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

The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socio-economic level is of crucial relevance

Traffic volume is a critical piece of information in many applications, such as transportation long-range planning and traffic operation analysis. Effectively capturing traffic volumes on a network scale is beneficial to Transportation Systems Management & Operations (TSM&O). Yet it is impractical to install sensors to cover a large road network. To address this issue, spatial prediction techniques

This study evaluates how random variations in urban development affect projections of urban water management over long time horizons, based on combined simulations of urban development and water management. The urban development was simulated based on existing procedural tools; however, novel procedures were implemented to allow the simulation of re-development in existing areas. The urban layout was

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between

Recent studies suggest that the traditional determinants of housing wealth are insufficient to explain its current inequality levels. Thus, they argue that efforts should focus on understanding institutional factors. From the perspective of complex adaptive systems, institutions are more than the ‘the rules of the game’, they also consider the interaction protocols or the ‘algorithm’ through which

Google Earth Engine (GEE) has been increasingly used in environmental and urban studies due to its cloud-based geospatial processing capability and accessibility to a large collection of geospatial datasets like Landsat, Modis, etc. However, at present, ecological and urban modeling efforts based on GEE are facing three grave challenges: current illustrations of GEE are to a large extent “straightforward

Rapid urbanization has led to a massive influx of people into cities. When many people congregate in urban areas, crowd crushing emergencies are likely to occur. If vulnerable areas with potential evacuation problems are detected in advance, crowd crushing emergencies may be minimized or even avoided. Thus, an evacuation vulnerability assessment from a precautionary perspective is fundamental. However

High-quality temperature data at a finer spatio-temporal scale is critical for analyzing the risk of heat exposure and hazards in urban environments. The variability of urban landscapes makes cities a challenging environment for quantifying heat exposure. Most of the existing heat hazard studies have inherent limitations on two fronts; first, the spatio-temporal granularities are too coarse, and second

Greenery and open spaces play significant roles in environmentally sustainable societies, providing urban ecosystem services and economic benefits that reduce urban poverty. Current urban poverty research has solely focused on top-down observations or direct human exposure to greenery and open spaces and has failed to sense landscape characteristics, including occupation and inequality, representing

Mobility models have a broad range of applications in areas related to human movements, such as urban planning, transportation, and simulations of diseases spread. In the last decade, the extensive geolocated user trajectories collected from mobile devices allowed for more realistic mobility modelling, improving its accuracy. However, mobility data sharing raises privacy concerns, which in turn limits

In his classic book “The Image of the City” Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated

Urban land use is known to affect commuting efficiency according to the excess commuting framework. However, most studies do not include temporal dynamics, and those that do, focus on decadal, yearly, or daily temporal resolutions. However, commuting is not a stationary spatial process. Since people leave home and start their jobs at different times of the day and since traffic congestion varies throughout

This study investigated the spatial consistency of the SLEUTH urban growth and land use change model using a massive data set. The research asks whether SLEUTH can yield both a reliable forecast of land use in the state of California for the year 2100 CE, and an assessment of the forecast's reliability. Data were prepared, and SLEUTH calibrated for 174 tiles made by partitioning the data within the

Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches

Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities

Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster

Spatial flow data are often used to represent spatial interaction phenomena such as daily commuting trips, human or animal migrations, and the exchanges of commodities, capital, or even information between regions. With the increasingly available large volume of flow data in fine spatiotemporal resolution, exploratory spatial data analysis (ESDA) has become more important than ever to gain understanding

This study investigates the impact of extreme weather events on urban human flow disruptions using location-based service data obtained from Baidu Map. Utilizing the 2018 Typhoon Mangkhut as an example, the spatial and temporal variations of urban human flow patterns in Shenzhen are examined using GIS and spatial flow analysis. In addition, the variation of human flow by different urban functions (e

Use of Public Participation Geographic Information System (PPGIS) for data collection has been significantly growing over the past few years in different areas of research and practice. With the growing amount of data, there is little doubt that a potentially wider community can benefit from open access to them. Additionally, open data add to the transparency of research and can be considered as an

GIS-based Multi-Criteria Decision Analysis is a well-known decision support tool that can be used in a wide variety of contexts. It is particularly useful for territorial planning in situations where several actors with different, and sometimes contradictory, point of views have to take a decision regarding land use development. While the impact of the weights used to represent the relative importance

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public

The roll-out of 5G infrastructure can provide enhanced high capacity, low latency communications enabling a range of new use cases. However, to deliver the improvements 5G promises, we need to understand how to enhance capacity and coverage, at reasonable cost, across space and over time. In this paper, we take a spatio-temporal simulation modeling approach, using industry-standard engineering models

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in

Planning support systems (PSS) enabled by smart city technologies (big data and information and communication technologies (ICTs)) are becoming more widespread in their availability, but have not yet been fully recognized as being useful in planning practice. Thus, a better understanding of the determinants of PSS usefulness in practice helps to improve the functional support of PSS for smart cities

Socio-economic activities and incidents such as crimes and traffic accidents have a negative impact on our society, and their reduction has been a priority in our social-science endeavour. These events are not uniform in their occurrences but, rather, manifest a distinct set of concentrations, commonly known as hotspots. Detecting the exact extent, shape and changes in these hotspots can lead to deeper

As a major method to serve the demand for land requirement in Chinese urban construction, land acquisition has been intensified since the speeding up of urbanization. However, clashes arise during the process of land acquisition, out of conflicts of interests, which to some extent have affected the development and direction of urban expansion, as well as social harmony and stability. Therefore, the

Uncertainty and error are inevitable when continuously varying geographic phenomena are approximated. Describing geography and spatial relationships is, therefore, challenging using analytics for planning and management. A critical aspect of spatial description has to do with allocation, a process of determining who is best served by which facility. Allocation is useful in reflecting customer behavior

Roadside channel systems are critical for the management of stormwater runoff and the protection of the structural integrity of roads; and thus, require systematic evaluation and maintenance. However, the evaluation of these systems remains ad hoc due to the lack of efficient inspection methods. This paper contributes to filling this gap by providing an automated process for the inspection and evaluation

Metrics representing exposure to the natural environment are widely used in environmental health-related studies. They are calculated using a variety of different data sources representing greenspace and a range of buffer sizes representing human interaction with the environment. Previous studies have identified issues relating to buffer distance and scaling effects on greenspace exposure assessments

Since 2015, Uganda has welcomed over 700,000 refugees from South Sudan, Democratic Republic of the Congo, Burundi, and other East African nations, and currently hosts over 1.4 million refugees with 92% of that population living in UNHCR-managed settlements. Despite refugee settlements being essential spaces for physical protection and humanitarian aid distribution and reception, the sheer rate of refugee

High uncertainty about future urbanization and flood risk conditions limits the ability to increase resiliency in traditional scenario-based urban planning. While scenario planning integrating urban growth prediction modeling is becoming more common, these models have not been effectively linked with future flood plain changes due to sea level rise. This study advances scenario planning by integrating

There are numerous studies on the identification and explanation of the implementation gap of planning support systems (PSS), that support the smart planning process by analyzing and visualizing data, but which are currently not systematically used in urban planning practice. There is insufficient knowledge about how the PSS implementation gap can be bridged and how PSS can be embedded into the planning

Geographic variation in digital inequality manifests as a result of a range of demographic, attitudinal, behavioural and locational factors. To better understand this multidimensional geography, our paper develops a new geodemographic classification for the spatial extent of Great Britain. In this model, we integrate a range of new small area measures that are drawn from multiple new forms of data

We propose a method which uses Flickr tags to predict a wide variety of environmental features, such as climate data, land cover categories, species occurrence, and human assessments of scenicness. The role of Flickr tags in our method is two-fold. First, we show that Flickr tags capture information which is highly complementary to what is found in traditional structured environmental datasets. By

When examining the relationship between landscape characteristics and water quality, most previous studies did not pay enough attention to the spatial aspects of landscape characteristics and water quality sampling stations. We analyzed the spatial pattern of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and suspended solids (SS) in the Han River basin of South Korea to

Many methods can be used to construct geographical cellular automata (CA) models of urban land use, but most do not adequately capture spatial heterogeneity in urban dynamics. Spatial regression is particularly appropriate to address the problem to reproduce urban patterns. To examine the advantages and disadvantages of spatial regression, we compare a spatial lag CA model (SLM-CA), a spatial error

This paper presents ACCESS, an agent-based model for exploring job accessibility inequalities among different social groups. ACCESS allows for investigation on the impact of public transport and land use policies on the residential location of the working population and their accessibility to job opportunities. ACCESS can be adapted to different realities, allowing to represent societies with diverse

Road systems provide a set of essential services to modern societies, and as such, their functioning becomes crucial to sustaining urban ecosystems. Understanding infrastructure robustness is one with understanding how major disruptions affect the functionality of the overall infrastructure network. Preparing for and responding to unexpected disruptions call for a broad understanding of robustness

Land suitability is one of the important variables influencing urbanization and needs to be considered in urban growth simulation and modeling. The present study is aimed to introduce land suitability which is a function of a few important urbanization explanatory drivers into an urban growth model for realistic urban growth simulation. Development of SLEUTH-Suitability, an improved version of the

Addresses are one of the most important geographical reference systems in natural languages. In China, due to the relatively backward address planning, there are a large number of non-standard addresses. This kind of unstructured text makes the management and application of Chinese addresses much more difficult. However, by extracting the computational representations of addresses, it can be structured

Urban parks can offer both physical and psychological health benefits to urban dwellers and provide social, economic, and environmental benefits to society. Earlier research on the usage of urban parks relied on fixed distance or walking time to delineate urban park catchment areas. However, actual catchment areas can be affected by many factors other than park surface areas, such as social capital

An important class of facility location problems deals with service coverage. While there are many types of coverage models, among the most widely applied is the location set covering problem. A common instance is to find the minimum number of facilities that provide service to a region. It has been applied to a broad range of infrastructural services, including transportation, telecommunication, safety

Computational models have been described as exceptionally adept at examining the complex relationships of human and crowd behaviour, with a significant portion dedicated to investigating spatial behaviour in defined environments. Within this context, this paper presents an agent-based model (ABM) for simulating activity in public spaces at the level of the individual user. Although other ABMs of individuals'

There is a Chinese proverb, “if your wine tastes really good, you do not need to worry about the location of your bar (酒香不怕巷子深)”, which implies that the popular places for local residents are sometimes hidden behind an unassuming door or on unexpected streets. Discovering these unassuming places (e.g. restaurants) of a city will benefit the understanding of local culture and help to build livable neighborhoods

There is a recent surge in research focused on urban transformations in the United States via empirical analysis of neighborhood sequences. The alignment-based sequence analysis methods have seen many applications in urban neighborhood change research. However, it is unclear to what extent these methods are robust in terms of producing consistent and converging neighborhood sequence typologies. This

The optimization of scheduling and sequencing of multiple resources during disaster management is a challenge due to substantial uncertainty. This paper presents an emergency operation model that aims to facilitate the scheduling and sequencing resources using multiple stochastic scenarios. The proposed model integrates GIS and Mixed Integer Programming (MIP) approaches. The ultimate goal of this paper

Pedestrian volume is an important indicator of urban walkability and vitality. Hence, information on pedestrian volumes of different streets is indispensable for creating healthy, pedestrian-oriented cities. Pedestrian volume data have traditionally been collected through field observations, which has many methodological limitations, e.g. time-consuming, labor-intensive, and inefficient. Assessing

Understanding and forecasting the dynamics of urban growth can be helpful for making sustainable land-use policies. Computing models can simulate urban growth but many require extensive data input, which cannot be always met. Here we proposed coupling localized spatio-temporal association (LSTA) analysis and binary logistic regression (BLR) to model urban growth from historical land cover configurations

The revolution in mobility-sharing services brings disruptive changes to the transportation landscape around the globe. The authorities often rush to regulate the services without a good knowledge of these new options. In Singapore and some other cities, dockless bike-sharing systems rose and fell in just one year and were followed by the booming of docking scooter-sharing systems. This study conducts

The complexity, variety and volume of urban datasets have soared in the past decade. By utilising these datasets, urban planners and researchers have built a wide range of evidence-based methods and analytical tools for planning and decision-making purposes. However, the data heterogeneity remains one of the key problems in this process. Building generic urban analytics tools adaptable to diverse data

Cities play an important role in fostering and amplifying the transmission of airborne diseases (e.g., influenza) because of dense human contacts. Before an outbreak of airborne diseases within a city, how to determine an appropriate containment area for effective vaccination strategies is unknown. This research treats airborne disease spreads as geo-social interaction patterns, because viruses transmit

Sensitivity analysis (SA) in spatially explicit agent-based models (ABMs) has emerged to address some of the challenges associated with model specification and parameterization. For spatially explicit ABMs, the comparison of spatial or spatio-temporal patterns has been advocated to evaluate models. Nevertheless, less attention has been paid to understanding the extent to which parameter values in ABMs

Measuring change in the spatial arrangement of deprivation over time, and making international, inter-city comparisons, is technically challenging. Meeting these challenges offers a means of furthering understanding and providing new insights into the geography of urban poverty and deprivation. In this paper, we introduce a novel approach to mapping and analysing spatio-temporal patterns of household

Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map

We describe a strategy for regionalizing subnational administrative units in conjunction with harmonizing changes in unit boundaries over time that can be applied to provide small-area geographic identifiers for census microdata. The availability of small-area identifiers blends the flexibility of individual microdata with the spatial specificity of aggregate data. Regionalizing microdata by administrative