World’s biggest city database shines light on our increasingly urbanised planet

Curated on Posted on by Stefaan Verhulst

EU Joint Research Centers: “The JRC has launched a new tool with data on all 10,000 urban centres scattered across the globe. It is the largest and most comprehensive database on cities ever published.

With data derived from the JRC’s Global Human Settlement Layer (GHSL), researchers have discovered that the world has become even more urbanised than previously thought.

Populations in urban areas doubled in Africa and grew by 1.1 billion in Asia between 1990 and 2015.

Globally, more than 400 cities have a population between 1 and 5 million. More than 40 cities have 5 to 10 million people, and there are 32 ‘megacities’ with above 10 million inhabitants.

There are some promising signs for the environment: Cities became 25% greener between 2000 and 2015. And although air pollution in urban centres was increasing from 1990, between 2000 and 2015 the trend was reversed.

With every high density area of at least 50,000 inhabitants covered, the city centres database shows growth in population and built-up areas over the past 40 years.  Environmental factors tracked include:

  • ‘Greenness’: the estimated amount of healthy vegetation in the city centre
  • Soil sealing: the covering of the soil surface with materials like concrete and stone, as a result of new buildings, roads and other public and private spaces
  • Air pollution: the level of polluting particles such as PM2.5 in the air
  • Vicinity to protected areas: the percentage of natural protected space within 30 km distance from the city centre’s border
  • Disaster risk-related exposure of population and buildings in low lying areas and on steep slopes.

The data is free to access and open to everyone. It applies big data analytics and a global, people-based definition of cities, providing support to monitor global urbanisation and the 2030 Sustainable Development Agenda.

The information gained from the GHSL is used to map out population density and settlement maps. Satellite, census and local geographic information are used to create the maps….(More)”.

Urban Big Data: City Management and Real Estate Markets

Curated on Posted on by Stefaan Verhulst

Report by Richard Barkham, Sheharyar Bokhari and Albert Saiz: “In this report, we discuss recent trends in the application of urban big data and their impact on real estate markets. We expect such technologies to improve quality of life and the productivity of cities over the long run.

We forecast that smart city technologies will reinforce the primacy of the most successful global metropolises at least for a decade or more. A few select metropolises in emerging countries may also leverage these technologies to leapfrog on the provision of local public services.

In the long run, all cities throughout the urban system will end up adopting successful and cost-effective smart city initiatives. Nevertheless, smaller-scale interventions are likely to crop up everywhere, even in the short run. Such targeted programs are more likely to improve conditions in blighted or relatively deprived neighborhoods, which could generate gentrification and higher valuations there. It is unclear whether urban information systems will have a centralizing or suburbanizing impact. They are likely to make denser urban centers more attractive, but they are also bound to make suburban or exurban locations more accessible…(More)”.

Reimagining Democracy: What if votes were a currency? A crypto-currency?

Curated on Posted on by Stefaan Verhulst

Opinion piece by Praphul Chandra: “… The first key tenet of this article is that the institution of representative democracy is a severely limited realization of democratic principles. These limitations span three dimensions:

First, citizen representation is extremely limited. The number of individuals whose preference an elected representative is supposed to represent is so large as to be essentially meaningless.

The problem is exacerbated in a rapidly urbanizing world with increasing population densities but without a corresponding increase in the number of representatives. Furthermore, since urban settings often have individuals from very different cultural backgrounds, their preferences are diverse too.

Is it realistic to expect that a single individual would be able to represent the preferences of such large & diverse communities?

Second, elected representatives have limited accountability. The only opportunity that citizens have to hold elected representatives accountable is often years away — ample time for incidents to be forgotten and perceptions to be manipulated. Since human memory over-emphasizes the recent past, elected representatives manipulate perception of their performance by populist measures closer to forthcoming elections.

Third, citizen cognition is not leveraged. The current model where default participation is limited to choosing representatives every few years does not engage the intelligence of citizens in solving the societal challenges we face today. Instead, it treats citizens as consumers offering them a menu card to choose their favourite representative.

To summarize, representative democracy does not scale well. With our societies becoming denser, more interconnected and more complex, the traditional tools of democracy are no longer effective.

Design Choices of Representative Democracy: Consider the following thought experiment: what would happen if we think of votes as a currency? Let’s call such a voting currency — GovCoin. In today’s representative democracy,

(i) GovCoins are in short supply — one citizen gets one GovCoin (vote) every 4–5 years.

(ii) GovCoins (Votes) have a very high negative rate: if you do not use them on election day, they lose all value.

(iii) GovCoins (Votes) are “accepted” by very few people: you can give your GovCoins to only pre-selected “candidates”

These design choices reflect fundamental design choices of representative democracy — they were well suited for the time when they were designed:

Since governance needs continuity and since elections were a costly and time-consuming exercise, citizens elected representatives once every 4–5 years. This also meant that elections had to be coordinated — so participation was coordinated to a particular election day requiring citizens to vote simultaneously.

Since the number of people who were interested in politics as a full-time profession was limited, the choice set of representatives was limited to a few candidates.

Are these design choices valid today? Do we really need citizens physically travelling to polling booths? With today’s technology? Must the choice of citizen participation in governance be binary: either jump in full time or be limited to vote once every 4–5 years? Aren’t there other forms of participation in this spectrum? Is limiting participation the only way to ensure governance continuity?

Rethinking Democracy: What if we reconsider the design choices of democracy? Let’s say we:

(i) increase the supply of GovCoins so that every citizen gets one unit every month;

(ii) relax the negative rate so that even if you do not “use” your GovCoin, you do not lose it i.e. you can accumulate GovCoins and use them at a later time;

(iii) enable you to give your GovCoins to anyone or any public issue / project.

What would be the impact of these design choices?

By increasing the supply of GovCoins, we inject liquidity into the system so that information (about citizens’ preferences & beliefs) can flow more fluidly. This effectively increases the participation potential of citizens in governance. Rather than limiting participation to once every 4–5 years, citizens can participate as much and as often as they want. This is a fundamental change when we consider institutions as information processing systems.

By enabling citizens to transfer GovCoins to anyone, we realize a form of liquid democracy where I can delegate my influence to you — maybe because I trust your judgement and believe that your choice will be beneficial to me as well. In effect, we have changed the default option of participation from ‘opt out’ to ‘opt in’ — every citizen can receive GovCoins from every other citizen. The total GovCoins a citizen holds is a measure of how much influence she holds in democratic decisions. We evolve from a binary system (elected representative or citizen) to a continuous spectrum where your GovCoin ‘wealth’ is measure of your social capital.

By enabling citizens to transfer GovCoins directly to a policy decision, we realize a form of direct democracy where citizens can express their preferences (and the strength of their preferences) on an issue directly rather than relying on a representative to do so.

By allowing citizens to accumulate GovCoins, we allow them to participate when they want. If I feel strongly about an issue, I can spend my GovCoins and influence this decision; If I am indifferent about an issue, I hold on to my GovCoins so that I can have a larger influence in future decisions. A small negative interest rate on GovCoins may still be needed to ensure that (i) citizens do not hoard the currency and (ii) to ensure that net influence of any individual is finite and time bounded.

Realizing Democracy: Given today’s technology landscape, realizing a democracy with new design choices is no longer a pipe dream. The potential to do this is here and now. A key enabling technology is blockchains (or Distributed Ledger Technologies) which allow the creation of new currencies. Implementing votes as a currency opens the door to realizing new forms of democracy….(More)”.

Artificial intelligence and smart cities

Curated on Posted on by Stefaan Verhulst

Essay by Michael Batty at Urban Analytics and City Sciences: “…The notion of the smart city of course conjures up these images of such an automated future. Much of our thinking about this future, certainly in the more popular press, is about everything ranging from the latest App on our smart phones to driverless cars while somewhat deeper concerns are about efficiency gains due to the automation of services ranging from transit to the delivery of energy. There is no doubt that routine and repetitive processes – algorithms if you like – are improving at an exponential rate in terms of the data they can process and the speed of execution, faithfully following Moore’s Law.

Pattern recognition techniques that lie at the basis of machine learning are highly routinized iterative schemes where the pattern in question – be it a signature, a face, the environment around a driverless car and so on – is computed as an elaborate averaging procedure which takes a series of elements of the pattern and weights them in such a way that the pattern can be reproduced perfectly by the combinations of elements of the original pattern and the weights. This is in essence the way neural networks work. When one says that they ‘learn’ and that the current focus is on ‘deep learning’, all that is meant is that with complex patterns and environments, many layers of neurons (elements of the pattern) are defined and the iterative procedures are run until there is a convergence with the pattern that is to be explained. Such processes are iterative, additive and not much more than sophisticated averaging but using machines that can operate virtually at the speed of light and thus process vast volumes of big data. When these kinds of algorithm can be run in real time and many already can be, then there is the prospect of many kinds of routine behaviour being displaced. It is in this sense that AI might herald in an era of truly disruptive processes. This according to Brynjolfsson and McAfee is beginning to happen as we reach the second half of the chess board.

The real issue in terms of AI involves problems that are peculiarly human. Much of our work is highly routinized and many of our daily actions and decisions are based on relatively straightforward patterns of stimulus and response. The big questions involve the extent to which those of our behaviours which are not straightforward can be automated. In fact, although machines are able to beat human players in many board games and there is now the prospect of machines beating the very machines that were originally designed to play against humans, the real power of AI may well come from collaboratives of man and machine, working together, rather than ever more powerful machines working by themselves. In the last 10 years, some of my editorials have tracked what is happening in the real-time city – the smart city as it is popularly called – which has become key to many new initiatives in cities. In fact, cities – particularly big cities, world cities – have become the flavour of the month but the focus has not been on their long-term evolution but on how we use them on a minute by minute to week by week basis.

Many of the patterns that define the smart city on these short-term cycles can be predicted using AI largely because they are highly routinized but even for highly routine patterns, there are limits on the extent to which we can explain them and reproduce them. Much advancement in AI within the smart city will come from automation of the routine, such as the use of energy, the delivery of location-based services, transit using information being fed to operators and travellers in real time and so on. I think we will see some quite impressive advances in these areas in the next decade and beyond. But the key issue in urban planning is not just this short term but the long term and it is here that the prospects for AI are more problematic….(More)”.

Who Owns Urban Mobility Data?

Curated on Posted on by Stefaan Verhulst

David Zipper at City Lab: “How, exactly, should policymakers respond to the rapid rise of new private mobility services such as ride-hailing, dockless shared bicycles, and microtransit?   … The most likely solution is via a data exchange that anonymizes rider data and gives public experts (and perhaps academic and private ones too) the ability to answer policy questions.

This idea is starting to catch on. The World Bank’s OpenTraffic project, founded in 2016, initially developed ways to aggregate traffic information derived from commercial fleets. A handful of private companies like Grab and Easy Taxi pledged their support when OpenTraffic launched. This fall, the project become part of SharedStreets, a collaboration between the National Association of City Transportation Officials (NACTO), the World Resources Institute, and the OECD’s International Transport Forum to pilot new ways of collecting and sharing a variety of public and private transport data. …(More).

People-Led Innovation: Toward a Methodology for Solving Urban Problems in the 21st Century

Curated on Posted on by Stefaan Verhulst

New Methodology by Andrew Young, Jeffrey Brown, Hannah Pierce, and Stefaan G. Verhulst: “More and more people live in urban settings. At the same time, and often resulting from the growing urban population, cities worldwide are increasingly confronted with complex environmental, social, and economic shocks and stresses. When seeking to develop adequate and sustainable responses to these challenges, cities are realizing that traditional methods and existing resources often fall short.

people-led-innovation-coverAddressing 21st century challenges will require innovative approaches.

“People-Led Innovation: Toward a Methodology for Solving Urban Problems in the 21st Century,” is a new methodology by The GovLab and Bertelsmann Foundation aimed at empowering public entrepreneurs, particularly city-level government officials, to engage the capacity and expertise of people in solving major public challenges. This guide focuses on unlocking an undervalued asset for innovation and the co-creation of solutions: people and their expertise…..

Designed for city officials, and others seeking ways to improve people’s lives, the methodology provides:

  • A phased approach to helping leaders develop approaches in an iterative manner that is more effective and legitimate by placing people, and groups of people, at the center of all stages of problem-solving process, including: problem definition, ideation, experimentation, and iteration.
  • A flexible framework that instead of rigid prescriptions, provides suggested checklists to probe a more people-led approach when developing innovative solutions to urban challenges.
  • A matrix to determine what kind of engagement (e.g., commenting, co-creating, reviewing, and/or reporting), and by whom (e.g., community-based organizations, residents, foundation partners, among others) is most appropriate at what stage of the innovation lifecycle.
  • A curation of inspirational examples, set at each phase of the methodology, where public entrepreneurs and others have sought to create positive impacts by engaging people in practice….(More)”.

Developing online illustrative and participatory tools for urban planning: towards open innovation and co-production through citizen engagement

Curated on Posted on by Stefaan Verhulst

Virpi Oksman and Minna Kulju in the International Journal of Services Technology and Management: “This article examines the challenge of involving various stakeholders in urban planning through user-driven innovation and collaborative design and leveraging these processes to achieve mutually beneficial outcomes. Consequently, we introduce a novel illustrative and participatory tool combining mixed reality visualisations with user-centred interactions and feedback-tools so as to promote user insights and involve them in design.

This article analyses how these co-design services should be designed and offered to users in order to effectively support public participation and citizen-governance collaboration in future urban planning projects. We conclude that, in order to provide real benefit and value for urban planning and smart city solutions, participatory service should be integrated as part of the decision-making. Adoption of this kind of services system also means reforming of some of work processes in governance and planning how to exploit the results of the participatory processes to make informed decisions….(More)”

Data-Intensive Approaches To Creating Innovation For Sustainable Smart Cities

Curated on Posted on by Stefaan Verhulst

Science Trends: “Located at the complex intersection of economic development and environmental change, cities play a central role in our efforts to move towards sustainability. Reducing air and water pollution, improving energy efficiency while securing energy supply, and minimizing vulnerabilities to disruptions and disturbances are interconnected and pose a formidable challenge, with their dynamic interactions changing in highly complex and unpredictable manners….

The Beijing City Lab demonstrates the usefulness of open urban data in mapping urbanization with a fine spatiotemporal scale and reflecting social and environmental dimensions of urbanization through visualization at multiple scales.

The basic principle of open data will generate significant opportunities for promoting inter-disciplinary and inter-organizational research, producing new data sets through the integration of different sources, avoiding duplication of research, facilitating the verification of previous results, and encouraging citizen scientists and crowdsourcing approaches. Open data also is expected to help governments promote transparency, citizen participation, and access to information in policy-making processes.

Despite a significant potential, however, there still remain numerous challenges in facilitating innovation for urban sustainability through open data. The scope and amount of data collected and shared are still limited, and the quality control, error monitoring, and cleaning of open data is also indispensable in securing the reliability of the analysis. Also, the organizational and legal frameworks of data sharing platforms are often not well-defined or established, and it is critical to address the interoperability between various data standards, balance between open and proprietary data, and normative and legal issues such as the data ownership, personal privacy, confidentiality, law enforcement, and the maintenance of public safety and national security….

These findings are described in the article entitled Facilitating data-intensive approaches to innovation for sustainability: opportunities and challenges in building smart cities, published in the journal Sustainability Science. This work was led by Masaru Yarime from the City University of Hong Kong….(More)”.

Selected Readings on Data, Gender, and Mobility

Curated on Posted on by Michelle Winowatan

By Michelle Winowatan, Andrew Young, and Stefaan Verhulst

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data, gender, and mobility was originally published in 2017.

This edition of the Selected Readings was  developed as part of an ongoing project at the GovLab, supported by Data2X, in collaboration with UNICEF, DigitalGlobe, IDS (UDD/Telefonica R&D), and the ISI Foundation, to establish a data collaborative to analyze unequal access to urban transportation for women and girls in Chile. We thank all our partners for their suggestions to the below curation – in particular Leo Ferres at IDS who got us started with this collection; Ciro Cattuto and Michele Tizzoni from the ISI Foundation; and Bapu Vaitla at Data2X for their pointers to the growing data and mobility literature. 

Introduction

Daily mobility is key for gender equity. Access to transportation contributes to women’s agency and independence. The ability to move from place to place safely and efficiently can allow women to access education, work, and the public domain more generally. Yet, mobility is not just a means to access various opportunities. It is also a means to enter the public domain.

Women’s mobility is a multi-layered challenge
Women’s daily mobility, however, is often hampered by social, cultural, infrastructural, and technical barriers. Cultural bias, for instance, limits women mobility in a way that women are confined to an area with close proximity to their house due to society’s double standard on women to be homemakers. From an infrastructural perspective, public transportation mostly only accommodates home-to-work trips, when in reality women often make more complex trips with stops, for example, at the market, school, healthcare provider – sometimes called “trip chaining.” From a safety perspective, women tend to avoid making trips in certain areas and/or at certain time, due to a constant risk of being sexually harassed on public places. Women are also pushed toward more expensive transportation – such as taking a cab instead of a bus or train – based on safety concerns.

The growing importance of (new sources of) data
Researchers are increasingly experimenting with ways to address these interdependent problems through the analysis of diverse datasets, often collected by private sector businesses and other non-governmental entities. Gender-disaggregated mobile phone records, geospatial data, satellite imagery, and social media data, to name a few, are providing evidence-based insight into gender and mobility concerns. Such data collaboratives – the exchange of data across sectors to create public value – can help governments, international organizations, and other public sector entities in the move toward more inclusive urban and transportation planning, and the promotion of gender equity.
The below curated set of readings seek to focus on the following areas:

  1. Insights on how data can inform gender empowerment initiatives,
  2. Emergent research into the capacity of new data sources – like call detail records (CDRs) and satellite imagery – to increase our understanding of human mobility patterns, and
  3. Publications exploring data-driven policy for gender equity in mobility.

Readings are listed in alphabetical order.

We selected the readings based upon their focus (gender and/or mobility related); scope and representativeness (going beyond one project or context); type of data used (such as CDRs and satellite imagery); and date of publication.

Annotated Reading List

Data and Gender

Blumenstock, Joshua, and Nathan Eagle. Mobile Divides: Gender, Socioeconomic Status, and Mobile Phone Use in Rwanda. ACM Press, 2010.

  • Using traditional survey and mobile phone operator data, this study analyzes gender and socioeconomic divides in mobile phone use in Rwanda, where it is found that the use of mobile phones is significantly more prevalent in men and the higher class.
  • The study also shows the differences in the way men and women use phones, for example: women are more likely to use a shared phone than men.
  • The authors frame their findings around gender and economic inequality in the country to the end of providing pointers for government action.

Bosco, Claudio, et al. Mapping Indicators of Female Welfare at High Spatial Resolution. WorldPop and Flowminder, 2015.

  • This report focuses on early adolescence in girls, which often comes with higher risk of violence, fewer economic opportunity, and restrictions on mobility. Significant data gaps, methodological and ethical issues surrounding data collection for girls also create barriers for policymakers to create evidence-based policy to address those issues.
  • The authors analyze geolocated household survey data, using statistical models and validation techniques, and creates high-resolution maps of various sex-disaggregated indicators, such as nutrition level, access to contraception, and literacy, to better inform local policy making processes.
  • Further, it identifies the gender data gap and issues surrounding gender data collection, and provides arguments for why having a comprehensive data can help create better policy and contribute to the achievements of the Sustainable Development Goals (SDGs).

Buvinic, Mayra, Rebecca Furst-Nichols, and Gayatri Koolwal. Mapping Gender Data Gaps. Data2X, 2014.

  • This study identifies gaps in gender data in developing countries on health, education, economic opportunities, political participation, and human security issues.
  • It recommends ways to close the gender data gap through censuses and micro-level surveys, service and administrative records, and emphasizes how “big data” in particular can fill the missing data that will be able to measure the progress of women and girls well being. The authors argue that dentifying these gaps is key to advancing gender equality and women’s empowerment, one of the SDGs.

Catalyzing Inclusive FInancial System: Chile’s Commitment to Women’s Data. Data2X, 2014.

  • This article analyzes global and national data in the banking sector to fill the gap of sex-disaggregated data in Chile. The purpose of the study is to describe the difference in spending behavior and priorities between women and men, identify the challenges for women in accessing financial services, and create policies that promote women inclusion in Chile.

Ready to Measure: Twenty Indicators for Monitoring SDG Gender Targets. Open Data Watch and Data2X, 2016.

  • Using readily available data this study identifies 20 SDG indicators related to gender issues that can serve as a baseline measurement for advancing gender equality, such as percentage of women aged 20-24 who were married or in a union before age 18 (child marriage), proportion of seats held by women in national parliament, and share of women among mobile telephone owners, among others.

Ready to Measure Phase II: Indicators Available to Monitor SDG Gender Targets. Open Data Watch and Data2X, 2017.

  • The Phase II paper is an extension of the Ready to Measure Phase I above. Where Phase I identifies the readily available data to measure women and girls well-being, Phase II provides informations on how to access and summarizes insights from this data.
  • Phase II elaborates the insights about data gathered from ready to measure indicators and finds that although underlying data to measure indicators of women and girls’ wellbeing is readily available in most cases, it is typically not sex-disaggregated.
  • Over one in five – 53 out of 232 – SDG indicators specifically refer to women and girls. However, further analysis from this study reveals that at least 34 more indicators should be disaggregated by sex. For instance, there should be 15 more sex-disaggregated indicators for SDG number 3: “Ensure healthy lives and promote well-being for all at all ages.”
  • The report recommends national statistical agencies to take the lead and assert additional effort to fill the data gap by utilizing tools such as the statistical model to fill the current gender data gap for each of the SDGs.

Reed, Philip J., Muhammad Raza Khan, and Joshua Blumenstock. Observing gender dynamics and disparities with mobile phone metadata. International Conference on Information and Communication Technologies and Development (ICTD), 2016.

  • The study analyzes mobile phone logs of millions of Pakistani residents to explore whether there is a difference in mobile phone usage behavior between male and female and determine the extent to which gender inequality is reflected in mobile phone usage.
  • It utilizes mobile phone data to analyze the pattern of usage behavior between genders, and socioeconomic and demographic data obtained from census and advocacy groups to assess the state of gender equality in each region in Pakistan.
  • One of its findings is a strong positive correlation between proportion of female mobile phone users and education score.

Stehlé, Juliette, et al. Gender homophily from spatial behavior in a primary school: A sociometric study. 2013.

    • This paper seeks to understand homophily, a human behavior characterizes by interaction with peers who have similarities in “physical attributes to tastes or political opinions”. Further, it seeks to identify the magnitude of influence, a type of homophily has to social structures.
    • Focusing on gender interaction among primary school aged children in France, this paper collects data from wearable devices from 200 children in the period of 2 days and measure the physical proximity and duration of the interaction among those children in the playground.
  • It finds that interaction patterns are significantly determined by grade and class structure of the school. Meaning that children belonging to the same class have most interactions, and that lower grades usually do not interact with higher grades.
  • From a gender lens, this study finds that mixed-gender interaction lasts shorter relative to same-gender interaction. In addition, interaction among girls is also longer compared to interaction among boys. These indicate that the children in this school tend to have stronger relationships within their own gender, or what the study calls gender homophily. It further finds that gender homophily is apparent in all classes.

Data and Mobility

Bengtsson, Linus, et al. Using Mobile Phone Data to Predict the Spatial Spread of Cholera. Flowminder, 2015.

  • This study seeks to predict the 2010 cholera epidemic in Haiti using 2.9 million anonymous mobile phone SIM cards and reported cases of Cholera from the Haitian Directorate of Health, where 78 study areas were analyzed in the period of October 16 – December 16, 2010.
  • From this dataset, the study creates a mobility matrix that indicates mobile phone movement from one study area to another and combines that with the number of reported case of cholera in the study areas to calculate the infectious pressure level of those areas.
  • The main finding of its analysis shows that the outbreak risk of a study area correlates positively with the infectious pressure level, where an infectious pressure of over 22 results in an outbreak within 7 days. Further, it finds that the infectious pressure level can inform the sensitivity and specificity of the outbreak prediction.
  • It hopes to improve infectious disease containment by identifying areas with highest risks of outbreaks.

Calabrese, Francesco, et al. Understanding Individual Mobility Patterns from Urban Sensing Data: A Mobile Phone Trace Example. SENSEable City Lab, MIT, 2012.

  • This study compares mobile phone data and odometer readings from annual safety inspections to characterize individual mobility and vehicular mobility in the Boston Metropolitan Area, measured by the average daily total trip length of mobile phone users and average daily Vehicular Kilometers Traveled (VKT).
  • The study found that, “accessibility to work and non-work destinations are the two most important factors in explaining the regional variations in individual and vehicular mobility, while the impacts of populations density and land use mix on both mobility measures are insignificant.” Further, “a well-connected street network is negatively associated with daily vehicular total trip length.”
  • This study demonstrates the potential for mobile phone data to provide useful and updatable information on individual mobility patterns to inform transportation and mobility research.

Campos-Cordobés, Sergio, et al. “Chapter 5 – Big Data in Road Transport and Mobility Research.” Intelligent Vehicles. Edited by Felipe Jiménez. Butterworth-Heinemann, 2018.

  • This study outlines a number of techniques and data sources – such as geolocation information, mobile phone data, and social network observation – that could be leveraged to predict human mobility.
  • The authors also provide a number of examples of real-world applications of big data to address transportation and mobility problems, such as transport demand modeling, short-term traffic prediction, and route planning.

Lin, Miao, and Wen-Jing Hsu. Mining GPS Data for Mobility Patterns: A Survey. Pervasive and Mobile Computing vol. 12,, 2014.

  • This study surveys the current field of research using high resolution positioning data (GPS) to capture mobility patterns.
  • The survey focuses on analyses related to frequently visited locations, modes of transportation, trajectory patterns, and placed-based activities. The authors find “high regularity” in human mobility patterns despite high levels of variation among the mobility areas covered by individuals.

Phithakkitnukoon, Santi, Zbigniew Smoreda, and Patrick Olivier. Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data. PLoS ONE, 2012.

  • This study used a year’s call logs and location data of approximately one million mobile phone users in Portugal to analyze the association between individuals’ mobility and their social networks.
  • It measures and analyze travel scope (locations visited) and geo-social radius (distance from friends, family, and acquaintances) to determine the association.
  • It finds that 80% of places visited are within 20 km of an individual’s nearest social ties’ location and it rises to 90% at 45 km radius. Further, as population density increases, distance between individuals and their social networks decreases.
  • The findings in this study demonstrates how mobile phone data can provide insights to “the socio-geography of human mobility”.

Semanjski, Ivana, and Sidharta Gautama. Crowdsourcing Mobility Insights – Reflection of Attitude Based Segments on High Resolution Mobility Behaviour Data. vol. 71, Transportation Research, 2016.

  • Using cellphone data, this study maps attitudinal segments that explain how age, gender, occupation, household size, income, and car ownership influence an individual’s mobility patterns. This type of segment analysis is seen as particularly useful for targeted messaging.
  • The authors argue that these time- and space-specific insights could also provide value for government officials and policymakers, by, for example, allowing for evidence-based transportation pricing options and public sector advertising campaign placement.

Silveira, Lucas M., et al. MobHet: Predicting Human Mobility using Heterogeneous Data Sources. vol. 95, Computer Communications , 2016.

  • This study explores the potential of using data from multiple sources (e.g., Twitter and Foursquare), in addition to GPS data, to provide a more accurate prediction of human mobility. This heterogenous data captures popularity of different locations, frequency of visits to those locations, and the relationships among people who are moving around the target area. The authors’ initial experimentation finds that the combination of these sources of data are demonstrated to be more accurate in identifying human mobility patterns.

Wilson, Robin, et al. Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Current Disasters, 2016.

  • Utilizing call detail records of 12 million mobile phone users in Nepal, this study seeks spatio-temporal details of the population after the earthquake on April 25, 2015.
  • It seeks to answer the problem of slow and ineffective disaster response, by capturing near real-time displacement pattern provided by mobile phone call detail records, in order to inform humanitarian agencies on where to distribute their assistance. The preliminary results of this study were available nine days after the earthquake.
  • This project relies on the foundational cooperation with mobile phone operator, who supplied the de-identified data from 12 million users, before the earthquake.
  • The study finds that shortly after the earthquake there was an anomalous population movement out of the Kathmandu Valley, the most impacted area, to surrounding areas. The study estimates 390,000 people above normal had left the valley.

Data, Gender and Mobility

Althoff, Tim, et al. “Large-Scale Physical Activity Data Reveal Worldwide Activity Inequality.” Nature, 2017.

  • This study’s analysis of worldwide physical activity is built on a dataset containing 68 million days of physical activity of 717,527 people collected through their smartphone accelerometers.
  • The authors find a significant reduction in female activity levels in cities with high active inequality, where high active inequality is associated with low city walkability – walkability indicators include pedestrian facilities (city block length, intersection density, etc.) and amenities (shops, parks, etc.).
  • Further, they find that high active inequality is associated with high levels of inactivity-related health problems, like obesity.

Borker, Girija. “Safety First: Street Harassment and Women’s Educational Choices in India.” Stop Street Harassment, 2017.

  • Using data collected from SafetiPin, an application that allows user to mark an area on a map as safe or not, and Safecity, another application that lets users share their experience of harassment in public places, the researcher analyzes the safety of travel routes surrounding different colleges in India and their effect on women’s college choices.
  • The study finds that women are willing to go to a lower ranked college in order to avoid higher risk of street harassment. Women who choose the best college from their set of options, spend an average of $250 more each year to access safer modes of transportation.

Frias-Martinez, Vanessa, Enrique Frias-Martinez, and Nuria Oliver. A Gender-Centric Analysis of Calling Behavior in a Developing Economy Using Call Detail Records. Association for the Advancement of Articial Intelligence, 2010.

  • Using encrypted Call Detail Records (CDRs) of 10,000 participants in a developing economy, this study analyzes the behavioral, social, and mobility variables to determine the gender of a mobile phone user, and finds that there is a difference in behavioral and social variables in mobile phone use between female and male.
  • It finds that women have higher usage of phone in terms of number of calls made, call duration, and call expenses compared to men. Women also have bigger social network, meaning that the number of unique phone numbers that contact or get contacted is larger. It finds no statistically significant difference in terms of distance made between calls in men and women.
  • Frias-Martinez et al recommends to take these findings into consideration when designing a cellphone based service.

Psylla, Ioanna, Piotr Sapiezynski, Enys Mones, Sune Lehmann. “The role of gender in social network organization.” PLoS ONE 12, December 20, 2017.

  • Using a large dataset of high resolution data collected through mobile phones, as well as detailed questionnaires, this report studies gender differences in a large cohort. The researchers consider mobility behavior and individual personality traits among a group of more than 800 university students.
  • Analyzing mobility data, they find both that women visit more unique locations over time, and that they have more homogeneous time distribution over their visited locations than men, indicating the time commitment of women is more widely spread across places.

Vaitla, Bapu. Big Data and the Well-Being of Women and Girls: Applications on the Social Scientific Frontier. Data2X, Apr. 2017.

  • In this study, the researchers use geospatial data, credit card and cell phone information, and social media posts to identify problems–such as malnutrition, education, access to healthcare, mental health–facing women and girls in developing countries.
  • From the credit card and cell phone data in particular, the report finds that analyzing patterns of women’s spending and mobility can provide useful insight into Latin American women’s “economic lifestyles.”
  • Based on this analysis, Vaitla recommends that various untraditional big data be used to fill gaps in conventional data sources to address the common issues of invisibility of women and girls’ data in institutional databases.

A Guide to Chicago’s Array of Things Initiative

Curated on Posted on by Stefaan Verhulst

Sean Thornton at Data-Smart City Solutions: “The 606, Chicago’s rails-to-trails project that stretches for 4.2 miles on the city’s northwest side, has been popular with residents and visitors ever since its launch last year.  The trail recently added a new art installation, Blue Sky, that will greet visitors over the next five years with an array of lights and colors. Less noticed, but no less important, will be another array on display near the trail: a sensor node from Chicago’s Array of Things initiative.

If you’re a frequent reader of all things civic tech, then you may have already come across the Array of Things (AoT).  Launched in 2016, the project, which consists of a network of sensor boxes mounted on light posts, has now begun collecting a host of real-time data on Chicago’s environmental surroundings and urban activity.   After installing a small number of sensors downtown and elsewhere in 2016, Chicago is now adding additional sensors across the city and the city’s data portal currently lists locations for all of AoT’s active and yet-to-be installed sensors.  This year, data collected from AoT will be accessible online, providing valuable information for researchers, urban planners, and the general public.

AoT’s public engagement campaign has been picking up steam as well, with a recent community event held this fall. As a non-proprietary project, AoT is being implemented as a tool to improve not just urban planning and sustainability efforts, but quality of life for residents and communities. To engage with the public, project leaders have held meetings and workshops to build relationships with residents and identify community priorities. Those priorities, which vary from community to community, could range from monitoring traffic congestion around specific intersections to addressing air quality concerns at local parks and schoolyards.

The AoT project is a leading example of how new technology—and the Internet of Things (IoT) in particular—is transforming efforts for sustainable urban growth and “smart” city planning.  AoT’s truly multi-dimensional character sets it apart from other smart city efforts: complementing environmental sensor data collection, the initiative includes educational programming, community outreach, and R&D opportunities for academics, startups, corporations, and other organizations that could stand to benefit.

Launching a project like AoT, of course, isn’t as simple as installing sensor nodes and flipping on a switch. AoT has been in the works for years, and its recent launch marks a milestone event for its developers, the City of Chicago, and smart city technologies.  AoT has frequently appeared in the press  – yet often, coverage loses sight of the many facets of this unique project. How did AoT get to where it is today?  What is the project’s significance outside of Chicago? What are AoT’s implications for cities? Consider this article as your primer for all things AoT….(More)”.