A population density grid for Spain

This is an author's accepted manuscript of an article published in "International Journal of Geographical Information Science"; Volume 27, Issue 12, 2013; copyright Taylor & Francis; available online at: http://www.tandfonline.com/doi/abs/10.1080/13658816.2013.799283This 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.Goerlich Sanchis, FJ.; Cantarino Martí, I. (2013). A population density grid for Spain. International Journal of Geographical Information Science. 27(12):1-17. https://doi.org/10.1080/13658816.2013.799283S117271

Population and age structure in Hungary: a residential preference and age dependency approach to disaggregate census data

We present a simple model to disaggregate age structured population census data to a 1-km grid for Hungary. A dasymetric approach was used to predict the spatial distribution of population in different age groups by distinguishing residential preferences (in relation to accessible social, economic and green amenities) for working age groups (15–29, 30–49 and 50–64) and population dependencies for children and the elderly (aged 0–14 and 65+). By using open-access land cover data and fine-level population census data as inputs, the model predicts the likely spatial distribution of population and age structure for Hungary in 2011. The resulting map and gridded data provide information to support spatial planning of residential development and urban infrastructure. The model is less data-demanding than most existing approaches, but provides greater power for describing population patterns. It can also be used to create scenarios of future demographic change

Empirically Derived Suitability Maps to Downscale Aggregated Land Use Data

Understanding mechanisms that drive present land use patterns is essential in order to derive appropriate models of land use change. When static analyses of land use drivers are performed, they rarely explicitly deal with spatial autocorrelation. Most studies are undertaken on autocorrelation-free data samples. By doing this, a great deal of information that is present in the dataset is lost. This paper presents a spatially explicit, cross-sectional, logistic analysis of land use drivers in Belgium. It is shown that purely regressive logistic models can only identify trends or global relationships between socio-economic or physico-climatic drivers and the precise location of each land use type. However, when the goal of a study is to obtain the best model of land use distribution, a purely autoregressive (or neighbourhood-based) model is appropriate. Moreover, it is also concluded that a neighbourhood based only on the 8 surrounding cells leads to the best logistic regression models at this scale of observation. This statement is valid for each land use type studied – i.e. built-up, forests, cropland and grassland.

Geodemography: Land cover, geographical information systems and population distribution

This paper examines the recent application of the Geographical Information Systems (GIS) to the analysis of population distribution. We mention the efforts of the National Statistical Institutes in this direction boosted by the last census 2011.The stating point is a growing need to have available population figures for areas not related to administrative boundaries, either user defined zones or in grid format.This allows a convenient zonal system to combine demographic characteristics with environmental and pure geographic data, so the relation between the man and the environment can be analyzed in a unified way.Eventually, we offer a practical illustration of the interactions between GIS techniques and administrative population data in the study of spatial population distribution: We build a density grid for Spain by dasymetric methods from census tracts population data and Land Cover and Use Information System of Spain (SIOSE).The analysis is done within the spatial reference framework of the European Union

Review and Improvements of Existing Delimitations of Rural Areas in Europe

This report aims to improve current delimitations of rural areas in Europe as a support to statistical descriptions by introducing the criteria of peripherality/remoteness and ¿natural(non-artificial) area¿ in the Organisation for Economic Co-operation and Development (OECD) typology. In 1994, the OECD developed an easy concept to identify rural and urban areas based on the population density of a geographical unit. This scheme proved to be highly sensitive to the size of the geographical area and the classification of the thresholds. Over the years, endeavours have been made to review and improve the OECD approach and also alternative methodologies have been proposed. The current methods based solely on population distributions, do not allow for detailed and quantified geographical analysis and do not reflect two main characters differentiating rural from urban areas: the ¿natural¿ (non-artificial) surface and the accessibility/remoteness. In this study, a new rural typology has been developed by integrating the peripherality index and the land cover indicator in the OECD methodology. The analyses were carried out at Local Administrative Unit (LAU) 2 and NUTS3 level for 3 Member States (Belgium, France and Poland). The resulting rural typology classes for LAU2 are ¿rural-peripheral¿, ¿rural-accessible¿, ¿urban-open-space¿ and ¿urban-closed space¿. The typology at regional level (NUTS3) does not provide an accurate picture of the rurality. The methodology applied is flexible and the thresholds of accessibility or land cover implemented can easily be modified to fit-for-purpose. Simple queries were applied with standard procedures using Pan-European homogeneous datasets so as to allow to upscale for assessment at European level.JRC.H.5-Rural, water and ecosystem resource

Spatial Disaggregation of Population Subgroups Leveraging Self-Trained Multi-Output Gradient Boosting Regression Trees

Accurate and consistent estimations on the present and future population distribution, at fine spatial resolution, are fundamental to support a variety of activities. However, the sampling regime, sample size, and methods used to collect census data are heterogeneous across temporal periods and/or geographic regions. Moreover, the data is usually only made available in aggregated form, to ensure privacy. In an attempt to address these issues, several previous initiatives have addressed the use of spatial disaggregation methods to produce high-resolution gridded datasets describing the human population distribution, although these projects have usually not addressed specific population subgroups. This paper describes a spatial disaggregation method based on self-training regression models, innovating over previous studies in the simultaneous prediction of disaggregated counts for multiple inter-related variables, by leveraging multi-output models based on gradient tree boosting. We report on experiments for two case studies, using high-resolution data (i.e., counts for different subgroups available at a resolution of 100 meters) for the municipality of Amsterdam and the region of Greater Copenhagen. Results show that the proposed approach can capture spatial heterogeneity and the dependency on local factors, outperforming alternatives (e.g., seminal disaggregation algorithms, or approaches leveraging individual regression models for each variable) in terms of averaged error metrics, and also upon visual inspection of spatial variation in the resulting maps.</p

MODELING DAYTIME AND NIGHTTIME POPULATION DISTRIBUTIONS IN PORTUGAL USING GEOGRAPHIC INFORMATION SYSTEMS

Most official population censuses release a total count of residents by census area, despite the fact that the distribution of population varies widely in space and time. Increasingly, better spatial representation of population distributions are required for many applications, especially at the local scale. Combining available statistical and physiographic data sets, a dasymetric interpolation approach was developed and tested to produce 2001 spatio-temporal representations of population distributions for the municipalities of Cascais and Oeiras in Portugal. This model was implemented within a geographic information system using streets as a spatial basis to allocate population. For each municipality, digital raster maps of nighttime, daytime, daytime residential, daytime worker and student, total daytime, and ambient population densities were produced at high spatial resolution. Quality assessment procedures confirmed that the method suits the objectives. However, the accuracy of results is mostly dependent on the quality of input data sets