Off the shortest path: Betweenness on street network level to study pedestrian movement

Betweenness centrality is an important measure in network sciences that reflects the extent a node lies in between any pairs in a graph. The measure has been used by urban studies, to discuss the relationship between urban mobility and the spatial street network of a city, using Dijkstra shortest path betweenness centrality to describe human wayfinding procedures. As in reality, wayfinding is a more complex endeavor, results of studies using both random path or the most optimal shortest path approach might be misleading. In this paper we propose with the exploratory betweenness centrality (EBC) a more realistic set of measures that uses an exploratory path in calculating centrality rather than an optimal path in studying pedestrian movement. In particular we calculate EBC where the agent selects the longest street nearest to the destination (App-EBC) or any random street that is approaching the destination (Ran-EBC). In doing so, we compare how EBC and GBC correlate with aggregate pedestrian movement for two case studies in London. The result shows the EBC measures explains equal or greater variation of aggregate pedestrian movement than the GBC measure for both of the case studies, indicating the potential of using measures of EBC in modeling urban mobility

"...when you’re a Stranger": Evaluating Safety Perceptions of (un)familiar Urban Places

What makes us feel safe when walking around our cities? Previous research has shown that our perception of safety strongly depends on characteristics of the built environment; separately, research has also shown that safety perceptions depend on the people we encounter on the streets. However, it is not clear how the two relate to one another. In this paper, we propose a quantitative method to investigate this relationship. Using an online crowd–sourcing approach, we collected 5452 safety ratings from over 500 users about images showing various combinations of built environment and people inhabiting it. We applied analysis of covariance (ANCOVA) to the collected data and found that familiarity of the scene is the single most important predictor of our sense of safety. Controlling for familiarity, we identified then what features of the urban environment increase or decrease our safety perception

A Quantitative Approach to Evaluate and Develop Theories on (Fear of) Crime in Urban Environments

Well established work in criminological, architectural and urban studies suggests that there is a strong correlation between crime, perceived safety, the fear of crime, and the presence of different demographics, the people dynamics, in an urban environment. These studies have been conducted primarily using qualitative evaluation methods, and are typically limited in terms of the geographical area they cover, the number of respondents they reach out to, and the temporal frequency with which they can be repeated. As cities are rapidly growing and evolving complex entities, complementary approaches that afford social and urban scientists the ability to evaluate urban crime and fear of crime theories at scale are required. In this thesis, I propose a combination of methodologies following a data mining and crowdsourcing approach to quantitatively validate these theories at scale, and to support the exploration of new ones. To relate people dynamics to crime quantitatively, I first analyse footfall counts as recorded by telecommunication data, and extract metrics that act as proxies of urban crime theories. Using correlation and regression analysis between such proxies and crime activity derived from open crime data records, the method can help to understand to what extent different theories of urban crime hold, and where. To relate people dynamics to fear of crime quantitatively, I then built two image– based online crowdsourcing platforms to investigate to what extent online crowdsourcing can be used to gather safety perceptions about urban places, defined by the combination of built environment and the people inhabiting it. As existing theories suggest that knowing who the respondents are is crucial for understanding safety perceptions, I also gathered their demographic background information to discuss their perceptions accordingly. I applied analysis of variance (ANOVA) and covariance (ANCOVA) to these data. The method can help to understand what visual properties based on peopl

Modeling mediated urban space through geo-located social microblogging

This paper explores the hybrid nature of mediated urban space in the contemporary city, consisting of architectural spaces interlinked with the digital; situated and networked. We suggest an alternative way of looking into the city, using digitally augmented methods beyond the traditionally established ones in urban design and spatial analysis. Taking the physical space as a starting point, we apply an eco-systemic model to investigate mediated urban forces in the city of London. We identify the implications for the city through mapping, visualization and analysis of geo-located social micro blogging in the form of Twitter data. The discussion of this project is mainly concentrated on the results of time based patterns on geo-located Twitter flows and urban conditions, outlining initial observations about the city and its digital apparatus. By exploring how technology mediates sociality through Twitter channels and the creation of potentially new social representation forms, the project outlines the influence of those mechanisms on social, cultural and political life of cities

Learning from polls

Voters’ expectations of party strengths are a central part of many foundational political science theories that posit a strategic act by the voter. But how do voters develop these beliefs and how is this belief formation affected by polling reports? In this article, we present a dynamic Bayesian learning model that serves as a baseline for how beliefs are formed. We use survey experiments to estimate parameters of the dynamic learning process and analyze how and when belief formation deviates from theoretical model. We find that respondents update closely to new arriving poll results, they judge the polls to be two times more imprecise as the actual sample error and that this makes the induced differences in prior beliefs about a race vanish over time. We further apply the experiment to the study of partisan bias and the quality of the polls

Eliciting beliefs as distributions in online surveys

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter, we ask if there is an effective way of eliciting beliefs as distributions in the context of online surveys. Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It measures average citizens’ subjective belief distributions reliably and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs

Replication Data for: What is Islamophobia? Disentangling Citizens’ Feelings Towards Ethnicity, Religion and Religiosity Using a Survey Experiment

Replication data set (STATA format) and R code to reproduce analyses and figures in the paper. Abstract: What citizens think about Muslim immigrants is of great importance for some of the most pressing challenges facing Western democracies. To advance our understanding of what “Islamophobia” really is – i.e. whether it is a dislike based on immigrants` ethnic background, their religious identity or their specific religious behaviour – we fielded a representative online survey experiment in the UK in the summer 2015. Our results suggest that Muslims are not per se viewed more negatively than Christian immigrants. Instead, we provide evidence that citizens’ uneasiness with Muslim immigration is first and foremost the result of a rejection of fundamentalist forms of religiosity. This suggests that com-mon explanations, which are based on simple dichotomies between liberal supporters and conservative critics of immigration need to be re-evaluated. While the politically left and culturally liberal have more positive attitudes towards immigrants than right leaning and conservatives, they are also far more critical towards religious groups. We conclude that a large part of the current political controver-sy over Muslim immigration has to do with this double opposition. Importantly, the current political conflict over Muslim immigration is not so much about immigrants versus natives or even Muslim versus Christians as it is about political liberalism versus religious fundamentalism

Replication Data for: The Sensitivity of Sensitivity Analysis

This article evaluates the reliability of sensitivity tests (Leamer 1978). Using Monte Carlo methods we show that, first, the definition of robustness exerts a large influence on the robustness of var¬iables. Second and more importantly, our results also demonstrate that inferences based on sen¬sitivity tests are most likely to be valid if determinants and confounders are almost uncorrelated and if the variables included in the true model exert a strong influence on outcomes. Third, no definition of robustness reliably avoids both false positives and false negatives. We find that for a wide variety of data-generating processes, rarely used definitions of robustness perform better than the frequently used model averaging rule suggested by Sala-i-Martin. Fourth, our results also suggest that Leamer’s extreme bounds analysis and Bayesian model averaging are extremely un¬likely to generate false positives. Thus, if based on these inferential criteria a variable is robust, it is almost certain to belong into the empirical model. Fifth and finally, we also show that research¬ers should avoid drawing inferences based on lack of robustness

Eliciting beliefs as distributions in online surveys

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter we ask whether there is an effective way to elicit beliefs as distributions in the context of (online) surveys? Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It reliably measures the subjective belief distribution of average citizens and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs