Pedestrians, while walking alone, feel unsafe and vulnerable in certain outdoor spaces at certain times of the day. This fear of victimisation often leads pedestrians to avail costlier alternatives, such as taking viable detours or abandoning walking altogether and switching to alternative forms of transport. This fear reduces the appeal of walking, the most basic transport mode, and one that is essential

This work examines different spatial and sociodemographic models for predicting residential fire counts at the census tract level for 118 U.S. fire departments across 25 states. The models give five-year forecasts of residential fire counts for 3392 census tracts which contain over 13 million residents in total. All models described in this paper train on fire incident data from the National Fire Incident

Spatio-temporal patterns of human activities can be affected by events, such as extreme weather. Events cause anomalies that could be expressed by abnormal activity patterns deviating from the inherent ones. The detection of spatio-temporal anomalies thus helps to understand the implicit influencing mechanism with which the external factors affect human activities. Existing methods of spatio-temporal

Multi-Criteria Decision Analysis (MCDA) offers a unique analytical lens for examining socio-economic indicators within cities. Competing, interrelated criteria are combined to produce a single weighted score for each location or spatial unit. The Toronto Foundation's Vital Signs report provides an annual snapshot of Toronto's quality of life, using ten indicator categories. While the report is published

Flood risk maps are essential sources of information for flood risk management (FRM) decisions. Commercial flood models used by the insurance industry are rarely studied in the academic literature which has led to difficulties in understanding their sources of uncertainty and opportunities for improvement. This paper compares regions and residential properties identified as exposed to floods by an

Social sensing is an analytical method to study the interaction between human and space through extracting reliable information from massive volunteered information data. During the ongoing COVID-19 pandemic, there are a large number of Internet social sensing data. However, most of them lack geographic attribute. In order to resolve this problem, this paper proposes a convolutional neural network

This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to

The drug-related problem poses a serious threat to human health and safety. Previous studies have associated drug places with factors related to place management and accessibility, often at several scattered places, as data at the micro level are hard to obtain at a city-wide scale. Google Street View imagery presents a new source for deriving micro built environment characteristics, including place

Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in

While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show

Detecting borders of urban activity spaces is essential for understanding urban dynamic structures. The emerging big geo-data help to extract valuable knowledge about the relationship between urban structures and human activities at fine granularities. Despite the well-developed urban structure and transportation network design technology, barriers attenuating intra-urban travel still exist as borders

Mobile phone data help us to understand human activities. Researchers have investigated the characteristics and relationships of human activities and regional function using information from physical and virtual spaces. However, how to establish location mapping between spaces to explore the relationships between mobile phone call activity and regional function remains unclear. In this paper, we employ

Carsharing services aim at offering short-term car rentals. Those rentals can be offered in different modalities, such as round-trip and one-way. This work simulates the dynamics of possible carsharing clients searching for resources availability and using the carsharing service. Clients can rent a vehicle if there is an available car at the client's origin station and an available parking slot at

Twitter users mention cities in the context of tourist attractions or events, such as protests or games, thus forming a network between cities from which they tweet and cities that they tweet about. This study tackles the challenge of explaining why users tweet about cities outside of their own by analyzing an underlying network of city mentions on Twitter. It applies graph theory as well as various

Flooding has been one of the major issues that have seriously hampered the developments of the urban systems, as well as threatened the urban water environments. To mitigate the impacts of floods, it is highly important to identify effective evacuation plans with the aid of computer-based methods. However, effectively identifying the dynamic evacuation route based on the evolution of flooding status

Countries in Southeast Asia have been developing quickly from a predominantly rural to predominantly urban society, leading to a rapid increase in urban land. This increase in urban land has mainly occurred in river deltas and floodplains, exposing humans and human assets to flood hazard. Here we present an assessment of current and future flood risk in five countries of mainland Southeast Asia, using

Efficiently predicting traffic congestion benefits various traffic stakeholders, from regular commuters and logistic operators to urban planners and responsible authorities. This study aims to give a high-quality estimation of traffic conditions from a large historical Floating Car Data (FCD) with two main goals: (i) estimation of congestion zones on a large road network, and (ii) estimation of travel

How to reveal the hierarchical structures of urban space has long posed a challenge for researchers. Yet, revealing such structures is of fundamental importance to our understanding of multiscale urban organization. This study, therefore, built a spatial interaction network based on a massive body of human movement data to derive the hierarchical organization of ranked urban clusters. These urban clusters

Geospatial data conflation is the process of combining two datasets to create a better one. It has received increased research attention due to the emergence of new data sources and the need to combine information from these sources in spatial analyses. Many conflation methods exist to date, ranging from simple ones based on spatial join, to sophisticated methods based on statistics and optimization

Extracting hidden information from human mobility patterns is one of the long-standing challenges of urban studies. In addition, exploring the relationship between urban functional structure and traffic spatial interaction pattern has long been of interest. Recently, vehicle GPS trajectory data emerged as a popular data source for revealing human mobility patterns and urban functions. However, few

Rapid urbanization in China has prompted plenty of urban facilities to be constructed with the expectation of harmonizing with the rapid growth of urban population. However, regarding the spatial interactions produced by cross-area human mobility, the diversity and variability of residents' trip requirements inevitably cause the deviations of the real interaction patterns from the optimal status determined

This paper discusses the opportunities afforded by novel population sensing technologies in the field of ‘smart’ urban management. In particular, it focuses on the application of these new sources of data in retail analysis. Our goal is to integrate data derived through novel pedestrian counting and point-of-sale systems to build a statistical model that captures the relationship between retail turnover

The ability to predict human mobility, i.e., transitions between a user's significant locations (the home, workplace, etc.) can be helpful in a wide range of applications, including targeted advertising, personalized mobile services, and transportation planning. Most studies on human mobility prediction have focused on the algorithmic perspective rather than on investigating human predictability. Human

Cellular automata (CA) models have increasingly been used to simulate land use/cover changes (LUCC). Metaheuristic optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) have been recently introduced into CA frameworks to generate more accurate simulations. Although Markov Chain Monte Carlo (MCMC) is simpler than PSO and GA, it is rarely used to calibrate CA models

Urban expansion across the globe accelerates land cover change and significantly influences the environment and human beings. Measuring the urban expansion process can help us better understand urban expansion dynamics and contribute to urban growth simulation and spatial planning. By using urban land density (defined as the proportion of the built-up area to the buildable area) as a spatial variable

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory

The experience of walking through a city is influenced by amenities and the visual qualities of its built environment. This paper uses thousands of pedestrian trajectories obtained from GPS signals to construct a desirability index for streets in Boston. We create the index by comparing the actual paths taken by pedestrians with the shortest path between any origin-destination pairs. The index captures

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated

This paper explores the potential of machine learning algorithms (MLAs) for the simulation of intercity networks. To this end, we implement the random forest MLA to simulate the intercity corporate networks created by Fortune China 500 firms in mainland China. The random forest MLA does not require a predefined model but detects patterns directly from the data to automatically build models. The city-dyad

This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure

Establishing the relationship between elements of alternative network-based representations of a system is an important component of conflation, error/uncertainty assessment, change detection, and other geospatial analyses. An array of techniques have been proposed in support of such tasks to relate arcs and nodes in one network with those in another based upon establishing linkages among their attributes

Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built

Points of interest (POIs) digitally represent real-world amenities as point locations. POI categories (e.g. restaurant, hotel, museum etc.) play a prominent role in several location-based applications such as social media, navigation, recommender systems, geographic information retrieval tools, and travel-related services. The majority of user queries in these applications center around POI categories

The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups

Landmarks have been identified as relevant and prominent urban elements, explicitly involved in human navigation processes. Despite the understanding accumulated around their functions, landmarks have not been included in simulation models of pedestrian movement in urban environments. In this paper, we describe an Agent-Based Model (ABM) for pedestrian movement simulation that incorporates the role

Urban areas of interest (AOIs) represent areas within the urban environment featuring high levels of public interaction, with their understanding holding utility for a wide range of urban planning applications. Within this context, our study proposes a novel space-time analytical framework and implements it to the taxi GPS data for the extent of Manhattan, NYC to identify and describe 31 road-constrained

Segregation often dismantles common activity spaces and isolates people of different backgrounds, leading to irreconcilable inequalities that disfavour the poor and minorities and intensifies societal fragmentation. Therefore, segregation has become an increasing concern and topic of research with studies typically concentrating on the residential communities of a particular racial or socioeconomic

Maptables are increasingly used to support collaborative spatial planning processes. Despite the proven benefits and claimed potential of using a maptable in such processes, software applications specifically designed for this device are still scarce. Moreover, often-used applications do not fully exploit the touch functionality of a maptable, or have low usability. To address this gap, we developed

Urban growth is a complex spatio-temporal phenomenon that includes built-up activities taking place both horizontally and vertically. The built-up land density in a city is a function of land desirability and suitability of a location in terms of the quality of the available public services, access to infrastructure, neighborhood type, vibrancy of socio-economic and cultural characteristics. The simulation

Based on an offender spatial decision-making perspective, this burglary target location choice study aims to understand how physical and social barriers affect why residential burglars commit their crimes at particular locations in a major Chinese city. Using data on 3860 residential burglaries committed by 3772 burglars between January 2012 and June 2016 in ZG city, China, conditional logit (discrete

Utility networks comprise a fundamental part of our complex urban systems and the integration of digital representations of these networks across multiple spatial scales can be used to help address priority challenges. Deteriorating water utility infrastructure and low routing redundancy result in network fragility and thus supply outages when assets fail. Water distribution network configurations

As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital

Cities are continually evolving through adjustments to zonings. The potential to integrate more equitable and effective value capture mechanisms into the rezoning process makes estimation of increases in land value from rezoning of interest to urban planners. The use of Residual Land Valuation (RLV), while accepted practice in development feasibility calculations, tends to be absent from the rezoning

Since new (Power) Transmission Lines (TLs) can have a long-term effect on the makeup of a landscape and on the human living space, it should be expected that the route of any new TL will be based on objective criteria that take into account the views of the public. Geographic Information Science (GIScience) provides powerful tools that assist in the determination of feasible locations for new TLs based

Cellular Automata (CA) are widely used to model the dynamics within complex land use and land cover (LULC) systems. Past CA model research has focused on improving the technical modeling procedures, and only a few studies have sought to improve our understanding of the nonlinear relationships that underlie LULC change. Many CA models lack the ability to simulate the detailed patch evolution of multiple

The landscape of cycling activities from a dockless bike-share system is dynamic over space and time. Decoding usage patterns from bike-share trips have been a highly charged area in the literature. Therefore, this study aims at developing an analytical approach to understanding the trip demands of bike-share and model the spatiotemporal dynamics of cycling flows. Under the proposed framework, global

The challenge posed by the management of sudden migration of large groups of people lies in the ability to portray and predict the scale and dynamics of such movement accurately. This is further complicated by the fact that associated data pertaining to such migration are largely incomplete or untrustworthy. In view of the shortcomings in respect of modelling instances of conflict and the lack of data

In China, the illegal adoption of missing persons and especially of missing children is a major public safety issue that affects social and family stability. Recent work has established a trafficking information network developed from a volunteer-managed database of missing persons that identifies and locates node cities and critical paths of illegal adoption. In order to evaluate locations where trafficking

This study develops a Geographic Data Science framework that transforms the Foursquare check-in locations and user origin-destination flows data into knowledge about the emerging forms and characteristics of cities' neighbourhoods. We employ a longitudinal mobility dataset describing human interactions with Foursquare venues in ten global cities: Chicago, Istanbul, Jakarta, London, Los Angeles, New

News travels fast and far, and the general idea is that the spatial extent of news coverage has increased over time. Information flows are always involved in systems of interdependent cities. This is the reason why George Zipf and Allan Pred, both pioneers of the urban systems literature, were eager to obtain data on these relations to understand urban system dynamics. However, because of limited resources

The rise of small businesses, self-employment, and homeworking are transforming traditional industrial ways of working. Our research fills a noticeable gap in the literature by using portable devices (i.e., smartphones) to capture individual mobility data on an understudied population group – small business owners (owner managers and self-employed with up to 49 employees) and whether they work from

Estimating health outcomes at a neighborhood scale is important for promoting urban health, yet costly and time-consuming. In this paper, we present a machine-learning-enabled approach to predicting the prevalence of six common non-communicable chronic diseases at the census tract level. We apply our approach to the City of Austin and show that our method can yield fairly accurate predictions. In searching

Digital footprints collected from social media platforms are often clustered using methods such as the density-based spatial clustering of applications with noise (DBSCAN) and its variants to identify daily travel activities (e.g., dwelling, working, entertainment, and eating). However, these clustering methods mostly only consider the spatial distribution of travel activity points while ignoring their

Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities

Mobile activity tracking data, i.e. data collected by mobile applications that enable activity tracking based on the use of the Global Navigation Satellite Systems (GNSS), contains information on cycling in urban areas at an unprecedented spatial and temporal extent and resolution. It can be a valuable source of information about the quality of bicycling in the city. Required is a notion of quality

The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a Call Details Record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Voronoi polygon of a cell

Geo-located travel blogs, a new data source, enable to achieve more detailed analysis of tourists' spatio-temporal behavior. Taking Chinese tourists in Nordic countries as the research object, this paper focuses on their behavior, seasonal patterns and complex network effects by using geo-located travel blog data collected from Qunar.com. The results show that: (1) Chinese tourists visiting Nordic