Meryam Benabdeljelil (p) / Epainos Award Candidate

Mathieu Steijn

Based on the Internet of Things (IoT), Smart Cities seem to become inevitable, due to technology evolution, progressive and permanent integration of connected objects within urban networks and their ubiquity in various fields, assuring interconnections between virtual and physical objects, places and environments, allowing instant communications. Cities are indeed continuously integrating new technologies into urban space, considered both as instruments and catalysts of their development. Symbol of a digitalized, sustainable and automated future, this city model, promises potential improvements in services by monitoring numerous urban flows. The city's "Smart" name brings together various topics relating to urban space, particularly in terms of representation, infrastructure, social and spatial practices, mobility, data …

A critical analysis of Smart Cities from a mobility angle seems to be herein an essential approach to further examine thru specific case studies its numerous influences on urban spaces. Such contribution aims to suggest a conceptual approach of this booming city model and an empirical study hoping to analyze the distinction of what is really smart in a city oriented towards information and data, particularly in a pandemic context.
Disparities in integration of digital and infrastructural networks within cities are discussed, while questioning the possible evolution of its urban dynamics and technological diversification at various scales. This research aims to see how “smartness” manifests itself thru a study of selected French cities and/or regions, particularly in mobility practices and their influences on behaviors often in an unevenly digitalized environment. The study reported herein aims to develop a city approach considering new urban dynamics as mobility restrictions and new ways of normality set in a COVID19 pandemic context.

The profusion of real time consumer created data allow personalizing services, via algorithms that analyze needs, wishes and habits. Its main goal is to reinforce the event-based character of a city that seems built on integrating the latest technology and infrastructure innovations, including urban mobility. Transition from infrastructure oriented to info-structure based mobility practices, disparities and unequal access to mobility services resulting from “digital divide” in urban development actions aligned with a strategic thinking on city level will be discussed.
Behaviors and work have also undergone radical changes that are still experienced today. This approach aims to highlight new urban dynamics challenges in a Covid-19 pandemic context marked by mobility restriction and their impacts on work and behavior equally highlights new possibilities of ways of life in French cities.

Emerald Dilworth (p), Emmanouil Tranos, Daniel Lawson

Meryam Benabdeljelil

Spatial relationships are at the core of spatial sciences, however geographers often lack means of observing spatial relationships. Understanding how two places are connected is useful, but can only be inferred and not observed. With access to more and more new sources of Big Data, there are opportunities to extract spatial relationships from the data, and develop methods to effectively do so. Being able to capture trade and connections between regions, and how these change with time, are spatial relationships that currently there is a limited understanding of. By using these new data, with methods that have developed alongside our ability to process larger quantities of data, we can draw out new information about spatial relationships we know exist and can benefit from understanding better.

Using data from the Internet Archive (IA)’s web collection related to the UK, which has been collected over the period 1996-2013, hyperlinks between websites over time and the geolocations of the websites are recorded. The IA obtained geolocations for websites, by analysing the html text for the websites and identifying postcodes in the text, which provides spatial aspect to this data. The data can be grouped by year, to add in a temporal dimension.

By aggregrating the data from individual website granularity to a level that avoids high sparsity (e.g. to MSOAs), methods for analysing embedded dynamic networks can be applied. The data can be put into graphs, to develop an understanding of the spatial locations and volumes of which there are connections between websites in the UK.

By building a spectrally embedded temporal model, new things about the spatial relationships can be learnt. A joint spectral embedding of multiple graphs technique called unfolded adjacency spectral embedding (UASE) is a process that can be applied to the data described, to see how the graph evolves with time. The findings can then be interpreted to understand the changes in behaviour of websites, communities, or the entire graph, that happen with time. We can visualise where clusters of communities are linked with one another are located spatially, and how these connections evolve with time. By adding the temporal aspect to the spatial relationships found, insights to why/how these relationships change can be made.

Mathieu Steijn (p)

Emerald Dilworth

Crises tend to accelerate economic change of which labour polarisation is one of the most relevant trends in urban labour markets. The Covid-19 crisis is very different from previous economic shocks as it not only reduced spending by consumers and investments by firms like previous crises in general do but also through the particularity of the corona measures, which disproportionally affect activities dependent on being close to other persons.

In this paper, the traditional measures of, respectively, manual, routine, or abstract task content of jobs are supplemented with measures on the feasibility of remote work, following (Dingel & Neiman, 2020), and if a job is present on the list of jobs that are allowed to operate during lockdowns by the Dutch government. First, it is shown that essential jobs are roughly equally distributed over the wage distribution of jobs. Remote work, on the other hand, is strongly concentrated among higher incomes. An analysis of employment dynamics during the Covid-19 crisis in the Dutch labour market shows that a strong growth of high income jobs has taken place whereas jobs below the 55th percentile have generally seen a large amount of job loss, although this is smaller for the lowest income groups. This suggests that this crisis has led to a rise in income inequality rather than labour polarisation. The regression results confirm that a higher abstract task content, remote work ability, and essential requirement leads to growth. Whereas manual tasks rather routine tasks are associated with a employment loss. The small employment decrease in routine jobs is due to the fact that some of these jobs are designated as essential for production purposes. I also show that this starkly differs from the financial crisis when routine tasks are a strong predictor of employment loss and remote work and essential jobs are irrelevant. Further analysis shows that the rapid rise in income inequality occurs mostly in large cities and that these new high-skilled jobs are generally taken up by young highly educated workers and expats, whereas older less-educated workers are more likely to exit the work force or move to lower-income jobs.