scholarly journals Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany

2020 ◽  
Vol 12 (10) ◽  
pp. 3976 ◽  
Author(s):  
Sebastian Eichhorn
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Population Distribution ◽  
Population Data ◽  
Added Value ◽  
Moran’S I ◽  
Dispersion Measure ◽  
Test Statistics ◽  
Moran's I ◽  
Urban Density

High-resolution population data are a necessary basis for identifying affected regions (e.g., natural disasters, accessibility of social infrastructures) and deriving recommendations for policy and planning, but municipalities are, as in Germany, regularly the smallest available reference unit for data. The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. In addition to common test statistics like MAE or MAPE, the Gini-coefficient and the local Moran’s I are applied and their added value for accuracy assessment is tested. With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. With 23.6%, the dispersion measure cannot improve distribution accuracy. Overall, the algorithms perform better for urban than for rural areas. Gini-coefficients show that same spatial concentration patterns are achieved as in the actual population distribution. According to local Moran’s I, there are statistically significant underestimations, especially in the highly-dense inner-urban areas. Overestimates are found in the transition to less urbanized areas and the core areas of peripheral cities. Overall, the additional test statistics can provide important insights into the data, which go beyond common methods for evaluation.

scholarly journals WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019 ◽  
Vol 11 (18) ◽  
pp. 2128 ◽  
Author(s):  
Mugiraneza ◽  
Nascetti ◽  
Ban
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Informal Settlements ◽  
Urban Land ◽  
Urban Environments ◽  
Urban Density ◽  
Rule Based ◽  
Urban Land Cover ◽  
Object Based ◽  
Rule Based Approach

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

scholarly journals Geostatistical modelling of the malaria risk in Mozambique: effect of the spatial resolution when using remotely-sensed imagery

2015 ◽  
Vol 10 (2) ◽  
Author(s):  
Federica Giardina ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Spatial Resolution ◽  
Population Data ◽  
Earth Observation ◽  
Malaria Risk ◽  
Geostatistical Model ◽  
Geostatistical Modelling ◽  
Elevation Data ◽  
Moderate Resolution

<p>The study of malaria spatial epidemiology has benefited from recent advances in geographic information system and geostatistical modelling. Significant progress in earth observation technologies has led to the development of moderate, high and very high resolution imagery. Extensive literature exists on the relationship between malaria and environmental/climatic factors in different geographical areas, but few studies have linked human malaria parasitemia survey data with remote sensing-derived land cover/land use variables and very few have used Earth Observation products. Comparison among the different resolution products to model parasitemia has not yet been investigated. In this study, we probe a proximity measure to incorporate different land cover classes and assess the effect of the spatial resolution of remotely sensed land cover and elevation on malaria risk estimation in Mozambique after adjusting for other environmental factors at a fixed spatial resolution. We used data from the Demographic and Health survey carried out in 2011, which collected malaria parasitemia data on children from 0 to 5 years old, analysing them with a Bayesian geostatistical model. We compared the risk predicted using land cover and elevation at moderate resolution with the risk obtained employing the same variables at high resolution. We used elevation data at moderate and high resolution and the land cover layer from the Moderate Resolution Imaging Spectroradiometer as well as the one produced by MALAREO, a project covering part of Mozambique during 2010-2012 that was funded by the European Union’s 7<sup>th</sup> Framework Program. Moreover, the number of infected children was predicted at different spatial resolutions using AFRIPOP population data and the <em>enhanced</em> population data generated by the MALAREO project for comparison of estimates. The Bayesian geostatistical model showed that the main determinants of malaria presence are precipitation and day temperature. However, the presence of wetlands and bare soil are also very important factors. The model validation performed on a subset of locations revealed that the use of high-resolution covariates (MALAREO land cover and elevation data) improved prediction performance.</p>

scholarly journals Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 13 ◽  
Author(s):  
Taïs Grippa ◽  
Catherine Linard ◽  
Moritz Lennert ◽  
Stefanos Georganos ◽  
Nicholus Mboga ◽  
...  
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Population Distribution ◽  
Urban Land Use ◽  
Added Value ◽  
Dasymetric Mapping ◽  
Distribution Models ◽  
Satellite Information ◽  
Lower Accuracy ◽  
Very High

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.

scholarly journals Global Flood Exposure from Different Sized Rivers

2021 ◽  
Author(s):  
Mark V. Bernhofen ◽  
Mark A. Trigg ◽  
P. Andrew Sleigh ◽  
Christopher C. Sampson ◽  
Andrew M. Smith
Keyword(s):  
High Resolution ◽  
Flood Risk ◽  
Population Distribution ◽  
Flood Hazard ◽  
Population Data ◽  
Flood Exposure ◽  
Model Independent ◽  
Further Development ◽  
Global Population

Abstract. There is now a wealth of data to calculate global flood exposure. Available datasets differ in detail and representation of both global population distribution and global flood hazard. Previous studies of global flood risk have used datasets interchangeably without addressing the impacts using different datasets could have on exposure estimates. By calculating flood exposure to different sized rivers using a model independent geomorphological approach, we show that limits placed on the size of river represented in global flood models result in global flood exposure estimates that differ by greater than a factor of 2. The choice of population dataset is found to be equally important and can have enormous impacts on national flood exposure estimates Up-to-date, high resolution population data is vital for accurately representing exposure to smaller rivers and will be key in improving the global flood risk picture. Our results inform the appropriate application of these datasets and where further development and research is needed.

scholarly journals Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data

2020 ◽  
Vol 12 (4) ◽  
pp. 608 ◽  
Author(s):  
Min Xu ◽  
Chunxiang Cao ◽  
Peng Jia
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Population ◽  
Population Distribution ◽  
Housing Price ◽  
Population Estimation ◽  
Google Earth ◽  
Stereo Pair ◽  
Fine Scale ◽  
Building Height

Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.

scholarly journals Una aproximación volumétrica a la desagregación espacial de la población combinando cartografía temática y datos LIDAR

2016 ◽  
pp. 147 ◽  
Author(s):  
F. J. Goerlich
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Census Tract ◽  
Population Distribution ◽  
Geographical Information ◽  
Land Use Land Cover ◽  
Building Height ◽  
The Third ◽  
Third Dimension

<p>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.</p>

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

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