Atlas cybercartographique pilote représentant les risques d’itinérance


Published on

L’atlas pilote présente certaines façons de comprendre les questions de structure de l’itinérance au Canada en utilisant des représentations graphiques dynamiques.

Tracey Lauriault,
Chef de la recherche en matière de projet,
Université Carleton

Published in: Real Estate, Business
1 Like
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • The Atlas is guided by the theory and application of cybercartography. Cybercartography sees location as central to knowledge integration in the emerging information society and part of the growing domain of maps on the Internet. Cybercartography: Theory and Practice (ed. and contributor). Volume 4 in Modern Cartography Series, Amsterdam: Elsevier, 2005, pp. 574.
  • The GCRC specializes in the creation of cybercartographic atlases. Its team includes cartographers, ontologists, audio visual experts, programmers, geomaticians, visualization specialists among many other experts. The GCRC is not a subject matter specialist on the topic of housing and homelessness. The GCRC applies its skills to enable specialist, researchers, public officials, policy makers, community groups, statisticians and scientists to translate, model and mobilize their knowledge into an atlas.
  • To increase understanding of the topic of the risks of homelessness, more specifically structural issues, by the general public and provide decisions makers with new ways of seeing the impact of policy interventions. Homelessness is a complex problem with considerable variation both across Canada and within individual urban areas. The Atlas presents the variability of a select number of indicators in ways in which people can better understand the issues.
  • The FCM QoLRS technical team collaboratively selected a variety of indicators associated with the risk of homelessness and track these across time: - households that spend more than 50% of their income on rent; - people living in substandard housing; - those on social housing waiting lists; - the poor - those who are living on low, insecure or feeble incomes, people on fixed incomes such as seniors or those receiving social assistance, - and some demographic groups such as lone parent families.
  • Cybercartographic atlases are “living atlases” and are designed in a modular fashion to facilitate the addition of new content. This modularity, combined with the transdisciplinary cybercartographic approach, enables participants to mobilize their knowledge from multiple perspectives and present these in an atlas format. Users do not require advanced technical skill and the cybercartographic approach is particularly well suited to the needs of policy makers, researchers, public and community based organizations. We have changed our programming toward a Java Script approach that will allow atlases to be accessible in multiple browsers (e.g. IE 8). Early software development was experimental. To view the current Atlas you require a HIGH SPEED INTERNET CONNECTION and a recent version of SAFARI, FIREFOX and/or Google's CHROME. It is based on the Nunaliit Framework. Due to the advanced, W3C (XHTML + SVG) standards compliant nature of the technology powering the atlas. IE did not meet those standard when we began developing in 2002.
  • Users arrives at the Atlas Introduction page. The text on the map links to thematic modules. The modules are also available in the text window on the right of the map. The text also provides a variety of administrative information related to the Atlas. Additional cities and themes could easily be added to this page as the Atlas grows. All the maps are interactive. A series of tabs at the bottom can be selected and these will change the map view. The scroll bar on the right moves up and down to enable the reading of the full text and the text within is hyperlinked to other related content. The map itself is also interactive as the cursor scrolls over municipal/neighbourhood/social housing data pop up in a window to provide more information. Also a region can be selected and will stay highlighted as the user navigates across topics in the tabs. This allows for the comparison across variables or time. The user can also zoom in and out to see the data at different scales. Other multimedia content can be added such as video, images, audio and these can also be accessed from the map.
  • The GraphoMap Module includes 3 FCM QoLRS risk of homelessness indicators, 11 data variables for 22 cities, at three time intervals. It was designed by Dr. Sebastien Caquard. It abstracts Canada’s geography into a 180º semi circle with QoLRS cities located relationally and distance wise from east to west. Circles proportionally represent the value of a particular variable in real numbers. For instance, the circles on the Vacancy Rate indicator represent the Total Number of Rented Dwellings for a particular QoLRS municipally and year. The Position of the Point on the Radial Line works as follows: the closer a point is to the centre of the semi circle, the higher the risk of homelessness for that particular variable. A video accompanies the GraphoMap to explain how it works just in case a user needs some pointers. Interactivity and a well-designed visualization can make accessible great complexity relatively easily when compared to data tables on multiple pages in a PDF report.
  • The location of the proportionally sized dots on the Lone Parent Families Spending variable represents the Percent of Lone-Parent Family Households spending 50% or more of Household Income Spent on Rent for a given year as a proportion of the total number of Lone-Parent Family Renters that same year. The higher the proportion of these Lone Parent Households being over extended the higher the risk of homelessness for that year.
  • This series of maps represents the spatial interpolation of the percentage of both the Low Income Cut Off (LICO) and the households spending more than 30% in rent (30% plus). This interpolation is based on data provided at the EA scale (1991) and DA scale (2001 and 2006). How to read this map: the darker areas represent the higher percentages, either in terms of LICO or 30% plus. For instance we can see an important increase of the percentage of households spending more than 30% in rent (30% plus) between 1996 and 2001. The City of Calgary Module tells the story of the economic risk of becoming homeless. The variables shown are Low Income (LICO) and also those who spend 30% + of their income on rent. The city wanted to see if these economic risk data could be rendered in such a way as to demonstrate variability within City of Calgary neighbourhoods. Often it is perceived that neighbourhoods show contiguous population groupings while the reality is that neighbourhoods depending on their historical evolution and where they are located vary.
  • This is a detailed zoomed in view with the same locale selected. This allows the user to navigate indicators and time for the same location.
  • The CMM Atlas Module tells the story of social housing and housing affordability for lower-income renting populations. The demand for and the supply side of social, affordable and different types of housing providers and programs are part of this module. Affordability remains an issue for many in the CMM while the supply does not meet the demand. This module shows the distribution of these data in both real numbers and proportionally for all 82 CMM municipalities. The CMM contributed these data to the project and crafted the text to accompany the interactive maps. Points display absolute values (numbers) ‏ Colours display percentages for the same criteria.
  • The City of Toronto Atlas Module tells the story of its aging social housing stock. The City is faced with the daunting and expensive task of managing a large inventory of social housing that is aging, in need of repair and refurbishing to meet current energy efficiency and heating standards. It also has a large social housing waiting list, indicating that it cannot fully meet the current demand. The City contributed their Toronto Community Housing data. This dataset includes the location, name, number of rent geared to income (RGI) units and the year of construction for all city managed social housing. The data were aggregated by city neighbourhood and by decade. On this map the dark green circles represent the decade selected and the lighter green represent the construction of social housing prior to that. The story quickly becomes obvious as the construction boom for social housing appears in the 60s and 70s, which much stock built earlier, and drops in the 90s and tapers to only one new construction in 2003. We therefore get both an aging story and imagine the repair issues of a stock built according to old standards and generally very cheaply. The images also reveal some of the political changes in the priorities for social housing in the City of Toronto.
  • This was the first iteration which included the location of social housing by building and the decade of construction. This was considered to be useful but less informative than the former (what we have just seen) and also very slow to load on the Internet. Both versions are made available to users while the aggregated by neighbourhood (Green Dots) is the primary module.
  • The Canada Module was created to assess data variability of Census Subdivisions (CSDs) in Canada across time. CSDs are the Statistics Canada geographic units that represent the administrative boundaries of cities and municipalities. The points on the map represent the current 24 QoLRS cities. Researchers also wanted to develop a rate of change map series to show change in terms of units available for sale and for rent by CSD. MADGIC Data Liberation Initiative Census data were used to create these maps. We had another theme in mind but we were informed by Statistics Canada that one cross tabulation would cost upwards of $60 000. We dutifully declined and experimented. We believe the Rate of Change methodology we developed is useful, albeit difficult to understand by general users. Also CSD geographies have radically changed across time due to the wave of amalgamations across the country and with the advent of Nunavut. We have concluded that the results of this experiment are inaccurate. We would have liked to have been able to acquire data adjusted to 2001 boundaries. However, those are terribly expensive and are not part of the Data Liberation Initiative. Finally, because we are not housing experts, and we do not have experts positioned locally across the country, we could not explain the variations we were seeing in the maps. We kept this map to tell a cautionary tale for other researchers and to discuss data access in Canada. “One may need to mortgage the house to afford studying homelessness in Canada”.
  • HRSDC Proposal to build upon the pilot and create an atlas of homelessness in Canada. Includes FCM, Cities, Province of Ontario and HIFIS. Preparing a SSHRC CURA Letter of Intent proposing to work with academic & community based researchers and Homelessness Coalitions. The team includes researchers from geography, cartography, health, social policy, psychology and comparative criminology. It also represents many cities and regions in Canada. This aim is to mobilize existing research and render those into the Atlas, to build local mapping capacity and a community based geodata infrastructure.
  • Atlas cybercartographique pilote représentant les risques d’itinérance

    1. 1. Atlas cybercartographique pilote du risque d’itinérance au Canada Comité national de recherche sur le logement Ottawa, 2 novembre 2009 <ul><li>Participants : Tracey P. Lauriault ( [email_address] ), responsable de la recherche </li></ul><ul><li>D. R. Fraser Taylor, chercheur principal </li></ul><ul><li>Cartographe : Sebastien Caquard, Ph.D. </li></ul><ul><li>Géomaticienne : Christine Homuth </li></ul><ul><li>Grâce à la contribution de Glenn Brauen, Amos Hayes et Jean-Pierre Fiset </li></ul>
    2. 2. Table des matières <ul><li>Cybercartographie </li></ul><ul><li>Financement, partenariat et collaboration </li></ul><ul><li>Raison d’être de l’Atlas du risque d’itinérance </li></ul><ul><li>Sources de données – intégration des connaissances </li></ul><ul><li>À propos de l’Atlas </li></ul><ul><li>Page d’accueil de l’Atlas </li></ul><ul><li>Grandes villes : indicateurs des villes du SRQDV au fil du temps </li></ul><ul><li>Ville de Calgary : seuil de faible revenu (SFR) et 30 % du revenu consacré au loyer </li></ul><ul><li>Ville de Toronto : vieillissement du parc de logements sociaux </li></ul><ul><li>Grand Montr éal : logements sociaux et populations ayant des difficultés financières pour se loger </li></ul><ul><li>Canada : comparaison entre les locataires et les propriétaires – Qu’est-ce qui ne va pas sur cette carte? </li></ul><ul><li>Mobilisation des connaissances : analyse multiscalaire de divers points de vue à différentes échelles </li></ul><ul><li>Coordonnées </li></ul>
    3. 3. Qu’est-ce que la cybercartographie? « L’organisation, la présentation, l’analyse et la communication d’informations à référence spatiale sur une grande variété de sujets d’intérêt pour la société dans un format interactif, dynamique, multimédia, multisensoriel et multidisciplinaire. » D.R. Fraser Taylor (2005, 2008)
    4. 4. Financement, partenariat et collaboration <ul><li>Atlas cybercartographique pilote du risque d’itinérance </li></ul><ul><li>Financement </li></ul><ul><ul><li>Projets d’élaboration de données sur l’itinérance, Programme de développement des connaissances sur l’itinérance, Secrétariat des partenaires de lutte contre l’itinérance de Ressources humaines et Développement des compétences Canada (RHDCC) ‏ </li></ul></ul><ul><li>Partenariat </li></ul><ul><ul><li>Système de rapports sur la qualité de vie (SRQDV) de la Fédération canadienne des municipalités (FCM) (24 villes) ‏ </li></ul></ul><ul><li>Deux villes et une région métropolitaine </li></ul><ul><ul><li>Ville de Calgary </li></ul></ul><ul><ul><li>Ville de Toronto </li></ul></ul><ul><ul><li>Communaut é m é tropolitaine de Montr é al (CMM) </li></ul></ul>
    5. 5. Raison d’être de l’Atlas du risque d’itinérance ‑ Accroître la compréhension à l’égard de l’itinérance ‑ Cartographier des données pour les rendre utiles, concrètes, intéressantes et accessibles ‑ Représenter des questions structurelles au moyen d’interfaces visuelles et interactives ‑ Stimuler la réflexion sur la prévention ‑ Proposer au public et aux décideurs de nouvelles manières d’évaluer l’incidence des interventions en matière de politique au fil du temps ‑ Les atlas sont des objets-frontières facilitant la présentation de données et le récit d’histoires de divers points de vue. ‑ Les méthodes cybercartographiques permettent la collaboration entre les secteurs, les disciplines et les mandats, de même qu’entre les frontières territoriales institutionnelles et administratives.
    6. 6. Sources de données – intégration des connaissances <ul><li>Système de rapports sur la qualité de vie (SRQDV) de la Fédération canadienne des municipalités ( FCM ) </li></ul><ul><ul><li>Société canadienne d’hypothèques et de logement ( SCHL ) </li></ul></ul><ul><ul><li>Statistique Canada , recensements et tableaux croisés spéciaux </li></ul></ul><ul><li>Maps, Data, and Government Information Centre ( MADGIC ), bibliothèque, Université Carleton </li></ul><ul><ul><li>Initiative de démocratisation des données (IDD) des recensements de Statistique Canada </li></ul></ul><ul><ul><li>Cartes numériques de la Division de la géographie de Statistique Canada (secteurs de dénombrement [SD], aires de diffusion [AD], secteurs de recensement [SR], divisions de recensement [DR], subdivisions de recensement [SDR], provinces, représentation politique du Canada et villes) </li></ul></ul><ul><li>Ville de Toronto </li></ul><ul><ul><li>Social Policy Analysis and Research Section : dossier sur les quartiers </li></ul></ul><ul><ul><li>Housing Connections de Toronto : registre des logements sociaux </li></ul></ul><ul><ul><li>Toronto Community Housing Corporation : données sur les logements sociaux </li></ul></ul><ul><li>Ville de Calgary </li></ul><ul><ul><li>Community and Neighbourhood Services, Social Policy and Planning Division : dossier sur les quartiers </li></ul></ul><ul><li>Communauté métropolitaine de Montréal (CMM) </li></ul><ul><ul><li>Direction des politiques et interventions de développement : tabulations spéciales des données de recensement, carte de base de la CMM et données sur le logement </li></ul></ul>
    7. 7. À propos de l’Atlas <ul><li>Élaboré à partir du cadre de l’Atlas cybercartographique Nunaliit </li></ul><ul><ul><li>« Nunaliit » signifie « établissement », « communauté » ou « habitat » en inuktitut </li></ul></ul><ul><ul><li>Le code Nunaliit est distribué sous la nouvelle licence Berkeley Software Distribution (BSD) </li></ul></ul><ul><ul><li> </li></ul></ul><ul><li>Conception modulaire </li></ul><ul><li>Conçu pour raconter des histoires </li></ul><ul><li>Possibilité d’ajouter du contenu </li></ul><ul><li>Autres atlas : </li></ul><ul><ul><li>Atlas cybercartographique du cinéma canadien </li></ul></ul><ul><ul><li>Projet d’utilisation et d’occupation des glaces marines par les Inuits </li></ul></ul><ul><ul><li>Living Cybercartographic Atlas of Indigenous Perspectives and Knowledge (atlas cartographique vivant des points de vue et des connaissances autochtones) </li></ul></ul><ul><ul><li>Atlas of Arctic Bay (atlas de la baie de l’Arctique) </li></ul></ul><ul><ul><li>Atlas Kitikmeot des noms de lieux </li></ul></ul><ul><ul><li>Cybercartographic Atlas of Canada's Trade with the World (atlas cybercartographique du commerce mondial du Canada) </li></ul></ul><ul><ul><li>Cybercartographic Atlas of Antarctica (atlas cybercartographique de l’Antarctique) </li></ul></ul>
    8. 8. Présentation de l’Atlas pilote du risque d’itinérance
    9. 9. Grandes villes : indicateurs des villes du SRQDV au fil du temps
    10. 10. GraphoMap : indicateurs des villes du SRQDV au fil du temps 50% + Income Spent on Rent
    11. 11. Ville de Calgary : SFR et 30 % du revenu consacré au loyer
    12. 12. Ville de Calgary : SFR et 30 % du revenu consacré au loyer
    13. 13. Grand Montréal: Grand Montréal: Logements sociaux et populations ayant des difficultés financières pour se loger
    14. 14. Vieillissement du parc de logements sociaux par quartier : Toronto
    15. 15. Détails par quartier : Toronto
    16. 16. Vieillissement du parc de logements sociaux par édifice : Toronto
    17. 17. Détails par édifice : Toronto
    18. 18. Canada : comparaison entre les locataires et les propriétaires s
    19. 19. Mobilisation des connaissances : analyse multisectorielle de divers points de vue à différentes échelles <ul><li>Groupe de travail sur le SRQDV de la FCM (planificateurs, dirigeants principaux de l’information, chercheurs du secteur social et intervenants en matière de logement) </li></ul><ul><li>Emplacement, quartiers, municipalités, régions métropolitaines et Canada </li></ul><ul><li>Représentants d’organismes non gouvernementaux, du milieu universitaire, de la CMM et des villes </li></ul><ul><li>L’histoire concerne le risque d’itinérance </li></ul><ul><ul><li>Les données du SRQDV permettent de construire l’histoire. </li></ul></ul><ul><ul><li>Les experts locaux racontent leur histoire au moyen de ces données (modules). </li></ul></ul><ul><ul><li>Les chercheurs du Centre de recherche en géomatique et cartographie (CRGC), en collaboration avec les experts locaux, présentent ces données dans des cartes interactives et des interfaces visuelles (atlas). </li></ul></ul><ul><ul><li>Ainsi, la question concrète est accessible aux usagers et aux décideurs. </li></ul></ul>
    20. 20. Étapes suivantes et coordonnées <ul><li>Proposition de RHDCC – FCM, villes, province de l’Ontario et Système d’information sur les personnes et les familles sans abri (SISA). </li></ul><ul><li>Préparation d’une lettre d’intention dans le cadre du programme des Alliances de recherche universités-communautés (ARUC) du Centre de recherches en sciences humaines (CRSH) </li></ul><ul><ul><li>Collaboration entre les chercheurs universitaires et communautaires, et les coalitions pour les sans-abri. </li></ul></ul><ul><ul><li>L’équipe est composée de chercheurs des secteurs de la géographie, de la cartographie, de la santé, de la politique sociale, de la psychologie et de la criminologie comparée. </li></ul></ul><ul><ul><li>L’objectif consiste à rassembler les recherches existantes pour les présenter dans l’Atlas, à renforcer la capacité locale en matière de cartographie et à créer une infrastructure communautaire relativement aux géodonnées. </li></ul></ul><ul><li>URL de l’Atlas : </li></ul><ul><li>Personne-ressource : Tracey P. Lauriault, </li></ul>