Afra J. Mashhadi

Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks

Governments and other organisations often rely on data collected by household surveys and censuses to identify areas in most need of regeneration and development projects. However, due to the high cost associated with the data collection process, many developing countries conduct such surveys very infrequently and include only a rather small sample of the population, thus failing to accurately capture the current socio-economic status of the countrys population. In this paper, we address this problem by means of a methodology that relies on an alternative source of data from which to derive up to date poverty indicators, at a very fine level of spatio-temporal granularity. Taking two developing countries as examples, we show how to analyse the aggregated call detail records of mobile phone subscribers and extract features that are strongly correlated with poverty indexes currently derived from census data.

Geographic human-computer interaction

Researchers and practitioners in human-computer interaction are increasingly taking geographic approaches to their work. Whether designing novel location-based systems, developing natural user interfaces for maps, or exploring online interactions over space and time, HCI is discovering that geographic questions, methods, and use cases are becoming integral to our field. Unfortunately, to our knowledge, there have been no direct efforts to unite members of the community exploring geographic HCI. The goal of this forum is to bring together researchers from a variety of areas to provide a summary of what has been done thus far and to discuss options for developing a more formal geographic HCI community. We will also highlight the troublesome lack of communication between scholars in geography and HCI and the opportunities that will result from increased collaboration between the two fields.

Tiny habits in the giant enterprise: understanding the dynamics of a quantified workplace

We offer a reflection on the technology usage for workplace quantification through an in the wild study. Using a prototype Quantified Workplace system equipped with passive and participatory sensing modalities, we collected and visualized different workplace metrics (noise, color, air quality, self reported mood, and self reported activity) in two European offices of a research organization for a period of 4 months. Next we surveyed 70 employees to understand their engagement experience with the system. We then conducted semi-structured interviews with 20 employees in which they explained which workplace metrics are useful and why, how they engage with the system and what privacy concerns they have. Our findings suggest that sense of inclusion acts as the initial incentive for engagement which gradually translates into a habitual routine. We found that incorporation of an anonymous participatory sensing aspect into the system could lead to sustained user engagement. Compared to past studies we observed a shift in the privacy concerns, due to the trust and transparency of our prototype system. We conclude by providing a set of design principles for building future Quantified Workplace systems.

The myth of subtle notifications

Push notifications keep user informed and engaged with the events around the mobile applications. However not all the notifications are of the same importance level to the user. We explore how mobile notifications are regarded as increasing number of applications are adopting notification services. We logged notification management traces from 10 individuals for 15 days to understand how they perceived mobile notifications and their importance, accompanying our results with semi-structured interviews.

On the accuracy of urban crowd-sourcing for maintaining large-scale geospatial databases

The world is in the midst of an immense population shift from rural areas to cities. Urban elements, such as businesses, Points-of-Interest (POIs), transportation, and housing are continuously changing, and collecting and maintaining accurate information about these elements within spatial databases has become an incredibly onerous task. A solution made possible by the uptake of social media is crowd-sourcing, where user-generated content can be cultivated into meaningful and informative collections, as exemplified by sites like Wikipedia. This form of user-contributed content is no longer confined to the Web: equipped with powerful mobile devices, citizens have become cartographers too, volunteering geographic information (e.g., POIs) as exemplified by sites like OpenStreetMap. In this paper, we investigate the extent to which crowd-sourcing can be relied upon to build and maintain an accurate map of the changing world, by means of a thorough analysis and comparison between traditional web-based crowd-sourcing (as in Wikipedia) and urban crowd-sourcing (as in OpenStreetMap).

The Impact of Society on Volunteered Geographic Information: The Case of OpenStreetMap

Volunteered Geographical Information (VGI) has been extensively studied in terms of its quality and completeness in the past. However, little attention is given to understanding what factors, beyond individuals’ expertise, contribute to the success of VGI. In this chapter we ask whether society and its characteristics such as socio-economic factors have an impact on what part of the physical world is being digitally mapped. This question is necessary, so to understand where crowd-sourced map information can be relied upon (and crucially where not), with direct implications on the design of applications that rely on having complete and unbiased map knowledge. To answer the above questions, we study over 6 years of crowd-sourced contributions to OpenStreetMap (OSM) a successful example of the VGI paradigm. We measure the positional and thematic accuracy as well as completeness of this information and quantify the role of society on the state of this digital production. Finally we quantify the effect of social engagement as a method of intervention for improving users’ participation.

Detecting human encounters from WiFi radio signals

We present the design, implementation and evaluation of a novel human encounter detection framework for measuring and analysing human behaviour in social settings. We propose the use of WiFi probes, management frames of WiFi, that periodically radiate from mobile devices (as proxies for humans), and existing WiFi access points to automatically capture radio signals and detect human copresence. Based on the spatio-temporal properties of this copresence and their interplay we defined a model, borrowing theories from sociology, to detect human encounters -- short-lived, spontaneous human interactions. We evaluated our framework using controlled and in-the-wild experiments yielding a detection performance of 96% and 86% respectively. As such, our framework opens up interesting opportunities for designing proxemic and group applications, as well as conducting large-scale studies in the areas of computational social sciences.

Human Data Interaction in IoT: The ownership aspect

As Internet of Things (IoT) becomes a growing reality, more ubiquitous devices are embedded in our daily lives, serving us in a broad range of purposes in everyday life from: personal healthcare to home automation to tailored smart city services. These devices primarily collect data that is about or produced by people, be it street noise level of a neighbourhood, or the energy footprint of an individuals home or her location and other situational context. As this unprecedented amount of data is collected, we are challenged with one fundamental research question: who owns this data and who should have access to it? Specifically, the emergent of the Human Data Interaction (HDI) topic which aims to put the human at the centre of the data driven industry, calls attention to the IoT community to address the data ownership aspect more carefully. In this note, we offer a reflection on the challenges that IoT faces in regards to the data ownership in HDI and advocate the roles that both ordinary people and industries must play to best answer those challenges in shaping the IoT landscape.

Modelling growth of urban crowd-sourced information

Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growths characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.

Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap

In the recent years Wikis have become an attractive platform for social studies of the human behaviour. Containing millions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a better understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis ---wiki-based collaborative mapping projects---differ from systems such as Wikipedia in a fundamental way due to spatial dimension of the content that limits the contributors to a set of those who posses local knowledge about a specific area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open collaboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise different type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously observed editing activity patterns of Wikipedia.