Academic literature on the topic 'Cartographie crowdsourcée'

In December 2010, HarassMap was launched as a Cairo-based interactive online mapping interface for reporting and mapping incidents of sexual harassment anonymously and in real time, in Egypt. The project’s use of spatial information technologies for crowdmapping sexual harassment raises important questions about the use of crowdsourced mapping as a technique of global human security governance, as well as the techno-politics of interpreting and representing spaces of gendered security and insecurity in Egypt’s urban streetscape. By recoding Egypt’s urban landscape into spaces subordinated to the visual cartography of the project’s crowdsourced data, HarassMap obscures the complex assemblage that it draws together as the differentially open space of the Egyptian street – spaces that are territorialized and deterritorialized for authoritarian control, state violence, revolt, rape, new solidarities, gender reversals, sectarian tensions, and class-based mobilization. What is at stake in my analysis is the plasticity of victimage: to what extent can attempts to ‘empower’ women be pursued at the microlevel without amplifying the similarly imperial techniques of objectifying them as resources used to justify other forms of state violence? The question requires taking seriously the practices of mapping and targeting as an interface for securing public space.

Abstract. Over the last half century, the world has witnessed rapid urbanization which is expected to increase over the near future. Use and property rights are reflected as a complex equation of overlapping interests. A fit-for-purpose approach for the initial registration of such rights is of a great importance both in formally and informally developed urban areas, as it may empower tenure security, improve property management, formalize property markets and enable poverty reduction. In this paper a more generalized framework, adjustable to the available cartographic infrastructure and funding availability in each region, for the simultaneous implementation of 2D and 3D property registration, based on a Mobile Cloud Computing (MCC) architecture, is proposed. A Database Management System (DBMS) based on the Land Administration Domain Model (LADM) standard is used for the management and storage of the collected data, while a prototype open-source mobile application for the collection of 2D and 3D crowdsourced cadastral data, the automatic 3D modelling and visualization of 3D property units as block models (LoD1) on a mobile’s phone screen in real-time is developed. A case study for a multi-storey building in an urban area of Athens, Greece, is presented. The first results seem to be interesting and promising. A Strengths, Weaknesses, Opportunities, and Threats (SWOT) assessment of the proposed framework, is conducted. The main conclusions referred to the potential and the perspectives of the proposed technical crowdsourced solution for the initial registration for the implementation of a Fit-For-Purpose 3D cadastral system are presented.

Abstract: One of the co-editors of this special issue of Histoire sociale / Social History, Marc Calvini-Lefebvre, is currently running a digital mapping project which seeks to crowdsource information about the sites of memory dedicated to the suffrage movement around the world and geolocate them on an open-software map. His initiative is one of many digital mapping projects launched by historians of feminism around the world as a means of spreading knowledge about the feminist past to ever-wider audiences but also of exploring new research questions via the methods of the digital humanities. To discuss the promise and limits of digital mapping, Histoire sociale / Social History invited Lucy Delap (University of Cambridge), Sarah Richardson (then University of Warwick, now University of Glasgow), and Marc Calvini-Lefebvre (Aix-Marseille Université) to participate in a forum chaired by Claire Sorin-Delpuech (Aix-Marseille Université). The conversation was edited by Marc Calvini-Lefebvre. L’un des co-rédacteurs de ce numéro spécial d’Histoire sociale / Social History, Marc Calvini-Lefebvre, dirige actuellement un projet de cartographie numérique dont le but est de rassembler des informations sur les lieux de mémoire dédiés au mouvement pour le droit de vote des femmes dans le monde entier et à les géolocaliser sur une carte en logiciel libre. Son initiative est l’un des nombreux projets de cartographie numérique lancés par des historiennes et historiens du féminisme à travers le monde afin de diffuser les connaissances sur le passé féministe auprès d’un public de plus en plus vaste, mais aussi pour explorer de nouvelles questions de recherche à l’aide de méthodes propres aux humanités numériques. Pour discuter des promesses et des limites de la cartographie numérique, Histoire sociale / Social History a invité Lucy Delap (Université de Cambridge), Sarah Richardson (à l’Université de Warwick au moment du forum mais désormais à l’Université de Glasgow), et Marc Calvini-Lefebvre (Aix-Marseille Université) à participer à un forum présidé par Claire Sorin-Delpuech (Aix-Marseille Université). La conversation a été éditée par Marc Calvini-Lefebvre.

<p><strong>Abstract.</strong> Despite the strong connection between art and emotion – and cartography’s position as both an art and a science – only recently has there been a strong call for cartographers to partake in research related to maps and emotion (Griffin and McQuoid 2012). Such research can typically be classified into one of three categories: (1) maps of emotions (i.e., visualizations of spaces as happy or fearful), (2) maps as tools for the collection of emotive data, and (3) affective responses to maps (Griffin and McQuoid). Of these, the first two have been the primary focus of most recent research in emotive cartography; this research has benefited greatly from the proliferation of participatory web-based technologies such as social media, smart phones, and fitness activity trackers.</p><p>The third category of affective map research – the study of emotional responses to maps – has received less attention, though some notable work (e.g., Fabrikant et al. 2012) has highlighted its importance. We suggest that such research on affective responses to maps might also benefit from an increased reliance on web-based data collection techniques. There has been, for example, a recent increase in the use of online crowdsourcing platforms such as Amazon Mechanical Turk for cartographic and related visual perception research (e.g., Heer and Bostock 2010; Limpisathian 2017). Such platforms decrease the cost of participant recruitment and survey dissemination, while facilitating easier testing of samples outside of the traditional undergraduate student subject pool.</p><p>Given the inherently personal and subjective nature of emotions, the use of crowdsourced samples to measure affective responses to maps poses an interesting dilemma. How might cartographers assess the emotive nature of maps and mapping products via participant samples that are both geographically dispersed and impersonal by design? As a case study, we present a user study recently conducted to measure participant responses to <i>affectively-congruent</i> (vs. incongruent) chorochromatic maps. By <i>affectively-congruent</i>, we mean maps with data topics whose emotive content is in harmony with the emotive nature of the colour scheme. For example, a thematic map about homicide with dull, muted, colours would be considered affectively-congruent, while one with bright, highly-saturated colours would be considered incongruent (Figure 1).</p><p>In this work, we discuss two contrasting methods for collecting and analysing emotive participant interpretations of these maps. First, with closed-ended survey questions, wherein study participants are asked to rate how much a map exhibits a type of emotion (e.g., positive). In our study, we used a modified 7-point version of the <i>Affective Slider</i>, a tool developed by Betella and Verschure (2016) for measuring emotion in web-based surveys. Thus, due to our 7-point scale, participants classified their rating of a map’s emotive content into one of 7 ranked categories. We use a between-subjects design and analysed participant responses using non-parametric tests.</p><p>The second method we discuss for analysing affective response to maps is the collection of free-form responses from survey respondents, followed by the classification of these responses using sentiment analysis. In our study, we asked participants to provide three words they would use to describe each map. We then categorized these responses as either positive, negative, or neutral using the <i>ParallelDots</i> Sentiment Analysis API. Thus, we did not ask participants to classify a map’s level of emotion specifically, but rather collected their more instinctive responses and categorized them later using text classification algorithms.</p><p>Following a discussion of the respective merits and drawbacks of these methods, we discuss how the selection of one of these contrasting approaches might influence affective cartographic research results. For example, in our study, while the Affective Slider proved a more efficient tool for analysing between-subject variance in emotive rankings of maps, sentiment analysis of free-form responses facilitated better analysis of within-subject variance. One participant, for example, listed both “exciting” and “depressing” in response to a single affectively-incongruent map, highlighting the confusion that can be triggered by a map with an incongruent design.</p><p>In closing, we discuss the multitude of challenges introduced by these methods – including the false precision sometimes provided by fine-grained survey scales and complex algorithms, and the paradoxical nature of personal data collection via anonymous participants. But despite these challenges we propose that crowdsourced surveys show strong promise for quantitative affective mapping research: future work is likely to result in new, exciting insights into user’s affective responses to maps and mapping products.</p>

A deficit framing continues to surround the perceived capabilities of people with dementia to comprehend and engage with cartographic maps. While some people with dementia might experience issues with wayfinding and spatial orientation, this has frequently been unfairly extended to all people living with dementia. From the perspective of qualitative research, the potentials for how mapping might contribute methodologically to understanding how some people with dementia negotiate and experience place has yet to be fully realised. A nascent turn towards geographic concepts of space and place has seen social health and dementia researchers acknowledging the vitality of place and neighbourhoods for understanding how everyday life unfolds relationally for people with dementia. However, creative methods to capture these spatial experiences are needed. This paper describes the nature of the scaffolds that supported involvement of people with dementia into a qualitative GIS project. These scaffolds were essential for supporting participants to trace out their narratives, histories, connections and barriers to engagement in their familiar neighbourhoods. We demonstrate via an Australian example how scaffolded sketch mapping interviews and crowdsourced maps offer tangible and evocative means to pin their lived experiences to the map. For place-based dementia research, qualitative GIS offers two paths forward. First, with the relevant scaffolds in place, the imaginative power of the map can engage people with dementia in qualitative research about the places they know and frequent. Second, spatial visualisations are imbued with political agency, acting as a conduit toward meaningful change in their local communities.

<p><strong>Abstract.</strong> Dynamic population estimation – counting people in a special event (e.g. rally, sport events, evacuation) has been challenging because a large crowd is difficult to acquire an accurate count manually as people can join and leave the crowd at any time and place. In the context of a rally where people are moving on the streets over an extended period, it is often difficult to answer some questions with regards to confine the phenomenon, including but not limited to: When and where does the rally start/end precisely? Who are the attendees (e.g. activists, spectators, organizers, police, opposing crowd(s), etc.)? How does the confining environment affect stochastic human behaviours, interactions and movements over time and space? What is the count (e.g. peak attendance, total count)?</p><p>Nevertheless, estimating the attendance of these dynamic events could be emotionally and politically charged. Due to these practical uncertainties of dynamic population estimation, the attendance of annual July 1st Rally in Hong Kong reported by the police and organizers could be very different as it often became a bluffing game to promote hidden political agenda. For example, the police estimated a peak attendance of 9,800 in the 2017 July 1st Rally, whereas the organizers reported five times the attendance at about 50,000. Independent researchers from the University of Hong Kong estimated the total attendance of 14,170 (Yip, 2017) and 29,000 (HKUPOP, 2017) respectively. With limited field observations, it is hard to examine the accuracy and confident level of these reported counts.</p><p>This research aims to estimate the total attendance of 2017 July 1st Rally in Hong Kong and examine the counts reported by various stakeholders. Based on many stories reported by the public and social media, the timeline of the rally event was reconstructed to trace the observed rally time of the head and tail crowds participated in the rally event. Important geospatial features, including the street network, approved protest areas and entry/exit gateways along the main rally route, were reconstructed in the Geographic Information System (GIS). This study adopted a crowdsourcing-geocomputation approach to simulate how a dynamic crowd would have navigated in such as rally event (Chow, 2019). Using a mobile application that tracks individual trajectory, volunteers were recruited to contribute valuable in-situ data of dynamic human movements and behaviours attending the rally event. These data were used to formulate and calibrate the parameters of a computational cartographic model where each rally attendees were represented as a moving agent (x, y, t) confined in a micro spatial environment where the protesters marched from Victoria Park to the Government Headquarter in Hong Kong. Hence, individual GPS (Global Positioning System) trajectory during the rally was collected and converted into GIS data format for further analysis. More details about the data collection and processing can be found on the project website (https://chowte.wixsite.com/dynamicpop). By leveraging a large number of observations volunteered by crowdsourcing, this study attempted to answer the research question: <i>What might be the simulated crowd size that reasonable range of model parameters can be converged?</i></p><p> Using this crowdsoucing-geocomputation model, a sensitivity analysis was conducted to simulate varying model parameters, including maximum walking speed, maximum crowd density, early departure and late entry rates. Based on the reported count reported by the police, organizers and independent researchers, various crowd sizes were simulated to be compared against the observed rally time of 209 minutes (i.e. about 3.5 hours) from start to finish. Using the crowdsourced data for calibration, most rally models simulated an arrival time of head crowd between 106–108 minutes, which was very close to the observed rally time of head crowd of 107 minutes. In this study, crowd sizes were considered to be acceptable based on a 95% confidence interval of arrival time of tail crowd (i.e. 174–192 minutes) and total rally time (i.e. 199–219 minutes). For example, a crowd size of 9,800 people was simulated matching the total rally time to examine the credibility of calibrated model parameters (Figure 1).</p><p> Within the tested range of calibrated model parameters, the results indicated that it was possible to tweak the model parameters of varying crowd size to match the observed rally time (Table 1). Despite the simulated rally time of some accepted models were within ±5% of the observed rally time, the parameters used to simulate such a model were not necessary reasonable in reality. The simulated count of 9,800, for example, would require a cap of maximum walking speed of 0.5 m/s, which seemed to be unreasonably slow under normal circumstance and incompatible with crowdsourced data (Figure 1). Given the observed rally time, it was found that the crowd sizes of 14,000–29,000 could be simulated with reasonable model parameters, whereas the crowd sizes of 9,800 and 50,000 would yield unreasonable model parameters. Taking the median within the range of 14,000–29,000, this study also found that a crowd size of 21,000 could yield eight matching simulations with varying reasonable model parameters that may be better simulate the actual rally attendance.</p><p>This paper provided empirical evidences to examined the credibility of various crowd sizes of the 2017 July 1st Rally in Hong Kong reported by the stakeholders. The research also presents a transparent, repeatable and verifiable approach to explore, quantify and simulate human movements in a rally event, such as the early departure and late arrival, to better understand dynamic crowd behaviours and interactions.</p>

Cartographers have always grappled with the question of how to depict spaces of conflict where place names or boundaries are disputed. We examine how these conflicts are represented in mass-produced online maps with a worldwide audience, focusing on both commercial maps produced by tech giants such as Google, and crowdsourced efforts such as OpenStreetMap. Producers of these maps occasionally publish policies on conflict resolution, emphasizing the mapping of ground truth and conformance to internationally recognized specifications. In practice, the makers of these maps violate their own appeals to neutrality and ground truth by (1) producing customized versions of the map tailored to local laws or expectations; and, (2) introducing ambiguity through the selective addition or omission of information in sensitive locations. We provide examples of these customization and ambiguity practices in commercial and crowdsourced maps. We demonstrate how commercial maps can appear in any number of versions to satisfy disputing parties, and crowdsourced maps can undergo “rogue customizations” for varying time periods as irredentist or separatist contributors seek for avenues to express their causes to a worldwide audience. We reflect on the long-term viability of customization and ambiguity as cartographic practices, commenting on ways that they shape — and are shaped by — the conflict on the ground and power of involved actors. Behind the seamless navigation of the Web 2.0 world map lies a patchwork of contributor motives and worldviews that complicates an understanding of ground truth and warrants interpretation through the lens of critical cartography.

Le déploiement de véhicules intelligents et connectés, dotés de capteurs de plus en plus sophistiqués, et capables de partager des positions et des trajectoires précises, permettra d’améliorer considérablement la sécurité routière et l’efficacité du trafic. Pour que ce gain de sécurité devienne effectif, les véhicules devront être géo-positionnés dans un référentiel commun avec précision, avec une erreur d’au plus quelques décimètres [1]. Pour y parvenir, ils pourront compter sur une variété de capteurs embarqués, tels que des récepteurs GNSS (Global Navigation Satellite Systems), ainsi que des capteurs proprioceptifs et des capteurs de perception. Toutefois, afin de garantir un positionnement précis dans toutes les conditions, y compris dans les zones denses où les signaux GNSS peuvent être dégradés par des effets de trajets multiples, les véhicules devront utiliser des cartes précises de l’environnement pour soutenir leurs algorithmes de localisation.Afin d’établir de telles cartes pour les principales autoroutes, les principaux acteurs automobiles ont eu recours à des flottes de véhicules spécialisés équipés de capteurs haut de gamme. Cependant, en raison des coûts opérationnels élevés qui y sont associés, ils n’ont exploité qu’un nombre limité de véhicules et ne sont pas en mesure de fournir des mises à jour en direct des cartes, ni de cartographier des réseaux routiers entiers. La cartographie crowdsourcée représente une solution rentable à ce problème et suscite aujourd’hui l’intérêt des acteurs du secteur automobile. Cette technique consiste à exploiter les mesures récupérées par de multiples véhicules de production équipés de capteurs standard, afin de construire une carte contenant des points de repère. Néanmoins, même si cette approche semble prometteuse, sa capacité réelle à construire une carte précise et à la maintenir à jour a besoin d’être évaluée dans des scénarios réalistes et long-terme.Dans cette thèse, nous proposons d’abord une solution de cartographie crowdsourcée basée sur une optimisation par triangulation, et l’évaluons à l’aide de tests de terrain. L’analyse des résultats montre le potentiel de cette approche à tirer profit des mesures émises par plusieurs véhicules. Elle permet aussi d’identifier certaines limitations critiques associées à l’optimisation par triangulation.Pour remédier à cela, nous proposons ensuite une autre solution de cartographie crowdsourcée basée sur l’optimisation de graphe, et nous introduisons différentes approches pour inclure et mettre à jour la carte dans l’optimisation, qui correspondent à différents compromis entre la qualité de la carte et la scalabilité. Des expériences de simulation sont menées afin de comparer ces approches. Les résultats permettent d’identifier la plus efficace, ainsi que de vérifier qu’elle représente une solution scalable de cartographie crowdsourcée.La robustesse de cette approche à divers types de bruits, tels que les bruits auto-corrélés et biaisés, est ensuite évaluée à l’aide de tests de simulation étendus. L’analyse des résultats montre sa capacité à construire une carte précise dans diverses conditions de bruits, en utilisant des mesures récupérées par plusieurs véhicules. Ensuite, des tests de terrain sont effectués afin de confirmer les résultats obtenus en simulation, et de tirer des conclusions tant d’un point de vue théorique que pratique. Enfin, la capacité de notre solution de cartographie crowdsourcée à améliorer les capacités de localisation des véhicules est évaluée en simulation. Les résultats montrent l’efficacité de l’approche proposée dans diverses conditions, tout en soulignant l’importance de fournir une carte avec une densité suffisante de points de repère<br>The deployment of intelligent and connected vehicles, equipped with increasingly sophisticated equipment, and capable of sharing accurate positions and trajectories, is expected to lead to a substantial improvement of road safety and traffic efficiency. For this safety gain to become effective, vehicles will have to be accurately geo-positioned in a common reference, with an error up to a few decimeters [1]. To achieve this, they will be able to count on a variety of embedded sensors, such as GNSS (Global Navigation Satellite Systems) receivers, as well as additional proprioceptive and perception sensors. Nevertheless, in order to guarantee accurate positioning in all conditions, including in dense zones where GNSS signals can get degraded by multi-path effects, it is expected that vehicles will need to use precise maps of the environment to support their localization algorithms.To build maps of the main highways, major automotive actors have made use of dedicated fleets of vehicles equipped with high-end sensors. Because of the associated high operational costs, they have been operating a limited number of vehicles, and remain unable to provide live updates of the maps and to register entire road networks. Crowdsourced mapping represents a cost-effective solution to this problem, and has been creating interest among automotive players. It consists in making use of measurements retrieved by multiple production vehicles equipped with standard sensors in order to build a map of landmarks. Nevertheless, while this approach appears promising, its real potential to build an accurate map of landmarks and maintain it up-to-date remains to be assessed in realistic, long-term scenarios.In this thesis, in a first time, we propose a crowdsourced mapping solution based on triangulation optimization, and evaluate it using field-tests. The result analysis shows the potential of crowdsourced mapping to take advantage from measurements issued by multiple vehicles. On the other hand, it also indicates some critical limitations associated with triangulation optimization.Therefore, in a second time, we propose another crowdsourced mapping solution based on graph optimization, and we introduce different approaches to include and update the map within the optimization, which correspond to different trade-offs between the map quality and computational scalability. Simulation experiments are conducted in order to compare the different approaches. The results enable to identify the most efficient one, and to assert that it provides a scalable solution for crowdsourced mapping.The robustness of this solution to various types of noises, such as auto-correlated and biased noises, is then evaluated using extended simulation tests. The results analysis show its ability to build an accurate map of landmarks in various noises conditions, making use of measurements retrieved by multiple vehicles. Subsequently, field-tests are performed to confirm the results obtained in simulation, and draw conclusions both from a theoretical and practical viewpoint. Finally, the capacity of our crowdsourced mapping solution to increase the localization capabilities of vehicles is evaluated in simulation. The results show the effectiveness of the proposed approach to improve positioning performances in various conditions, while also pointing out the importance of providing a map with a sufficient density of landmarks

Examining the case of Western Sahara, this chapter takes a closer look at the connection between identity, territory, and borders, as well as their construction in mapping processes. Analysing different web maps, I will show their rootedness in the dominant political world views and contrast them with the perspectives of the parties of conflict (Sahrawi Arab Democratic Republic and Morocco). The results show significant differences between proprietary and crowdsourced web maps as well as the cartographic representations authored by the factions. The delineation of borders is directly linked to territorial identity and nation-building. This demonstrates the important role of web maps and other digitally disseminated maps in the definition of identity and (self)-governance.