scholarly journals Regionalisation of asset values for risk analyses

2006 ◽  
Vol 6 (2) ◽  
pp. 167-178 ◽  
Author(s):  
A. H. Thieken ◽  
M. Müller ◽  
L. Kleist ◽  
I. Seifert ◽  
D. Borst ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Census Data ◽  
Population Data ◽  
Dasymetric Mapping ◽  
Unit Value ◽  
Land Cover Data ◽  
Mapping Techniques ◽  
Asset Data

Abstract. In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

scholarly journals Exploring the Relationship between Urbanization and the Eco-Environment: A Case Study of Beijing

2019 ◽  
Vol 11 (22) ◽  
pp. 6298 ◽  
Author(s):  
Yi Huang ◽  
Qianqian Qiu ◽  
Yehua Sheng ◽  
Xiangqiang Min ◽  
Yuwei Cao
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Local Development ◽  
Coupling Model ◽  
Spatiotemporal Analysis ◽  
Landsat Images ◽  
Major Function ◽  
Stage Of Development ◽  
Land Cover Data ◽  
Functional Zones

Beijing is one of the most developed cities in China and has experienced a series of environmental problems. In accordance with the Major Function Zone planning, Beijing is divided into four zones in an attempt to coordinate development between urban areas and the eco-environment. Classic coupling model uses statistical data to evaluate the interactions of these two subsystems; however, it lacks the capability to express dynamic changes to land cover. Thus, we extracted land cover data from Landsat images and examined the urbanization and eco-environment level as well as the coupling coordination in Beijing and its functional zones. The main conclusions are as follows. (1) Between 2001 and 2011, both urbanization and the eco-environment level in Beijing and its functional zones grew steadily. Different zones coordinated together according to their own characteristics, and the overall coupling coordination of the city transformed from the “basically balanced” to the “superiorly balanced” stage of development. (2) After 2011, the condition of the eco-environment worsened in Beijing and in most of the function zones, while the coordination between increased urbanization and the worsened eco-environment may be a result of environmental lag. This study integrated land cover data into the coupling mode and fully utilized the advantages of spatiotemporal analysis and the coupling model. In other words, the spatiotemporal analysis explains the land cover changes visually over the research period, while the coupling model explores the interaction mechanisms between urbanization and the eco-environment. The land cover data enriches the coupling theory and provides a reference for evaluating the effectiveness of local development policy.

scholarly journals Highlighting the Sustainability Implications of Urbanisation: A Comparative Analysis of Two Urban Areas in Ghana

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 300 ◽  
Author(s):  
Kwasi Anarfi ◽  
Ross A. Hill ◽  
Chris Shiel
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Land Tenure ◽  
Urban Areas ◽  
Local Level ◽  
Population Data ◽  
Temporal Analysis ◽  
Resource Extraction ◽  
Market Forces ◽  
Spatio Temporal

Ghana is urbanising rapidly, and over half of the country’s population have lived in urban areas since 2010. Although research has proliferated to explore Ghana’s urbanisation, there is a dearth of research that holistically explores the wider sustainability implications of urbanisation, offers comparative perspectives in the context of large and smaller urban areas, and provides a perspective of local level urbanisation in the context of resource extraction (mining). This study comparatively assesses two urban areas in Ghana (Kumasi and Obuasi), by conducting a spatio-temporal analysis of land cover change through remote sensing and by analysing demographic change through a synthesis of published population data, in order to highlight the sustainability implications of urbanisation. The results show that urbanisation has been rapid, and has resulted in changes in land cover and demography in Kumasi and Obuasi. The sustainability implications of urbanisation are identified to include limited economic opportunities, socio-spatial segregation, and destruction of natural vegetation. The evidence in this study provides insights into urbanisation in Ghana, and suggests that the positive sustainability impacts of urbanisation may be eroded by how factors such as market forces and land tenure interact at the local level.

Spatial Econometric Models for Panel Data

2005 ◽  
Vol 1902 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Christopher Frazier ◽  
Kara M. Kockelman
Keyword(s):  
Land Cover ◽  
Satellite Images ◽  
Agricultural Land ◽  
Population Data ◽  
Demographic Variables ◽  
Vehicle Ownership ◽  
Spatial Econometric ◽  
Spatial Econometric Models ◽  
Land Cover Data ◽  
Complicated Task

Cities are constantly evolving, complex systems, and modeling them, both theoretically and empirically, is a complicated task. However, understanding the manner in which developed regions change over time and space can be important for transportation researchers and planners. In this paper, methodologies for modeling developed areas are presented, and spatial and temporal effects of the data are incorporated into the methodologies. The work emphasizes spatial relationships between various geographic, land use, and demographic variables that characterize fine zones across regions. It derives and combines land cover data for the Austin, Texas, region from a panel of satellite images and U.S. Census of Population data. Models for population, vehicle ownership, and developed, residential, and agricultural land cover are estimated; the effects of space and time on the models are shown to be statistically significant. Simulations of population and land cover for the year 2020 help to illustrate the strengths and limitations of the models.

scholarly journals DASYMETRIC METHODS APPLIED TO JACAREPAGUÁ WATERSHED

2017 ◽  
Vol 23 (4) ◽  
pp. 606-622
Author(s):  
Otto Marques dos Santos Neves ◽  
Julia Celia Mercedes Strauch ◽  
Cesar Ajara
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Supervised Classification ◽  
Population Data ◽  
Likelihood Method ◽  
Dasymetric Mapping ◽  
Auxiliary Data ◽  
Original Population ◽  
Different Types ◽  
High Resolution Images

Abstract: 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.

scholarly journals LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 17-21
Author(s):  
J. R. Bergado ◽  
C. Persello ◽  
A. Stein
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Land ◽  
Urban Land Use ◽  
The Other ◽  
Classification Task ◽  
Land Use Classification ◽  
Convolutional Network

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.

scholarly journals Suburban frog ponds: mapping local land cover yields different estimates of landscape composition than the National Land Cover Database

2014 ◽  
Author(s):  
Max Lambert
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Scales ◽  
Landscape Composition ◽  
Ecological Processes ◽  
Simple Method ◽  
Fine Grain ◽  
Land Cover Data ◽  
Ecological Patterns

Suburban neighborhoods are rapidly spreading globally. As such, there is an increasing need to study the environmental and ecological effects of suburbanization. At large spatial extents, from county-level to global, remote sensing-derived land cover data, such as the National Land Cover Dataset (NLCD), have yielded insight into patterns of urbanization and concomitant large-scale ecological patterns in response. However, the components of suburban land cover (houses, yards, etc.) are dispersed throughout the landscape at a finer scale than the relatively coarse grain size (30m pixels) of NLCD may be able to detect. Our understanding of ecological processes in heterogeneous landscapes is reliant upon the accuracy and resolution of our measurements as well as the scale at which we measure the landscape. Analyses of ecological processes along suburban gradients are restricted by the currently available data. As ecologists are becoming increasingly interested in describing phenomena at spatial extents as small as individual households, we need higher-resolution landscape measurements. Here, I describe a simple method of translating the components of suburban landscapes into finer-grain, local land cover (LLC) data in GIS. Using both LLC and NLCD, I compare the suburban matrix surrounding ponds occupied by two different frog species. I illustrate large discrepancies in Forest, Yard, and Developed land cover estimates between LLC and NLCD, leading to markedly different interpretations of suburban landscape composition. NLCD, relative to LLC, estimates lower proportions of forest cover and higher proportions of anthropogenic land covers in general. These two land cover datasets provide surprisingly different descriptions of the suburban landscapes, potentially affecting our understanding of how organisms respond to an increasingly suburban world. LLC provides a free and detailed fine-grain depiction of the components of suburban neighborhoods and will allow ecologists to better explore heterogeneous suburban landscapes at multiple spatial scales.

scholarly journals Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017

2019 ◽  
Vol 11 (11) ◽  
pp. 3047 ◽  
Author(s):  
Rongfeng Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Liang Hong ◽  
Xiaolu Zhou
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Agricultural Land ◽  
Temporal Evolution ◽  
Driving Factors ◽  
Deforestation Rate ◽  
Land Cover Data ◽  
Spatio Temporal ◽  
Landsat Satellite

Myanmar, abundant in natural resources, is one of the countries with high forest cover in Southeast Asia. Along with its rapid socio-economic development, however, the construction of large-scale infrastructure, expansion of agricultural land, and an increasing demand for timber products have posed serious threats to the forests and significantly affected regional sustainable development. However, the geographical environment in Myanmar is complex, resulting in the lack of long-term sequence of land cover data products. Based on 30 years’ Landsat satellite remote sensing imagery data and the land cover data extracted by a mixed classification method, this paper examined the spatial and temporal evolution characteristics of forest cover in Myanmar and investigated driving factors of the spatio-temporal evolution. Results show that the forest cover has decreased by 110,621 km2 in the past 30 years with the annual deforestation rate of 0.87%. Cropland expansion is the main reason for the deforestation throughout the study period. The study can provide basic information of the forest cover data to the Myanmar government for ecological environment protection. At the same time, it can provide important support to the “Belt and Road” initiative to invest in the region’s economy.

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