallego et al, 2011;Harvey, 2002) and geostatistical estimations (Kyriakidis, 2004;Liu, Kyriakidis, & Goodchild, 2008;Wu & Murray, 2005). The paper of Wu, Qiu, and Wang (2005) systematizes and reviews most of existing population estimation methods.…”

Section: Introduction: the Dasymetric Mapping Fundamentalsmentioning

confidence: 99%

A high-resolution population grid map for Europe

2013

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Population figures are usually collected by national statistical institutes at small enumeration units (e.g. census tracts or building units). However, still for many countries in Europe, data are distributed at coarser geographical units like municipalities. This level of resolution is insufficient for analysis in many fields. In addition, the heterogeneity of the size of the geographical units causes great distortions in analysis, i.e. the Modifiable Areal Unit Problem (MAUP). Dasymetric mapping techniques have long been applied worldwide to derive finer (and MAUP-free) depictions of the population distribution. These techniques disaggregate population figures reported at coarse source zones into a finer set of zones using ancillary geographical data. Previous attempts to map the European population at high resolution have used CORINE Land Cover (CLC) as the main source of ancillary data. In this article, we test new geographical datasets to produce an updated and improved European population grid map. It is tested whether using more detailed ancillary data in the dasymetric mapping significantly yields higher accuracies. As final outcome of this cartographic exercise, a European population grid map for the reference year of 2006, with a spatial resolution of 100 × 100 meters, is presented and validated against reference data. Resident population reported at commune level, a refined version of CLC and information on the soil sealing degree are used as the main inputs to produce the final map.

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Section: Introduction: the Dasymetric Mapping Fundamentalsmentioning

confidence: 99%

A high-resolution population grid map for Europe

2013

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“…Next, overlaid network algorithms are a group of simple and more complicated methods that employ 1-D road network data (Xie, 1995). Last, the area-to-point interpolation by Kyriakidis (2004) originates from classic geostatistical point interpolations.…”

Section: Research Gap and The Current Studymentioning

confidence: 99%

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

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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Population 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.

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“…Wij bevelen aan om voor de Gd-kartering in laag Nederland relaties te zoeken tussen grondwaterstanden en nieuwe bronnen van gebiedsdekkend beschikbare hulpinformatie, en de toepassingsmogelijkheden te onderzoeken van nieuwe geostatistische methoden, zoals Area-toPoint Kriging (Kyriakidis, 2004;Kyriakidis en Yoo, 2005;Orton et al, 2012).…”

Section: Aanbevelingenunclassified

BasisRegistratie Ondergrond (BRO) Actualisatie Gt/Gd : beschrijving grondwaterkarakteristieken per Gt-klasse voor hoog Nederland

Stuyt¹,

Knotters²,

Walvoort³

et al. 2017

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A Geostatistical Framework for Area‐to‐Point Spatial Interpolation

Cited by 259 publications

References 33 publications

A high-resolution population grid map for Europe

A high-resolution population grid map for Europe

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

BasisRegistratie Ondergrond (BRO) Actualisatie Gt/Gd : beschrijving grondwaterkarakteristieken per Gt-klasse voor hoog Nederland

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