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Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region.

In the Big Data era, Earth observation is becoming a complex process integrating physical and social sectors. This study presents an approach to generating a 100 m population grid in the Contiguous United States (CONUS) by disaggregating the US census records using 125 million of building footprints released by Microsoft in 2018. Land-use data from the OpenStreetMap (OSM), a crowdsourcing platform, was applied to trim original footprints by removing the non-residential buildings. After trimming, several metrics of building measurements such as building size and building count in a census tract were used as weighting scenarios, with which a dasymetric model was applied to disaggregate the American Community Survey (ACS) 5-year estimates (2013–2017) into a 100 m population grid product. The results confirm that the OSM trimming process removes non-residential buildings and thus provides a better representation of population distribution within complicated urban fabrics. The building size in the census tract is found in the optimal weighting scenario. The product is 2.5Gb in size containing 800 million populated grids and is currently hosted by ESRI (http://arcg.is/19S4qK) for visualization. The data can be accessed via https://doi.org/10.7910/DVN/DLGP7Y. With the accelerated acquisition of high-resolution spatial data, the product could be easily updated for spatial and temporal continuity.

Areal interpolation is routinely used when spatial data are unavailable at desired geographical units. While many methods are available, few of them were developed specifically for and tested in highly developed urban cores. Even fewer studied subpopulation or population characteristics. This paper explores both issues using parcel map and decennial census data as ancillary information. Using census blocks as intermediate zones, the method first disaggregates source-zone data to intermediate zones, then disaggregates data to parcel level in intermediate zones intersecting target zones, and finally aggregates intermediate-zone and parcel-level estimates to obtain target-zone estimates. Compared to areal weighting and residential proportion, the proposed method is significantly more accurate. All three methods perform the best on population count, and worst on spatially clustered subpopulations such as black/African American population. Quotient variables are more difficult to interpolate than count variables. The research demonstrates the utility of parcel and decennial census data for areal interpolation in highly developed urban cores, and calls for future research on subpopulation and population characteristics.

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR, (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask, and (3) there is a clear complementarity between VHR land cover and land use.

Information on the distribution and dynamics of dwellings and their inhabitants is essential to support decision-making in various fields such as energy provision, land use planning, risk assessment and disaster management. However, as various different of approaches to estimate the current distribution of population and dwellings exists, further evidence on past dynamics is needed for a better understanding of urban processes. This article therefore addresses the question of whether and how accurately historical distributions of dwellings and inhabitants can be reconstructed with commonly available geodata from national mapping and cadastral agencies. For this purpose, an approach for the automatic derivation of such information is presented. The data basis is constituted by a current digital landscape model and a 3D building model combined with historical land use information automatically extracted from historical topographic maps. For this purpose, methods of image processing, machine learning, change detection and dasymetric mapping are applied. The results for a study area in Germany show that it is possible to automatically derive decadal historical patterns of population and dwellings from 1950 to 2011 at the level of a 100 m grid with slight underestimations and acceptable standard deviations. By a differentiated analysis we were able to quantify the errors for different urban structure types.

Where urban dwellers live at a fine scale is essential for the planning of services and response to city emergencies. Currently, most existing population mapping approaches considered census data as observational data for specifying models. However, census data usually have low spatial resolution and low frequency. Here, we presented a framework for mapping populations in residential neighborhoods with 30 m spatial resolution with little dependency upon census data. The framework integrated remote sensing and crowdsourcing data. The observational populations and number of households at residential neighborhood scale were obtained from real-time crowdsourcing data instead of census data. We tested our framework in Beijing. We found that (1) the number of households from a real estate trade platform could be a good proxy for accurate observational population. (2) The accuracy of the mapping population in residential neighborhoods was reasonable. The mean absolute percentage error was 47.26% and the R2 was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

Broad-scale impact and vulnerability assessments are essential for informing decisions on long-term adaptation planning at the national, regional, or global level. These assessments rely on population data for quantifying exposure to different types of hazards. Existing population datasets covering the entire globe at resolutions of 2.5 degrees to 30 arc-seconds are based on information available at administrative-unit level and implicitly assume uniform population densities within these units. This assumption can lead to errors in impact assessments and particularly in coastal areas that are densely populated. This study proposes and compares simple approaches to regionalize population within administrative units in the German Baltic Sea region using solely information on urban extent from the Global Urban Footprint (GUF). Our results show that approaches using GUF can reduce the error in predicting population totals of municipalities by factor 2 to 3. When assessing exposed population, we find that the assumption of uniform population densities leads to an overestimation of 120% to 140%. Using GUF to regionalise population within administrative units reduce these errors by up to 50%. Our results suggest that the proposed simple modeling approaches can result in significantly improved distribution of population within administrative units and substantially improve the results of exposure analyses.

Complex biophysical, social, and human behavioral factors influence population vulnerability to vector-borne diseases. Spatially and temporally dynamic environmental and anthropogenic patterns require sophisticated mapping and modeling techniques. While many studies use environmental variables to predict risk, human population vulnerability has been a challenge to incorporate into spatial risk models. This study demonstrates and applies dasymetric mapping techniques to map spatial patterns of vulnerable human populations and characterize potential exposure to mosquito vectors of West Nile Virus across Chesapeake, Virginia. Mosquito vector abundance is quantified and combined with a population vulnerability index to evaluate exposure of human populations to mosquitoes. Spatial modeling is shown to capture the intersection of environmental factors that produce spatial hotspots in mosquito vector abundance, which in turn poses differential risks over time to humans. Such approaches can help design overall mosquito pest management and identify high-risk areas in advance of extreme weather.

Cartographic presentation of heterogeneity/homogeneity in the spatial distribution of population is still a major problem in modern geography, and other geo-sciences as well. The traditional method of thematic or choropleth mapping rarely gives satisfactory results. This paper analyzes the applicability of dasymetric mapping method for the modelling of spatial distribution of population. Although it is a relatively old method, it becomes widely used following the development of computer technology, GIS and satellite imagery, and its applicability is increasing in social, economic and other sciences and disciplines. After showing the basis and development of dasymetric mapping, the authors present possible application of this method in the population distribution modelling of Vojvodina.

A land use regression (LUR) model for the mapping of NO(2) concentrations in Ottawa, Canada was created based on data from 29 passive air quality samplers from the City of Ottawa's National Capital Air Quality Mapping Project and two permanent stations. Model sensitivity was assessed against three spatial representations of population: population at the dissemination area level, population at the dissemination block level and a dasymetrically derived population representation. A spatial database with land use, roads, population, zoning, greenspaces and elevation was created. Polycategorical zoning data were used in dasymetric mapping to spatially focus population data derived from the dissemination blocks to a sub-block level for comparison purposes. Dasymetric population mapping provided no significant LUR model improvement in explained variance when compared to block level population; however, both the former were significantly better than the dissemination area level population representations. However, where block level population is not available or too costly to acquire, our method using polycategorical zoning data provides a viable alternative in LUR modelling endeavours.

Dasymetric mapping of high-resolution population facilitates the exploration of urban spatial feature. While most relevant studies are still challenged by weak spatial heterogeneity of ancillary data and quality of traditional census data, usually outdated, costly and inaccurate, this paper focuses on mobile phone data, which can be real-time and precise, and also strengthens spatial heterogeneity by its massive mobile phone base stations. However, user population recorded by mobile phone base stations have no fixed spatial boundary, and base stations often disperse in extremely uneven spatial distribution, this study defines a distance-decay supply&ndash, demand relation between mobile phone user population of gridded base station and its surrounding land patches, and outlines a dasymetric mapping method integrating two-step floating catchment area method (2SFCAe) and land use regression (LUR). The results indicate that LUR-2SFCAe method shows a high fitness of regression, provides population mapping at a finer scale and helps identify urban centrality and employment subcenters with detailed worktime and non-worktime populations. The work involving studies of dasymetric mapping based on LUR-2SFCAe method and mobile phone data proves to be encouraging, sheds light on the relationship between mobile phone users and nearby land use, brings about an integrated exploration of 2SFCAe in LUR with distance-decay effect and enhances spatial heterogeneity.

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-topoint 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.

Population data are traditionally obtained through censuses and aggregated up to the level of administrative units for reasons of privacy. This way, however, detailed information on the spatial distribution of the population within these units is masked. Dasymetric mapping techniques were developed to disaggregate population to a finer spatial level using ancillary data. However, a frequently recurring problem in dasymetric mapping studies relates to the overestimation of low-population-density areas and the underestimation of high-population-density areas. To tackle this issue, this research proposes a novel dasymetric mapping approach explicitly dealing with spatial non-stationarity. For this purpose, a comparative model building framework was set up. The impact of spatial non-stationarity on model performance was investigated by comparing global (OLS), regional (rule-based) and local (geographically weighted) regression. Also, the impact of model complexity was considered through stepwise inclusion of information on address type and location, household size and demographic and residential characteristics in the dasymetric model. The approach was tested in the highly complex environment of the Flanders–Brussels region. It was found that the regional model that incorporates address type and household size information performs best and overcomes the structural over- and underestimation issue in dasymetric mapping.

This paper outlines a methodology used to disaggregate a census population in order to more accurately determine the population distribution over a regional area or a state scale. Data regarding population distributions are usually accessible at the level of individual census designation places and are usually mapped as aggregated polygons by the choropleth method with the assumption of a homogeneous distribution of population within a cartographic unit. In contrast, dasymetric mapping provides a more reliable view into the allocation of inhabitants, which can be of significant importance when estimating population distributions. Coupling this methodology with the GIS environment and a free open access database of soil sealing facilitates the acquisition of population surface models for human and urban geography applications.

The ability of GIS to produce accurate analysis results is dependent on the accuracy and the resolution of the data. In many instances the resolution of census enumerator tract data is too coarse and therefore inefficient in conducting fine grained spatial analysis. Dasymetric techniques can increase the spatial resolution of data by incorporating related high resolution ancillary data layers allowing the primary data to be represented at finer resolutions. Areal interpolation relates to a geostatistical process of transferring data from one set of polygons to another. This paper proposes the application of a hybrid technique using dasymetric mapping and areal interpolation principals to overcome the issues of transferring data from arbitrary spatial units to fit for purpose analysis zones on demand. As a consequence the technique also overcomes the problems of coarse scale population data as well as issues relating to the modifiable areal unit problem (MAUP). The data used to illustrate the value and accuracy of the developed methodology is that of the 2011 census population data and ESKOM’s SPOT building count. The final outcome is an algorithm allowing the disaggregation and aggregation of population data to any spatial unit with a high level of accuracy.Keywords: Dasymetric Mapping; MAUP; Population; Census; GIS

This article describes a high-resolution land cover data set for Spain and its application to dasymetric population mapping (at census tract level). Eventually, this vector layer is transformed into a grid format. The work parallels the effort of the Joint Research Centre (JRC) of the European Commission, in collaboration with Eurostat and the European Environment Agency (EEA), in building a population density grid for the whole of Europe, combining CORINE Land Cover with population data per commune. We solve many of the problems due to the low resolution of CORINE Land Cover, which are especially visible with Spanish data. An accuracy assessment is carried out from a simple aggregation of georeferenced point population data for the region of Madrid. The bottom-up grid constructed in this way is compared to our top-down grid. We show a great improvement over what has been reported from commune data and CORINE Land Cover, but the improvements seem to come entirely from the higher resolution data sets and not from the statistical modeling in the downscaling exercise. This highlights the importance of providing the research community with more detailed land cover data sets, as well as more detailed population data. The dasymetric grid is available free of charge from the authors upon request., The authors acknowledge financial support from the BBVA Foundation-Ivie research programme and the first author also acknowledges support from the Spanish Ministry of Science and Technology, ECO2011-23248 project. Results mentioned, but not shown, are available from the authors upon request. The grid numbers are also available from the authors.

[EN] Availability of high resolution population distribution data, independent of the administrative units in which demographic statistics are collected, is a real necessity in many fields: risk evaluation due to earthquakes, flooding or fires, to name just a few, integration between socio-demographic and environmental or geographical information collected in different formats, policy design for the provision public services, such as health, education or public transport, or mobility studies in urban areas or metropolitan regions. Because of this, the literature has explored various methods of population downscaling, collected at communality or census tract level, into smaller areas; typically urban polygons from high resolution topographic maps or land use/land cover databases, or grid cells, allowing the elaboration of raster population layers. A common feature of all these methods is that they do not incorporate building height. In this way, downscaling methods don´t distinguish between the urban sprawl type of settlement, where most of the houses are detached or semi-detached, and compact cities with high buildings. This paper examines error reduction in downscaling census tract population into 1×1 km and 1 ha grids, when we add the third dimension, building height from LIDAR remote sensing data. Algorithms used are simple, and based on areal weighting with or without auxiliary land use/land cover information, since our focus is not in fine turning algorithms, but in measuring improvements due to the missing dimension: building height. Our results indicate that improvements are noticeable. They are comparable to the ones obtained when we move from binary dasymetric methods to more general models combining densities for different land use/land cover types. Hence, adding the third dimension to population downscaling algorithms seems worth pursuing. [ES] La disponibilidad de información sobre la distribución de la población a gran escala, e independiente de las unidades administrativas de recogida de la información, es una necesidad en muchos ámbitos: evaluación de riesgos de terremotos, inundaciones o incendios, integración de estadísticas demográficas con información medioambiental en formatos diversos, diseño de políticas de prestación de servicios públicos o estudios de accesibilidad y movilidad en las grandes ciudades. Por estas razones, la literatura ha explorado numerosos métodos de desagregación de la población, recogida a nivel municipal o de sección censal, en polígonos urbanos procedentes de los sistemas de información de coberturas y usos del suelo o celdas de una malla regular, permitiendo de esta forma la generación de capas raster de población, fáciles de combinar con información medioambiental y geográfica. Una característica común a todos estos métodos de desagregación es que la información auxiliar utilizada no incorpora la altura de las edificaciones. De esta forma los algoritmos de desagregación no distinguen entre asentamientos extensos, basados en edificaciones con pocas alturas, frente a asentamientos compactos, con predominio de edificios con muchas altu-ras. Este trabajo examina la reducción del error que se comente al desagregar la población por secciones censales a una malla regular, de 1×1 km y de 1 ha, cuando incorporamos la tercera dimensión, la altura de los edificios a partir de datos LIDAR, sobre una capa raster de densidad de edificación, y utilizamos algoritmos de desagregación muy simples, basados en distribuciones proporcionales por área o por volumen, con y sin información auxiliar de coberturas y usos del suelo. Los resultados indican que las mejoras, incluso con estos algoritmos sencillos, son notables; comparables a las que se producen al incorporar información sobre usos del suelo a métodos dasimétricos binarios. En consecuencia incorporar la tercera dimensión a los procesos de re-escalado espacial de la población resulta totalmente esencial. El autor agradece la ayuda del programa de Investigación FBBVA-Ivie, así como la del proyecto ECO2015-70632-R.

This paper examines the adaptation of dasymetric mapping methodologies to agricultural data, including their testing and transposition, in order to recover the underlying statistical surface (i.e., an approximation of the real distribution of data). A methodology based on the ideas of Gal- lego and Peedell (2001) and on the binary method is proposed. It has several steps: (i) the ex- clusion of target zones for which no observations exist (binary method), (ii) the application of an iterative process to define the most precise densities for data distribution, and (iii) the stratification/definition of sub-units with homogenous characteristics if the results of the previ- ous step are not satisfactory, and the subsequent application of step two.The methodology was applied in the Alentejo region of Portugal, using data from the 1999 Agricultural Census. Several counties are used as source zones. The aim was to generate a dis- tribution of agro-forestry occupations as close as possible to reality. Two lines of analysis were followed: (i) application of the methodology simultaneously to all counties (definition of regional densities), and (ii) application of the methodology separately to the different sub- areas with similar characteristics (definition of sub-regional densities). For an easy application of the methodology, a computer tool was created, which allowed the easy optimization, vali- dation, and exportation of the data into a Geographic Information System (GIS).The results were validated using several error indicators at the county level, as well as in a sample of parishes. We show that the second variant of the methodology yielded more precise results, and is superior for the types of data available. This method yielded maps in which the distribution of the most relevant agro-forestry occupations is closest to reality.Key Words: dasymetric mapping, agricultural data, spatial disaggregation, iterative process, Alentejo(ProQuest: ... denotes formulae omitted.)Population density data are available to the Euro- pean Commission at the level of the county or parish. However, this level of spatial resolution may be insufficient, in many cases, for planning or modeling purposes or to assess the impact of European Union policies. In some countries, as in France, where most counties have a rather small area (approximately 15 km2 on average), the reso- lution may be sufficient, but it is clearly insuffi- cient in other countries where the counties tend to be larger (Gallego and Peedell 2001).Therefore, maps produced have limited utility in detailed spatial analysis, especially where human populations are concentrated in a relatively small number of villages and cities (Bielecka 2005). Choropleth maps by administrative units give the impression that population is distributed homoge- neously throughout each areal unit, even when portions of the region are, in actuality, uninhab- ited. Openshaw (1984) described these limitations as the modifiable areal unit problem (MAPU)- defined as a situation in which modifying the boundaries and scale of data aggregation signifi- cantly affects the result of spatial data analysis- and stated that it is often unclear whether the re- sults of statistical data analysis indicate some re- ality about the individuals living in that region or are rather strictly a function of the particular areal unit used in the analysis.As regards agricultural data, the situation is the same. Agricultural data is available at the NUTS II, NUTS III, county, and parish levels, but no information is available regarding the true spatial distribution of the available agricultural variables. In Portugal, for instance, data for these levels of analysis are available only from official statistics and from studies on agricultural data disaggrega- tion (Martins, Fragoso, and Xavier 2010, Frag- oso, Martins, and Lucas 2008). However, agricul- tural policies, in many cases, correspond to terri- torial areas that do not conform to county or parish boundaries. …

This paper aimed to use the dasymetric mapping methods proposed by Mennis and Hultgreen (2006) and Strauch and Ajara (2015) to estimate the variation of the distribution in the population in the Jacarepaguá Watershed. For this, population data from the census tracts of 2010 and, as auxiliary data, the map of land use and land cover obtained from the supervised classification, were used - the auxiliary data were obtained using a maximum likelihood method with high resolution images. The method proposed by Mennis and Hultgreen (2006) preserved the pycnophylactic capacity of the dasymetric mapping; however, it resulted in a dasymetric map that distributes the population among the pixels, in accordance with the population variables, and in a more homogeneous way, since it considers only two classes of urban use and occupation. In the Strauch and Ajara (2015) method, there was a loss of 0.04% of the original population, but it emphasized the density differences, by distributing the population heterogeneously, because it allows the specialist to include other classes of land use and land cover and attribute different types of weights to these classes. Resumo: Este trabalho tem por objetivo empregar os métodos de mapeamento dasimétrico propostos por Mennis e Hultgreen (2006) e Strauch e Ajara (2015) para estimar a variação da distribuição da população na Bacia Hidrográfica de Jacarepaguá. Para isso são empregados dados populacionais de setores censitários de 2010 e como dado auxiliar o mapa de uso e cobertura da terra obtido através da classificação supervisionada usando método de máxima verossimilhança às imagens de alta resolução. O método proposto por Mennis e Hultgreen (2006) preservou a capacidade picnofilática do mapeamento dasimétrico, entretanto apresentou como resultado um mapa dasimétrico que distribui a população entre os pixels de acordo com as variáveis populacionais de mais forma homogênea uma vez que considera apenas duas classes de uso e ocupação urbana, enquanto o método proposto por Strauch e Ajara (2015) houve uma perda de 0,04% do contingente populacional original, mas realçou as diferenças de densidade distribuindo a população de forma heterogênea, uma vez que possibilita o especialista incluir outras classes de uso e cobertura da terra e atribuir diferentes tipos de pesos a estas classes.