A Social Science Approach Using Big Data for City Planning

Cities bring about economic dynamism through positive economic externalities; however, the concentration of people in dense locations has its costs — epidemics, social unrest, pollution, and congestion are some of the ills of cities. As cities evolve, they experience stress, and fault lines appear; the ability to pulse a city and provide early warning of these fault lines can prove advantageous for policymakers in managing and planning for cities. This paper outlines a research program that developed a city scanning tool to measure cities and detect aberrations as they surface. We aggregated data from various industry partners, governmental agencies, and public online sources to develop the measurement metric and applied social science theories to analyze and interpret the results. The results of this study contribute to information system (IS) research by showcasing the role IS research in city planning and for societal good

Cell Towers as Urban Sensors: Understanding the Strengths and Limitations of Mobile Phone Location Data

Understanding urban dynamics and human mobility patterns not only benefits a wide range of real-world applications (e.g., business site selection, public transit planning), but also helps address many urgent issues caused by the rapid urbanization processes (e.g., population explosion, congestion, pollution). In the past few years, given the pervasive usage of mobile devices, call detail records collected by mobile network operators has been widely used in urban dynamics and human mobility studies. However, the derived knowledge might be strongly biased due to the uneven distribution of people’s phone communication activities in space and time. This dissertation research applies different analytical methods to better understand human activity and urban environment, as well as their interactions, mainly based on a new type of data source: actively tracked mobile phone location data. In particular, this dissertation research achieves three main research objectives. First, this research develops visualization and analysis approaches to uncover hidden urban dynamics patterns from actively tracked mobile phone location data. Second, this research designs quantitative methods to evaluate the representativeness issue of call detail record data. Third, this research develops an appropriate approach to evaluate the performance of different types of tracking data in urban dynamics research. The major contributions of this dissertation research include: 1) uncovering the dynamics of stay/move activities and distance decay effects, and the changing human mobility patterns based on several mobility indicators derived from actively tracked mobile phone location data; 2) taking the first step to evaluate the representativeness and effectiveness of call detail record and revealing its bias in human mobility research; and 3) extracting and comparing urban-level population movement patterns derived from three different types of tracking data as well as their pros and cons in urban population movement analysis

A Social Citizen Dashboard for Participatory Urban Planning in Berlin: Prototype and Evaluation

Participatory urban planning enables citizens to make their voices heard in the urban planning process. The resulting measures are more likely to be accepted by the community. However, the parti-cipation process becomes more effortful and time-consuming. New approaches have been developed using digital technologies to facilitate citizen participation, such as topic modeling based on social media. Using Twitter data for the city of Berlin, we explore how social media and topic modeling can be used to classify and analyze citizen opinions. We develop a Social Citizen Dashboard allowing for a better understanding of changes in citizens’ priorities and incorporating constant cycles of feedback throughout planning phases. Evaluation interviews indicate the dashboard’s potential usefulness and implications as well as point to limitation in data quality and spur further research potentials

Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain)

[EN] This paper presents the big data architecture and work flow used to download georeferenced tweets, store them in a NoSQL database, analyse them using the Apache Spark framework, and visualize the results. The study covers a complete year (from December 10, 2016 to December 10, 2017) in the city of Valencia (Eastern Spain), which is considered to be the third most important in Spain, having a population of nearly 800,000 inhabitants and a size of 135 km(2). The concepts of a specific event map and a specific event map with positive or negative sentiment are developed to highlight the location of an event. This approach is undertaken by subtracting the heat map of a specific day from the mean daily heat map, which is obtained by taking into account the 365 days of the studied period. This paper demonstrates how the proposed analysis from tweets can be used to depict city events and discover their spatiotemporal characteristics. Finally, the combination of all daily specific events maps in a single map, leads to the conclusion that the city of Valencia city has appropriate urban infrastructures to support these events.The authors would like to thank the comments and suggestions of the anonymous reviewers and the editor, which have helped to improve the original version.Martín Furones, ÁE.; Anquela Julián, AB.; Cos-Gayón López, FJ. (2019). Analysis of Twitter messages using big data tools to evaluate and locate the activity in the city of Valencia (Spain). Cities. (86):37-50. https://doi.org/https://doi.org/10.1016/j.cities.2018.12.014S37508

Using Ambient Geographic Information (AGI) in Order to Understand Emotion & Stress within Smart Cities

Oliveira, T. H., & Painho, M. (2015). Using Ambient Geographic Information (AGI) in Order to Understand Emotion & Stress within Smart Cities. In F. Bação, M. Y. Santos, & M. Painho (Eds.), AGILE 2015 : 18th AGILE International Conference on Geographic Information Science: Geographic Information Science as an Enabler of Smarter Cities and Communities AGILE.Since one of the main ambitions of a smart city is to improve urban functions and provided services, it is often perceived as a living urban fabric, in which connected urban citizens, acting as active sensors, have the capacity to contribute even more efficiently to the spatial intelligence of cities. This “immaterial” dimension is related with the need that smart cities have to assess their citizen’s feelings, perception and well-being, giving rise to an emotion-aware city. Mapping emotion builds on a tradition of studies in cognitive mapping, evaluative mapping, environmental preference and environmental affect, adding an approach in which people experience, evaluate and describe their environment “in situ” through social media. This paper aims to present an Ambient Geographic Information (AGI) approach to assemble geo-tagged data from Twitter, Flickr, Instagram and Facebook related with people’s perception and feelings regarding Lisbon (Portugal), and therefore characterize its emotional dimension, by comparing these subjective observations with objective measurements (such as socio-demographic statistics, questionnaires and data retrieved from biometric sensors). With this vision of a smart city, that is capable to interpret and harnessing the emotional states of its citizens, it is essential to find new methods and techniques to sensing affect in an urban context.publishersversionpublishe

Open budget data: mapping the landscape

This report offers analysis of the emerging issue of open budget data, which has begun to gain traction amongst advocates and practitioners of financial transparency. Issues and initiatives associated with the emerging issue of open budget data are charted in different forms of digital media. The objective is to enable practitioners – in particular civil society organisations, intergovernmental organisations, governments, multilaterals and funders – to navigate this developing field and to identify trends, gaps and opportunities for supporting it. How public money is collected and distributed is one of the most pressing political questions of our time, influencing the health, well-being and prospects of billions of people. Decisions about fiscal policy affect everyone - determining everything from the resourcing of essential public services, to the capacity of public institutions to take action on global challenges such as poverty, inequality or climate change. Digital technologies have the potential to transform the way that information about public money is organised, circulated and utilised in society, which in turn could shape the character of public debate, democratic engagement, governmental accountability and public participation in decision-making about public funds. Data could play a vital role in tackling the democratic deficit in fiscal policy and in supporting better outcomes for citizens