Darshan Santani

CommuniSense: Crowdsourcing Road Hazards in Nairobi

Nairobi is one of the fastest growing metropolitan cities and a major business and technology powerhouse in Africa. However, Nairobi currently lacks monitoring technologies to obtain reliable data on traffic and road infrastructure conditions. In this paper, we investigate the use of mobile crowdsourcing as means to gather and document Nairobis road quality information. We first present the key findings of a city-wide road quality survey about the perception of existing road quality conditions in Nairobi. Based on the surveys findings, we then developed a mobile crowdsourcing application, called CommuniSense, to collect road quality data. The application serves as a tool for users to locate, describe, and photograph road hazards. We tested our application through a two-week field study amongst 30 participants to document various forms of road hazards from different areas in Nairobi. To verify the authenticity of user-contributed reports from our field study, we proposed to use online crowdsourcing using Amazons Mechanical Turk (MTurk) to verify whether submitted reports indeed depict road hazards. We found 92% of user-submitted reports to match the MTurkers judgements. While our prototype was designed and tested on a specific city, our methodology is applicable to other developing cities

Looking at Cities in Mexico with Crowds

Mobile and social technologies are providing new opportunities to document, characterize, and gather impressions of urban environments. In this paper, we present a study that examines urban perceptions of three cities in central Mexico (Guanajuato, Leon and Silao), which integrates a mobile crowdsourcing framework to collect geo-localized images of urban environments by a local youth community, and an online crowdsourcing platform (Amazon Mechanical Turk) to gather impressions of urban environments along twelve physical and psychological dimensions. Our study resulted in a collection of 7,000 geo-localized images containing outdoor scenes and views of each citys built environment, including touristic, historical, and residential neighbourhoods; and 156,000 individual judgments from MTurk. Statistical analyses show that outdoor environments can be reliably assessed with respect to most urban dimensions by the observers of crowdsourced images. Furthermore, a cross-city statistical analysis shows that outdoor urban places in Guanajuato (a touristic, cultural heritage site) are perceived as more quiet, picturesque and interesting compared to places in Leon and Silao, which are commercial and industrial hubs, respectively. In contrast Silao, is perceived to have lower accessibility than Leon. Finally, we investigate whether the perceptions of urban environments vary across different times of the day and found that places in the evening are perceived as less happy, pleasant and preserved, when compared to the same place in the morning. Through the use of collective action, participatory sensing and mobile crowdsourcing, our study engages citizens to understand socio-urban problems in their communities.

Speaking swiss: languages and venues in foursquare

Due to increasing globalization, urban societies are becoming more multicultural. The availability of large-scale digital mobility traces e.g. from tweets or checkins provides an opportunity to explore multiculturalism that until recently could only be addressed using survey-based methods. In this paper we examine a basic facet of multiculturalism through the lens of language use across multiple cities in Switzerland. Using data obtained from Foursquare over 330 days, we present a descriptive analysis of linguistic differences and similarities across five urban agglomerations in a multicultural, western European country.

Development of the Geographical Proportional-to-size Street-Intercept Sampling (GPSIS) method for recruiting urban nightlife-goers in an entire city

Abstract We developed the Geographical Proportional-to-size Street-Intercept Sampling (GPSIS) method in order to obtain a sample of nightlife-goers which accounted for the diversity of spaces, patrons and locations within two Swiss cities. Popular nightlife zones were identified and quantified using social media data and local experts’ knowledge. Young people were recruited in the streets on Friday and Saturday nights on three consecutive weekends using the ‘fixed-line method, pro-rated for the zone’s estimated popularity. Of the 3092 young adults approached, 896 agreed to pre-register. The importance of recruitment in multiple zones and over multiple weekend-days was evidenced by significant variations in participant demographics and registration rates between recruitment zones, times and weather conditions. To conclude, by combining a geographical approach with in situ recruitment, GPSIS has considerable potential as a tool for recruiting samples that represent the diversity of the nightlife population and spaces.

InnerView: Learning Place Ambiance from Social Media Images

In the recent past, there has been interest in characterizing the physical and social ambiance of urban spaces to understand how people perceive and form impressions of these environments based on physical and psychological constructs. Building on our earlier work on characterizing ambiance of indoor places, we present a methodology to automatically infer impressions of place ambiance, using generic deep learning features extracted from images publicly shared on Foursquare. We base our methodology on a corpus of 45,000 images from 300 popular places in six cities on Foursquare. Our results indicate the feasibility to automatically infer place ambiance with a maximum R2 of 0.53 using features extracted from a pre-trained convolutional neural network. We found that features extracted from deep learning with convolutional nets consistently outperformed individual and combinations of several low-level image features (including Color, GIST, HOG and LBP) to infer all the studied 13 ambiance dimensions. Our work constitutes a first study to automatically infer ambiance impressions of indoor places from deep features learned from images shared on social media.

Venues in Social Media: Examining Ambiance Perception Through Scene Semantics

We address the question of what visual cues, including scene objects and demographic attributes, contribute to the automatic inference of perceived ambiance in social media venues. We first use a state-of-art, deep scene semantic parsing method and a face attribute extractor to understand how different cues present in a scene relate to human perception of ambiance on Foursquare images of social venues. We then analyze correlational links between visual cues and thirteen ambiance variables, as well as the ability of the semantic attributes to automatically infer place ambiance. We study the effect of the type and amount of image data used for learning, and compare regression results to previous work, showing that the proposed approach results in marginal-to-moderate performance increase for up to ten of the ambiance dimensions, depending on the corpus.

Citizen Engagement and Awareness of the Road Surface Conditions in Nairobi, Kenya

In this poster, we present a mobile crowdsourcing application, called Afya Ya Barabara (AYB) (which means road health in Kiswahili). The application serves 2 major roles: First is to collect and visualize data on road surface conditions in Nairobi. Second role is as a citizen engagement platform to raise awareness on the state of road conditions in Nairobi using Twitter hashtags. The focus of this work is to discuss how AYB is employed to promote citizen engagement. Nairobi is one of the most active cities in East Africa in terms of Twitter usage. This guided our decision to employ Twitter as a social engagement tool. The demand for more systematic citizen engagement and public participation in the governance of developing cities is growing. As government budgets are becoming increasingly tight, new ways of addressing road maintenance problems are required. Public governance in developing countries faces accountability issues that allow corruption and poor service delivery to seethe. Lack of information and citizen engagement perpetuates such tendencies. We propose Afya Ya Barabara as an engagement platform that will expand the capacity of municipalities and its citizens to better monitor road conditions using mobile technology.

Insiders and Outsiders: Comparing Urban Impressions between Population Groups

There is a growing interest in social and urban computing to employ crowdsourcing as means to gather impressions of urban perception for indoor and outdoor environments. Previous studies have established that reliable estimates of urban perception can be obtained using online crowdsourcing systems, but implicitly assumed that the judgments provided by the crowd are not dependent on the background knowledge of the observer. In this paper, we investigate how the impressions of outdoor urban spaces judged by online crowd annotators, compare with the impressions elicited by the local inhabitants, along six physical and psychological labels. We focus our study in a developing city where understanding and characterization of these socio-urban perceptions is of societal importance. We found statistically significant differences between the two population groups. Locals perceived places to be more dangerous and dirty, when compared with online crowd workers; while online annotators judged places to be more interesting in comparison to locals. Our results highlight the importance of the degree of familiarity with urban spaces and background knowledge while rating urban perceptions, which is lacking in some of the existing work in urban computing.