Multi-layer multi-class dasymetric mapping to estimate population distribution

Su, Ming-Dawa; Lin, Meichun; Hsieh, Hsin-I; Tsai, Bor-Wen; Lin, Chun-Hung A.

doi:10.1016/j.scitotenv.2010.06.032

Cited by 58 publications

(49 citation statements)

References 21 publications

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“…The additional data usually produces more accurate results than those without auxiliary information, assuming that ancillary information refl ects the spatial distribution of the variables being mapped. The more often land cover/land use data derived from satellite imageries (Eicher and Brewer, 2001;Mennis, 2003;Ciołkosz and Bielecka, 2005;Gallego, 2010;Azar et al, 2013;Langford, 2013), topographic data (Su et. Al., 2010;Wu et al, 2008), street network (Zandbergen and Ignizio, 2010) or even cadastral data (Maantay et al, 2007) are used as ancillary information.…”

Section: Introductionmentioning

confidence: 99%

Redistribution population data across a regular spatial grid according to buildings characteristics

Całka

Bielecka

Zdunkiewicz

2016

Geodesy and Cartography

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Population data are generally provided by state census organisations at the predefi ned census enumeration units. However, these datasets very are often required at userdefi ned spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a fl at. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

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Section: Introductionmentioning

confidence: 99%

Redistribution population data across a regular spatial grid according to buildings characteristics

Całka

Bielecka

Zdunkiewicz

2016

Geodesy and Cartography

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“…It is mandatory to know, as precisely as possible, the location of potentially exposed persons to guarantee the efficiency of emergency plans and to reduce associated costs related with the rescue of people and social recovery (e.g. Bhaduri et al, 2002;Sutton et al, 2003;Chen et al, 2004;Su et al, 2010;Aubrecht et al, 2013). In fact, according to Bhaduri et al (2002), locating population at risk must be the first step in saving lives.…”

Section: General Concepts and Frameworkmentioning

confidence: 99%

“…population could be distorted (e.g. Fisher and Langford, 1996;Su et al, 2010). In addition, people can be concentrated in specific places within a BCU.…”

Section: Assessment Of Population Exposure -State Of the Artmentioning

confidence: 99%

“…The adopted methodology and obtained results are dependent on the type and quality of the input data used as ancillary information to disaggregate data (Su et al, 2010). In fact, on global scales (from world to regional scale), the tool used to disaggregate general information is frequently a land use map or an accessibility map that allows a spatial discrimination of the population between urban and rural areas to be made (e.g.…”

Section: Assessment Of Population Exposure -State Of the Artmentioning

confidence: 99%

“…A set of target zones should be defined and then, based on areal interpolation or other weighting algorithms, the input data should be disaggregated to estimate, for example, the population in a set of smaller units based on the known population for the global unit (e.g. Flowerdew and Green, 1992;Langford and Unwin, 1994;Mennis, 2003;Holt et al, 2014;Wu et al, 2008;Su et al, 2010;Tapp, 2010). In this work the dasymetric approach was performed following a binary analysis over residential building/not residential building areas (Fig.…”

Section: Population Distribution and Exposurementioning

confidence: 99%

See 2 more Smart Citations

Assessing population exposure for landslide risk analysis using dasymetric cartography

Garcia

Oliveira

Zêzere³

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Assessing the number and locations of exposed people is a crucial step in landslide risk management and emergency planning. The available population statistical data frequently have insufficient detail for an accurate assessment of potentially exposed people to hazardous events, mainly when they occur at the local scale, such as with landslides. The present study aims to apply dasymetric cartography to improving population spatial resolution and to assess the potentially exposed population. An additional objective is to compare the results with those obtained with a more common approach that uses, as spatial units, basic census units, which are the best spatial data disaggregation and detailed information available for regional studies in Portugal. Considering the Portuguese census data and a layer of residential building footprint, which was used as ancillary information, the number of exposed inhabitants differs significantly according to the approach used. When the census unit approach is used, considering the three highest landslide susceptible classes, the number of exposed inhabitants is in general overestimated. Despite the associated uncertainties of a general cost-benefit analysis, the presented methodology seems to be a reliable approach for gaining a first approximation of a more detailed estimation of exposed people. The approach based on dasymetric cartography allows the spatial resolution of population over large areas to be increased and enables the use of detailed landslide susceptibility maps, which are valuable for improving the exposed population assessment.

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Comparative assessment of gridded population data sets for complex topography: a study of Southwest China

Knudby

et al. 2020

Popul Environ

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Multi-layer multi-class dasymetric mapping to estimate population distribution

Cited by 58 publications

References 21 publications

Redistribution population data across a regular spatial grid according to buildings characteristics

Redistribution population data across a regular spatial grid according to buildings characteristics

Assessing population exposure for landslide risk analysis using dasymetric cartography

Comparative assessment of gridded population data sets for complex topography: a study of Southwest China

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