Migratiebewegingen en dynamische processen in de Brusselse wijken

Dit artikel onderzoekt de rol van de migratiebewegingen in de dynamische processen die leiden tot de sociaaldemografische transformatie van de wijken in het Brussels Hoofdstedelijk Gewest en formuleert enkele beschouwingen over de beleidsimplicaties daarvan. Uit deze analyse van de complexe migratiebewegingen in het Gewest kunnen enkele significante samenvattende conclusies worden getrokken. De armste gebieden van de stad, de zogenaamde “arme halve maan”, liggen op het kruispunt van uiteenlopende migratiebewegingen, die vooral gekenmerkt worden door de instroom van nieuwe migranten uit arme landen of landen uit de tussencategorie en door de uitstroom van de inwoners. De analyse toont evenwel ook aan dat de functie van die wijken niet helemaal kan worden herleid tot een transitfunctie, aangezien een groot deel van de bevolking er blijft wonen. In de rijkste gebieden van de stad, die in het zuidoostelijk kwadrant van het Gewest liggen, zijn er daarentegen veel minder grootschalige migratiebewegingen. Daar vestigen zich geen nieuwkomers noch gezinnen die wegtrekken uit de achtergestelde gebieden van de stad. Ze lijken in grote mate gesloten voor bevolkingsbewegingen.Cet article vise à cerner le rôle des mouvements migratoires dans les dynamiques de transformation socio-démographique des quartiers en région de Bruxelles-Capitale et à en tirer quelques réflexions en termes d’implications politiques. De cette analyse des mouvements migratoires complexes que connaît la Région, on peut tirer quelques éléments synthétiques forts. Les territoires les plus pauvres de la ville, dits du « croissant pauvre », sont à la croisée de mouvements migratoires divergents, marqués en particulier par l’arrivée de nouveaux immigrés issus de pays pauvres ou intermédiaires et le départ de populations résidentes. Néanmoins, l’analyse montre aussi que ces quartiers ne peuvent être entièrement réduits à une fonction de transit étant donné qu’une partie importante de leur population y demeure. Par opposition, les parties les plus riches de la ville, situées dans le cadrant sud-est de la Région, connaissent des mouvements migratoires beaucoup moins massifs. Ceux-ci n’accueillent ni les primo-arrivants, ni les ménages quittant les zones défavorisées de la ville. Ils apparaissent de la sorte largement fermés aux mouvements de population.This article is aimed at defining the role of migratory movements in the dynamics of the sociodemographic transformation of neighbourhoods in the Brussels-Capital Region, and at making some observations in terms of political implications. There are several significant summarising elements which may be drawn from this analysis of complex migratory movements in the Region. The poorest territories in the city – the “poor area” – are at the crossroads of diverging migratory movements, marked in particular by the arrival of new immigrants from poor or intermediate countries and the departure of resident populations. Nevertheless, the analysis also shows that these neighbourhoods may not be reduced to having a transit function, given that a significant proportion of their population remains there. In contrast, the richest parts of the city, located in the southeast quadrant of the Region, have experienced much less massive migratory movements. They are not home to newcomers or to households leaving the disadvantaged areas of the city. They thus appear to be closed for the most part to population movements

Migratory movements and dynamics of neighbourhoods in Brussels

This article is aimed at defining the role of migratory movements in the dynamics of the sociodemographic transformation of neighbourhoods in the Brussels-Capital Region, and at making some observations in terms of political implications. There are several significant summarising elements which may be drawn from this analysis of complex migratory movements in the Region. The poorest territories in the city – the “poor area” – are at the crossroads of diverging migratory movements, marked in particular by the arrival of new immigrants from poor or intermediate countries and the departure of resident populations. Nevertheless, the analysis also shows that these neighbourhoods may not be reduced to having a transit function, given that a significant proportion of their population remains there. In contrast, the richest parts of the city, located in the southeast quadrant of the Region, have experienced much less massive migratory movements. They are not home to newcomers or to households leaving the disadvantaged areas of the city. They thus appear to be closed for the most part to population movements.Cet article vise à cerner le rôle des mouvements migratoires dans les dynamiques de transformation socio-démographique des quartiers en région de Bruxelles-Capitale et à en tirer quelques réflexions en termes d’implications politiques. De cette analyse des mouvements migratoires complexes que connaît la Région, on peut tirer quelques éléments synthétiques forts. Les territoires les plus pauvres de la ville, dits du « croissant pauvre », sont à la croisée de mouvements migratoires divergents, marqués en particulier par l’arrivée de nouveaux immigrés issus de pays pauvres ou intermédiaires et le départ de populations résidentes. Néanmoins, l’analyse montre aussi que ces quartiers ne peuvent être entièrement réduits à une fonction de transit étant donné qu’une partie importante de leur population y demeure. Par opposition, les parties les plus riches de la ville, situées dans le cadrant sud-est de la Région, connaissent des mouvements migratoires beaucoup moins massifs. Ceux-ci n’accueillent ni les primo-arrivants, ni les ménages quittant les zones défavorisées de la ville. Ils apparaissent de la sorte largement fermés aux mouvements de population.Dit artikel onderzoekt de rol van de migratiebewegingen in de dynamische processen die leiden tot de sociaaldemografische transformatie van de wijken in het Brussels Hoofdstedelijk Gewest en formuleert enkele beschouwingen over de beleidsimplicaties daarvan. Uit deze analyse van de complexe migratiebewegingen in het Gewest kunnen enkele significante samenvattende conclusies worden getrokken. De armste gebieden van de stad, de zogenaamde “arme halve maan”, liggen op het kruispunt van uiteenlopende migratiebewegingen, die vooral gekenmerkt worden door de instroom van nieuwe migranten uit arme landen of landen uit de tussencategorie en door de uitstroom van de inwoners. De analyse toont evenwel ook aan dat de functie van die wijken niet helemaal kan worden herleid tot een transitfunctie, aangezien een groot deel van de bevolking er blijft wonen. In de rijkste gebieden van de stad, die in het zuidoostelijk kwadrant van het Gewest liggen, zijn er daarentegen veel minder grootschalige migratiebewegingen. Daar vestigen zich geen nieuwkomers noch gezinnen die wegtrekken uit de achtergestelde gebieden van de stad. Ze lijken in grote mate gesloten voor bevolkingsbewegingen

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Extending Data for Urban Health Decision-Making : a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs

Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products

Very‑high resolution earth observation data and open‑source solutions for mapping urban areas in sub-Saharan Africa. Implementation of an operational framework for production of geoinformation. Application on Ouagadougou (Burkina Faso) and Dakar (Senegal).

Nowadays, in sub-Saharan Africa (SSA), about 40% of the population is urban and this region is expected to face the highest growth rates during the next decades. By 2100, the three most populated cities in the world will be located in SSA. As a consequence of the extremely fast transformations experienced during the last decades, SSA cities are facing social and environmental issues combined with a lack of financial means and capacity in urban planning and management. The poorest often constitute a large part of the urban population that is extremely vulnerable to health and disaster risks.In SSA cities, up-to-date and spatially detailed geographic information is often missing. This lack of information is an important issue for many scientific studies focusing on different urban issues and there is a real need to improve the availability of geoinformation for these cities in order to support urban planning, urban management, environment monitoring, epidemiology or risk assessment, etc. The work presented in this thesis aims to develop different frameworks for the production of geoinformation. For this purpose, advantage is taken of Very-High Resolution Remote Sensing imagery (0.5 meters) and open-source software. These frameworks implement cutting-edge methods and can handle a large amount of data in a semi-automated fashion to produce maps covering very large areas of interest. In the spirit of open science, the processing chains are entirely based on open-source software and are released publicly in open-access for any interested researchers, in order to make the methods developed completely transparent and in order to contribute to the creation of a pool of common tools and scientific knowledge. These frameworks are used to produce very detailed land-cover and land-use maps that provide essential information such as the built-up density, or the fact that a neighborhood is residential or not. This detailed geoinformation is then used as indicators of presence of populated places to improve existing population models at the intra-urban level.Option Géographie du Doctorat en Sciencesinfo:eu-repo/semantics/nonPublishe

Opensource OBIA processing chain

0info:eu-repo/semantics/publishe

Open data with public repositories

Presentation on how to use Zenodo data repositories to make open-science easier.info:eu-repo/semantics/nonPublishe

Moving from proprietary to open-source solutions for academic research in remote sensing: Example with semi-automated land cover mapping

GRASS GIS has recently experienced significant improvements for Object-Based Image Analysis. At ULB the choice was made to combine GRASS GIS and Python in a semi-automated processing chain for land-cover mapping. The later proved its ability of being quickly customized in order to match the requirements of different projects. In order to promote the OSGEO software, we decided to make it freely available, allowing anyone interested to review, reuse and/or enhance it for further studies.info:eu-repo/semantics/nonPublishe

Street blocks features computation

info:eu-repo/semantics/publishe

Semi-automatic processing chain of VHR satellite imagery

With growth of big data in the remote sensing field, possibility for automation of geospatial analyses is more and more needed by researchers. However, not all of them have sufficient programming skills to develop processing routines for their usual tasks. GRASS GIS, an open-source software, provide different possibilities for automation of geospatial analyses, from very simple to the most advanced. This presentation aims to introduce the easiest ways to get started, from scratch, with automation of geo-spatial processing in GRASS GIS.info:eu-repo/semantics/nonPublishe