Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth

We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward

Spatial practices in digital work : calling for a spatial turn in information systems research

The growing use of digital media in the workplace is shifting work to digital platforms, this study explores the role of the physical office space in modern organisations where digital work is the norm. We capture the way in which digital media modulates the production of space by tracing the physical and digital interactions of a software development team in a global IT company. Taking a performative and ontogenetic view of space we conceptualise two types of spatial practices that form distinct modulations and assemblages of features of the physical and digital environment. The first spatial practice modulates space to support recurrent work activities, while the second spatial practice modulates space to support ephemeral and focused work activities. This study contributes to the IS literature with a conceptual basis to study the interconnected nature of physical space in digital work in modern workplace settings. It calls for greater attention to space as a performative and constitutive element of digital work in information systems research

The tasks of the crowd : a typology of tasks in geographic information crowdsourcing and a case study in humanitarian mapping

In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance

Being specific about geographic information crowdsourcing : a typology and analysis of the Missing Maps project in South Kivu

Recent development in disaster management and humanitarian aid is shaped by the rise of new information sources such as social media or volunteered geographic information. As these show great potential, making sense out of the new geographical datasets is a field of important scientific research. Therefore, this paper attempts to develop a typology of geographical information crowdsourcing. Furthermore, we use this typology to frame existing crowdsourcing projects and to further point out the potential of different kinds of crowdsourcing for disaster management and humanitarian aid. In order to exemplify its practical usage and value, we apply the typology to analyze the crowdsourcing methods utilized by the members of the Missing Maps project developed in South Kiv

Crafting workspaces by entangling physical and digital environments

Digital working is often seen to be replacing office-based work practices. This study captures the opposite, the entanglement of features of both physical and digital by software development teams in a multinational IT company. We observed how these software development teams crafted three types of entangled workspaces, characterised by different modulations of digital and physical features of their environment. We take an ontogenetic view of space that sees space as performative and constantly in the making to study the crafting of these entangled workspaces which transcend both physical and digital environments. This sociospatial view provides a novel conceptual basis to study the role of space in digital working

Crowdsourced validation and updating of dynamic features in OpenStreetMap an analysis of shelter mapping after the 2015 Nepal earthquake

The paper presents results from a validation process of OpenStreetMap (OSM) rapid mapping activities using crowdsourcing technology in the aftermath of the Gorkha earthquake 2015 in Nepal. We present a framework and tool to iteratively validate and update OSM objects. Two main objectives are addressed: first, analyzing the accuracy of the volunteered geographic information (VGI) generated by the OSM community; second, investigating the spatio-temporal dynamics of spontaneous shelter camps in Kathmandu. Results from three independent validation iterations show that only 10 % of the OSM objects are false positives (no shelter camps). Unexpectedly, previous mapping experience only had a minor influence on mapping accuracy. The results further show that it is critical to monitor the temporal dynamics. Out of 4,893 identified shelter camps, 54% were already empty/closed six days after the first mapping. So far, updating geographical features during humanitarian crisis is not properly addressed by the existing crowdsourcing approaches

Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes

Towards a participatory methodology for community data generation to analyse urban health inequalities : a multi-country case study

This paper presents results from the application of a methodological framework developed as part of an ongoing research project focused on understanding inequalities in the healthcare access of slum residents of cities in four countries: Bangladesh, Kenya, Pakistan and Nigeria. We employ a systematic approach to produce, curate and analyse volunteered geographic information (VGI) on urban communities, based on a combination of collaborative satellite-imagery digitization and participatory mapping, which relies upon geospatial open-source technologies and the collaborative mapping platform OpenStreetMap. Our approach builds upon and extends humanitarian mapping practices, in order to address the twofold challenge of achieving equitable community engagement whilst generating spatial data that adheres quality standards to produce rigorous and trusted evidence for policy and decision making. Findings show that our method generated promising results both in terms of community engagement and the production of high-quality data on communities to analyse urban inequalities

The ‘purpose ecosystem’ : emerging private sector actors in earth system governance

The private sector arguably plays a critical role in addressing the challenges of the Anthropocene and providing potential solutions to achieve the United Nations Sustainable Development Goals. Recently, a myriad of new actors in the form of intermediaries, initiatives and organisations have started driving wider systems change by advocating and advising companies to reconsider and broaden their fundamental ‘raison d’être’. In this Perspective we argue that the emergence of this ‘purpose ecosystem’ could play an important function within earth system governance, specifically by endorsing and accelerating action aligned with achieving the UN SDGs; yet we also highlight a number of risks, barriers and critical considerations for its overall assessment and propose important questions for further research

Exploring the use of IoT data for heightened situational awareness in centralised monitoring control rooms

This paper traces the expansion of a network of IoT sensors to improve the effectiveness of a centralised control room in Brazil in anticipating natural hazards. This centralised model relies on using IoT data by highly qualified experts replacing previous smaller local structures. We draw on the notion of Situational Awareness to carry out the study. Results show that although the operators were not always familiar with the characteristics of locations, the use of IoT data heightened their situational awareness in the centralised control room by improving perception and comprehension. However, they still relied on local knowledge and learned experiences to support projection and anticipation of risks. The study highlights that although data analytics systems are capable of expanding operators’ perception of local elements, they must be complemented by local richer forms of information, needed to anticipate risks and make critical decisions with major impact on local population