Une méthode pour l’estimation désagrégée de données de population à l’aide de données ouvertes

International audienceIn this article we present a method to perform dissagregated population estimation at building level using open data. Our goal is to estimate the number of people living at the fine level of individual households by using open urban data and coarse-scaled population data. First, a fine scale description of residential land use per building is built using OpenStreetMap. Then, using coarse-scale gridded population data, we perform the down-scaling for each household given their containing area for residential usage. We rely solely on open data in order to ensure replicability, and to be able to apply our method to any city in the world, as long as sufficient data exists. The evaluation is carried out using fine-grained census block data for cities in France as ground-truth.Nous présentons dans ce travail une méthode de désagrégation pour l'estimation de population à l'échelle locale à partir de données ouvertes globales. Notre but est d'estimer notamment le nombre de personnes résidant dans chaque bâtiment de la zone d'intérêt, à partir de données à plus grandé echelle. Une description fine à l'échelle résidentielle est tout d'abord effectuée à partir des données d'OpenStreetMap. Les surfaces des bâtiments d'habitation ou d'usage mixte (habitation et activités) sont notamment identifiées. Nous effectuons ensuite une désagrégation à partir de données de grille de population à grandé echelle (1km2 par carreau), guidée par les surfaces des bâtiments compris dans chaque carreau de la grille. Ensuite, nous effectuons une désagrégation à partir de données de grille de population à grande échelle (1km2 par carreau), guidée par les distributions spatiales découvertes à l'étape précédente. Nous utilisons exclusivement des données ouvertes pour favoriser la réplicabilité et pour pouvoir appliquer notre méthode à toute région d'intérêt, pour peu que la qualité des données soit suffisante. L'évaluation et la validation du résultat dans le cas de plusieurs villes Françaises sont effectuées à l'aide de données de recensement INSEE

Mapping Landcover Change and Population Displacement of Lakshmipur District, Bangladesh due to Riverbank Erosion From 2001-2021: A Geospatial Approach

Due to the geographical setting, the Lakshmipur district of Bangladesh experiences adverse effects of global climate change that include but are not limited to natural disasters such as tropical cyclones, storm surges, coastal flooding, and riverbank erosion. While riverbank erosion is an implicit impact of climate change, it directly affects human settlements, agricultural activity, and the overall livelihoods of people in this area. Examining the spatiotemporal changes in land cover and population due to riverbank erosion in this region could help us better understand the dynamics of human-environmental relations. This study aimed to classify land cover for every five years from 2001-2021, examine land cover changes in this area from 2001-2021, map populations in Lakshmipur district for every five years from 2001-2021, and estimate population displacement due to riverbank erosion every five years from 2001-2021. Landsat 5 TM 30 m satellite Imagery from 2001-2011 and Landsat 8 OLI 30 m resolution Imagery from 2016-2021 were used to classify landcover and observed landcover changes from 2001-2021. We classified Imagery using the smile random forest classifier in Google Earth Engine and calculated landcover change using the Change Detection Wizard in ArcGIS Pro 3.0. The overall classification accuracies range between 79.04% to 87 %. Our landcover change result suggests that from 2001-2021 fallow land/agricultural land has lost the largest area of land, 341.81 sq km, to homestead forest and waterbody among all the classes. To map populations vector and raster-based dasymetric mapping approaches were used. The vector and raster-based binary dasymetric mapping and population displacement calculation were carried out in ArcGIS Pro 3.0. The findings suggest that the lowest number of population displacements (1844 people using vector-based approach and 5241 raster-based approach) happened from 2001-2006, and the highest number (86107 people using vector-based approach, 63453 using raster-based approach) were displaced between 2016-2021. INDEX WORDS: Landcover Change, Population Mapping, Google Earth Engine, Riverbank Erosion, Dasymetric mapping, Population displacement, Lakshmipur District, Bangladesh

Spatial reallocation of areal data - a review

The analysis of socio-economic data often implies the combination of data bases originating from different administrative sources so that data have been collected on several separate partitions of the zone of interest into administrative units. It is therefore necessary to reallocate the data from the source spatial units to the target spatial units. We propose a review of the literature on statistical methods of spatial reallocation rules (spatial interpolation). Indeed one can distinguish several types of reallocation depending on whether the initial data and the final output are areal data or point data. We concentrate here on the areal-to-areal change of support case when initial and final data have an areal support with a particular attention to disaggregation for continuous data. There are three main types of such techniques: proportional weighting schemes also called dasymetric methods, smoothing techniques and regression based interpolation

Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This project studies employment containment in Melbourne, Australia. Employment containment is a measure of the proportion of people that work in a location close to their home. Recent urban planning policies in Melbourne have aimed to improve employment containment in the city’s suburbs. While there has been analysis of the rates at which people both live and work within broadly defined ‘local areas’, little work has been done to investigate employment containment using smaller and more uniform catchment areas as the unit of analysis. This research attempts such a finer scale analysis using dasymetric downscaling techniques. A regression modelling approach supported by land use data, alongside a binary dasymetric method, is used to develop fine scale estimates of employment distribution, while binary and populationdensity weighted methods are used to develop a fine scale estimate of working population distribution. For the employment distribution estimate, the Poisson model that distributed employment to employment-related land use classes produced the smallest error. However, the error produced by this model is still high. For the working population distribution estimate, the population-density weighted estimate is the more accurate of the approaches, and overall produced low error. For the employment containment analysis, a number of employment centres were randomly selected and an employment containment catchment has been derived from a 5 km2 commuting distance catchment. Commuting flows from an origin-destination matrix were areaweighted to estimate flows into the employment centre from the 5 km2 catchment. The method is found to be potentially useful; however inspecting the results of this employment containment calculation highlighted flaws in the current estimates that should be addressed before the measures can be used to further analyse employment containment in Melbourne. Improvements to this method would support urban strategic and transport planning analyses at a metropolitan-wide scale

Identifying barriers to sustainable food production by low resource producers and purchase by low income consumers in Washington and Beaufort Counties, North Carolina

Serving the interests of our client, Resourceful Communities of the Conservation Fund, our project investigates ways to better connect low-resource producers and low-income consumers of fresh produce in 31 low-income counties in NE North Carolina. To better characterize barriers rural producers and consumers face to produce and access healthy food, we conducted three separate analyses. A general linear model statistical analysis based on the USDA Food Environment Atlas data was used to identify significant demographic and socioeconomic variables that affect food access at the macro-level. For a qualitative analysis, surveys and interviews were used to define barriers producers and consumers face on the intra-county scale. Using Geographic Information Systems, a spatial analysis was developed to understand spatial patterns of food deserts and access barriers. The qualitative and spatial analyses were focused on two low-income counties: Beaufort County and Washington County, NC Community stakeholders, local food producers, consumers, and grocery retailers were interviewed. The statistical analysis focused both on 31 target North Carolina counties and on the entire Eastern Coastal plain. Two general linear models revealed that persistent poverty counties and counties experiencing population loss were more likely to experience little or no access to grocery stores. Race was also a factor, particularly within North Carolina where minorities are more vulnerable to food insecurity. Both Washington and Beaufort Counties exhibit a high level of economic and demographic stratification. Two-thirds of consumers from the survey had problems stretching their food budget, and identified a weekly food box at low or no-cost as the best intervention. Retail grocery stores already can and do buy local food. However, retailers buy locally according to the season and price. Major barriers to connecting low-resource producers and low-income consumers were identified as the decrease in the number of small farms, increasing bureaucracy, high cost of entry, and historical divisions between ethnic and socioeconomic groups. Using the geographic and socio-economic barriers, the spatial analysis identified three food deserts, in SE Beaufort County, NE Beaufort County, and SW Washington County and the main drivers for each

Evaluating the Potential of a Geospatial/Geostatistical Methodology for Locating Rain-Derived Infiltration and Inflow into Wastewater Treatment Systems in the Minneapolis/St. Paul Metropolitan Area, Minnesota, USA

A significant issue facing municipal wastewater treatment infrastructure (WWTI) is how to manage infiltration and inflow (I/I). I/I of rain and ground water permeate into WWTI after precipitation events, periods of groundwater table rise, and percolation from surrounding surface waters. This can create discharges above the infrastructure\u27s flow capacity, increase costs for processing the wastewater and add undesired stress to aging wastewater networks. In an attempt to assess this problem cost and time inefficient approaches have commonly been applied. This study utilizes a new and more radical methodology to try and make WWTI management more efficient. This study applies ArcGIS and Geostatistical Analysis to seven counties within the Metropolitan Council Environmental Services (MCES) network in the Minneapolis/St. Paul metro area. Data is collected from rain gauges and flow meters an average ten-year flow record is created from this data. The data is then analyzed in ArcGIS through Kriging to interpolate and predict where significant rates of I/I, due to high magnitude precipitation events, are located throughout the study area. I/I rates for high magnitude precipitation events are estimated through the comparison of the max flow rate data and the ten-year average flow rate. A percentage of increase flow is then calculated. Results reveal spatial patterns indicating variable I/I susceptibility across the MCES WWTI. By collaborating with MCES it is possible to determine how accurately this methodology can locate areas of high-risk I/I potential within the existing WWTI

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

Population density estimation using regression and area-to-point residual Kriging

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

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management