Commentary: A road map for future data-driven urban planning and environmental health research.

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy.

Exploring the nexus of urban form, transport, environment and health in large-scale urban studies: A state-of-the-art scoping review.

Background: As the world becomes increasingly urbanised, there is recognition that public and planetary health relies upon a ubiquitous transition to sustainable cities. Disentanglement of the complex pathways of urban design, environmental exposures, and health, and the magnitude of these associations, remains a challenge. A state-of-the-art account of large-scale urban health studies is required to shape future research priorities and equity- and evidence-informed policies.

The ABC of mobility.

The use of cars in cities has many negative impacts, including pollution, noise and the use of space. Yet, detecting factors that reduce the use of cars is a serious challenge, particularly across different regions. Here, we model the use of various modes of transport in a city by aggregating Active mobility (A), Public Transport (B) and Cars (C), expressing the modal share of a city by its ABC triplet.

Reducing cartel recruitment is the only way to lower violence in Mexico.

Mexican cartels lose many members as a result of conflict with other cartels and incarcerations. Yet, despite their losses, cartels manage to increase violence for years. We address this puzzle by leveraging data on homicides, missing persons, and incarcerations in Mexico for the past decade along with information on cartel interactions.

Detecting trends and shocks in terrorist activities.

Although there are some techniques for dealing with sparse and concentrated discrete data, standard time-series analyses appear ill-suited to understanding the temporal patterns of terrorist attacks due to the sparsity of the events. This article addresses these issues by proposing a novel technique for analysing low-frequency temporal events, such as terrorism, based on their cumulative curve and corresponding gradients. Using an iterative algorithm based on a piecewise linear function, our technique detects trends and shocks observed in the events associated with terrorist groups that would not necessarily be visible using other methods.

Scaling of the morphology of African cities.

A large proportion of Africa's infrastructure is yet to be built. Where and how these new buildings are constructed matters since today's decisions will last for decades. The resulting morphology of cities has lasting implications for a city's energy needs.

Constructing a simplified interurban road network based on crowdsourced geodata.

The road network that connects cities with the existing road infrastructure of a country is a valuable tool for analyzing its transport routes, connectivity, and urban patterns. Yet, it is challenging to construct, given the data available.•We present a method to construct a simplified and connected urban network.

Weekly Crime Concentration.

Objectives: Examine and visualise the temporal concentration of different crime types and detect if their intensity varies through distinct moments of the week.

Methods: The "heartbeat of the crime signal" is constructed by overlapping the weekly time they were suffered. This study is based on more than 220,000 crimes reported to the Mexico City Police Department between January 2016 and March 2020 to capture the day and time of crimes and detect moments of the week in which the intensity exceeds the average frequency.

A paradox of traffic and extra cars in a city as a collective behaviour.

Promoting walking or cycling and reducing cars' use is one of the city planners' main targets, contributing to a sustainable transport method. Yet, the number of vehicles worldwide is increasing as fast as the population, and motorized mobility has become the primary transport method in most cities. Here, we consider modal share as an emergent behaviour of personal decisions.

Vaccination strategies against COVID-19 and the diffusion of anti-vaccination views.

Misinformation is usually adjusted to fit distinct narratives and propagates rapidly through social networks. False beliefs, once adopted, are rarely corrected. Amidst the COVID-19 crisis, pandemic-deniers and people who oppose wearing face masks or quarantine have already been a substantial aspect of the development of the pandemic.

The heartbeat of the city.

Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or "heartbeat" of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City.

A continuous binning for discrete, sparse and concentrated observations.

Discrete observations from data which are obtained from sparse, and yet concentrated events are often observed (e.g. road accidents or murders).

A novel rare event approach to measure the randomness and concentration of road accidents.

Background: Road accidents are one of the main causes of death around the world and yet, from a time-space perspective, they are a rare event. To help us prevent accidents, a metric to determine the level of concentration of road accidents in a city could aid us to determine whether most of the accidents are constrained in a small number of places (hence, the environment plays a leading role) or whether accidents are dispersed over a city as a whole (hence, the driver has the biggest influence).

Methods: Here, we apply a new metric, the Rare Event Concentration Coefficient (RECC), to measure the concentration of road accidents based on a mixture model applied to the counts of road accidents over a discretised space.

Gravity and scaling laws of city to city migration.

Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities.

Early career researchers want Open Science.

Open Science is encouraged by the European Union and many other political and scientific institutions. However, scientific practice is proving slow to change. We propose, as early career researchers, that it is our task to change scientific research into open scientific research and commit to Open Science principles.

Modelling the fear of crime.

How secure people feel in a particular region is obviously linked to the actual crime suffered in that region but the exact relationship between crime and its fear is quite subtle. Two regions may have the same crime rate but their local perception of security may differ. Equally, two places may have the same perception of security even though one may have a significantly lower crime rate.

A measure of the concentration of rare events.

We introduce here an index, which we call the Rare Event Concentration Coefficient (RECC), that is a measure of the dispersion/concentration of events which have a low frequency but tend to have a high level of concentration, such as the number of crimes suffered by a person. The Rare Event Concentration Coefficient is a metric based on a statistical mixture model, with a value closer to zero meaning that events are homogeneously distributed, and a value closer to one meaning that the events have a higher degree of concentration. This measure may be used to compare the concentration of events over different time periods and over different regions.

A metric of the difference between perception of security and victimisation rates.

A person's perception of the level of security at a specific location depends on many factors, including past experiences in that location, the actual crime suffered by the population and more. Thus, when the individual perception that a location is insecure becomes the general rule is when the perception of security becomes an attribute of the region rather than the fears of some of its individuals, hence the relevance of aggregating individual perceptions of security into a single regional perception of security. Residents of two different regions, which have the same levels of crime, of a similar nature, may have different perceptions of the level of security.