Cities worldwide adopted lockdown policies in response to the outbreak of coronavirus disease 2019 (COVID-19), significantly influencing people's travel behavior. In particular, micro-mobility, an emerging mode of urban transport, is profoundly shaped by this crisis. However, there is limited research devoted to understanding the rapidly evolving trip patterns of micro-mobility in response to COVID-19. To fill this gap, we analyze the changes in micro-mobility usage before and during the lockdown period exploiting high-resolution micro-mobility trip data collected in Zurich, Switzerland. Specifically, docked bike, docked e-bike, and dockless e-bike are evaluated and compared from the perspective of space, time and semantics. First, the spatial and temporal analysis results uncover that the number of trips decreased remarkably during the lockdown period. The striking difference between the normal and lockdown period is the decline in the peak hours of workdays. Second, the origin-destination flows are used to construct spatially embedded networks. The results suggest that the origin-destination pairs remain similar during the lockdown period, while the numbers of trips between each origin-destination pair is reduced due to COVID-19 pandemic. Finally, the semantic analysis is conducted to uncover the changes in trip purpose. It is revealed that the proportions of Home, Park, and Grocery activities increase, while the proportions of Leisure and Shopping activities decrease during the lockdown period. The above results can help planners and policymakers better make evidence-based policies regarding micro-mobility in the post-pandemic society.

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 home in comparison with employees who work at their employer's premises or partly or mainly from home We recorded week-long individual GPS data on 702 participants and derived a set of measures of daily mobility (number of trips, trip duration, trip distance, and maximum distance from home) Each measure is modelled against a range of individual and neighbourhood-level covariates Our findings contrast with existing studies that suggest homeworking or self-employment may be associated with lower levels of daily mobility or with compensatory effects between work and non-work travel Overall, our study points to higher levels of daily mobility of owners of small businesses and the self-employed in cities as they travel longer distances Further, some homeworkers have on aggregate longer daily trip distances than ‘traditional’ premise-based employees Most striking, female home-based business owners fall into this group If homeworking is here to stay after the COVID-19 pandemic, we may see both increases and/or decreases of daily mobility depending on worker types and gender © 2020 Elsevier Ltd

Recently, some studies have shown that human movement patterns are strongly associated with regional socioeconomic indicators such as per capita income and poverty rate. These studies, however, are limited in numbers and they have not reached a consensus on what indicators or how effectively they can possibly be used to reflect the socioeconomic characteristics of the underlying populations. In this study, we propose an analytical framework — by coupling large scale mobile phone and urban socioeconomic datasets — to better understand human mobility patterns and their relationships with travelers' socioeconomic status (SES). Six mobility indicators, which include radius of gyration, number of activity locations, activity entropy, travel diversity, k-radius of gyration, and unicity, are derived to quantify important aspects of mobile phone users' mobility characteristics. A data fusion approach is proposed to approximate, at an aggregate level, the SES of mobile phone users. Using Singapore and Boston as case studies, we compare the statistical properties of the six mobility indicators in the two cities and analyze how they vary across socioeconomic classes. The results provide a multifaceted view of the relationships between mobility and SES. Specifically, it is found that phone user groups that are generally richer tend to travel shorter in Singapore but longer in Boston. One of the potential reasons, as suggested by our analysis, is that the rich neighborhoods in the two cities are respectively central and peripheral. For three other mobility indicators that reflect the diversity of individual travel and activity patterns (i.e., number of activity locations, activity entropy, and travel diversity), we find that for both cities, phone users across different socioeconomic classes exhibit very similar characteristics. This indicates that wealth level, at least in Singapore and Boston, is not a factor that restricts how people travel around in the city. In sum, our comparative analysis suggests that the relationship between mobility and SES could vary among cities, and such relationship is influenced by the spatial arrangement of housing, employment opportunities, and human activities.

Successful and efficient urban planning requires direct, rapid evaluations of the urban ventilation potential. An urban ventilation environment can be analyzed and evaluated using numerical simulations; however, such simulations require considerable time and effort, especially at the start of planning or when the area is vast. This study presents a method that can rapidly analyze urban ventilation potential of the whole city and each district by combining the rule-based modeling method with urban enclosure index. The first step of the proposed method is to rapidly generate building models using rule-based modeling, and the buildings in different districts are color coded by their use. Then, several vertical sections of the whole city and each district are cut, and the sectional data is extracted for plotting the enclosure charts. Finally, the enclosure index charts are superimposed on the urban wind rose diagram to analyze the ventilation potential of the whole city and each district, and optimization suggestions are proposed based on the analysis results. A case study was then performed on Lipu County, Guangxi, China and the optimization suggestions were proposed from the aspects of road orientations, building layout and the arrangement of greenbelts and open recreational space.

Urban environments are defined and modeled in a variety of ways depending on the scientific approach to analyze them. Even though a number of analysis could benefit from using a single model and re-using results of one for the sake of the other, so far no single data model is available. Moreover, the existing standardized models focus on describing objects in and around urban architectural void rather than the spaces themselves. Nevertheless, a number of phenomena such as heat, energy, pollution, also including social and mobility aspects would undoubtedly benefit from using a model that is explicitly focused on defining the urban architectural void and its characteristics as continuous field, interconnected network or series of spatial units. Therefore, this paper aims to suggest a versatile data model that would allow to separate, interpret, analyze and visualize the urban architectural void using a standardized automated procedure. The model relies on Gestalt theories for space compartmentalization. It allows performing various kinds of analysis and storing their results in a unified format using core concepts of GIS. The model can be rendered both as a 2D and 3D representation. Finally, user intervention and parameter calibration is allowed at every principal step of an automated procedure.

In earthquake disasters, the leading causes of death are directly related both to build collapses and fatalities during the following evacuation phase. Allowing people to autonomously gain safe areas and assembly points should be the basis for reducing human losses in urban systems. However, some important environmental and behavioural factors (e.g. vulnerability of buildings, compact urban fabric, cascade effects, presence of people unfamiliar with the urban layout, absence of information on evacuation paths) can hinder this ‘self-help’-based evacuation process. This issue is really important in historical centres where evacuees suffer a combination of unfavourable conditions to safely escape. This paper concerns a non-invasive solution for guiding people along probable safe evacuation routes in earthquake emergency. The proposed Seismic Pedestrians' Evacuation Dynamic Guidance Expert System (SpeedGuides) considers the influence of the main environmental and behavioural safety factors for evacuees (i.e. street vulnerability, street blockages probability, crowding conditions along paths, presence of mortal dangers, visibility conditions) and combines them in a safety index through the Multi-criteria techniques application. SpeedGuides dynamically collects safety factor data during the time and suggests the possible safest path to the nearest secure zone according to the Dijkstra's algorithm approach. SpeedGuides is an easy-to-use model proposed for application on personal devices (e.g. smartphone) that, taking advantage of different expert methods, allows evacuees to simple enhance their safety. A first effectiveness evaluation of SpeedGuides is provided through an earthquake pedestrians' evacuation simulator in a significant case study. The evacuee performances (with and without the proposed guidance tool) are compared and discussed. Results demonstrate how individuals' safety levels are increased when evacuees use SpeedGuides.

Urban growth may intensify local flooding problems. Understanding the spatially explicit flood consequences of possible future land cover patterns contributes to inform policy for mitigating these impacts. A cellular automata model has been coupled with the openLISEM integrated flood modeling tool to simulate scenarios of urban growth and their consequent flood; the urban growth model makes use of a continuous response variable (the percentage of built-up area) and a spatially explicit simulation of supply for urban development. The models were calibrated for Upper Lubigi (Kampala, Uganda), a sub-catchment that experienced rapid urban growth during 2004–2010; this data scarce environment was chosen in part to test the model's performance with data inputs that introduced important uncertainty. The cellular automata model was validated in Nalukolongo (Kampala, Uganda). The calibrated modeling ensemble was then used to simulate urban growth scenarios of Upper Lubigi for 2020. Two scenarios, trend conditions and a policy of strict protection of existing wetlands, were simulated. The results of simulated scenarios for Upper Lubigi show how a policy of only protecting wetlands is ineffective; further, a substantial increase of flood impacts, attributable to urban growth, should be expected by 2020. The coupled models are operational with regard to the simulation of dynamic feedbacks between flood and suitability for urban growth. The tool proved useful in generating meaningful scenarios of land cover change and comparing their policy drivers as flood mitigation measures in a data scarce environment.

Attributes of areal units are often estimates derived from survey samples. Estimates of these attributes with large standard errors ( SEs ) discount the confidence and validity of spatial analytical results. Large SE for estimates of enumeration units are often the results of small sample sizes in areal units and imply unreliable attribute values. One way to suppress error in attributes is to merge areal units to raise sample size. Traditional regionalization methods serve this purpose, but may unnecessarily alter the geography of the study area. We propose an interactive-heuristic aggregation approach to assist analysts in selecting and merging only units with SEs larger than acceptable levels while preserving the original geography and data as much as possible. Results of this approach and a recent automated optimization method are comparable. Both methods successfully lower the SEs in attribute data, but the interactive approach flexibly adjusts the importance levels of different aggregation criteria across areal units, thus offering a high degree of transparency in the aggregation process. The interactive approach also incorporates subjective and local knowledge of neighborhoods in selecting areal units for aggregation.

To solve current environmental challenges such as biodiversity loss, climate change, and rapid conversion of natural areas due to urbanization and agricultural expansion, researchers are increasingly leveraging large, multi-scale, multi-temporal, and multi-dimensional geospatial data. In response, a rapidly expanding array of collaborative geospatial tools is being developed to help collaborators share data, code, and results. Successful navigation of these tools requires users to understand their strengths, synergies, and weaknesses. In this paper, we identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of collaborative geospatial tools. Using this framework, we then score thirty-one existing collaborative geospatial tools and apply a cluster analysis to create a typology of these tools. We present this typology as a map of the emergent ecosystem and functional niches of collaborative geospatial tools. We identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. Overall, our results highlight how environmental collaborations have benefitted from the use of these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts.

Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs.

Globally urban growth destroys fertile soils and endangers food security. Fertile soils are often located in the vicinity of existing urban areas. Thus, preserving high-quality soils can conflict with the objective of developing compact urban patterns. In this study, we assess the trade-off between compact urban patterns and urban patterns that can help reduce the loss of agricultural productivity by maintaining fertile agricultural soils. We assess the trade-offs for selected municipalities in Switzerland using a multi-objective evolutionary algorithm to create a front of non-dominated solutions. These results are used as a benchmark against which we compare simulations of a Business-As-Usual urban expansion in Switzerland to estimate the potential for reducing the loss of agricultural productivity. By analysing the front of non-dominated solutions, we show that there are areas of open land that can be converted into residential land without trading-off compactness against agricultural productivity. We show that there is a large potential for reducing the loss of agricultural productivity when optimizing the configuration of urban development. This potential strongly varies between municipalities and seems to depend primarily on the amount of demand for new urban land within each municipality. The proposed methodology of using multi-objective optimization, followed by a post-optimality analysis and including results from business-as-usual development can be used to support the decision-making processes in urban planning.

Ridesharing with social contacts (i.e., ‘friends’) is substantially more accepted than with strangers. However, limiting ridesharing to friends while rejecting strangers also reduces ride choices and increases detour costs. This work studies, from a theoretical perspective, whether the additional detour costs of limiting shared rides to social network contacts would be prohibitive. It proposes a social network based ridesharing algorithm with heterogeneous detour tolerances for varied social contacts. The theoretical matching rates and detour costs are compared in a simulation for three levels of social connectivity: travelling with direct contacts only, with direct and indirect contacts, or with anyone. The simulation allows for a systematic and comprehensive testing of system behaviour when varying the parameters of social network structure, detour tolerance, and spatial distribution of friendship. Results show that for a clustered friendship – the expected spatial distribution of a social network growing with a ridesharing network – ridesharing with friends does not cause significantly higher costs. Furthermore, the algorithm prioritising friends can substantially increase the matching of friends. An empirical study justifies the findings.

Cellular automata (CA) models of spatial change have been developed and applied in the context of large regional or metropolitan areas and usually use regular cells, with spatial interactions and transition rules operating within fixed-size neighbourhoods. Model calibration has also been an area of intensive research with many models still using expert-based input to ensure visual calibration of modelled land use maps. In this paper, we present an innovative CA model where irregular cells and variable neighbourhoods are used to better represent space and spatial interaction. Calibration is based on an optimisation procedure that uses particle swarm (PS) to determine the optimal set of parameters of the CA model. Hypothetical test instances are used to assess the CA model and its calibration to small urban areas. Our conclusion was that the use of PS ensures calibration results for the CA model that compare very well with results obtained through other approaches reported in the literature.

Given the present size of modern cities, it is beyond the perceptual capacity of most people to develop a good knowledge about the qualities of the urban space at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions such as ‘where the quality of the physical environment is the most dilapidated in the city that regeneration should be given first consideration’ and ‘in fast urbanising cities, how is the city appearance changing’. To address this issue, in the present study, we present a computer vision method that contains three machine learning models for the large-scale and automatic evaluation on the qualities of the urban environment by leveraging state-of-the-art machine learning techniques and wide-coverage street view images. From various physical qualities that have been identified by previous research to be important for the urban visual experience, we choose two key qualities, the construction and maintenance quality of building facade and the continuity of street wall, to be measured in this research. To test the validity of the proposed method, we compare the machine scores with public rating scores collected on-site from 752 passers-by at 56 locations in the city. We show that the machine learning models can produce a medium-to-good estimation of people's real experience, and the modelling results can be applied in many ways by researchers, planners and local residents.

There are two main issues of concern for land change scientists to consider. First, selecting appropriate and independent land cover change (LCC) drivers is a substantial challenge because these drivers usually correlate with each other. For this reason, we used a well-known machine learning tool called genetic algorithm (GA) to select the optimum LCC drivers. In addition, using the best or most appropriate LCC model is critical since some of them are limited to a specific function, to discover non-linear patterns within land use data. In this study, a support vector regression (SVR) was implemented to model LCC as SVRs use various linear and non-linear kernels to better identify non-linear patterns within land use data. With such an approach, choosing the appropriate kernels to model LCC is critical because SVR kernels have a direct impact on the accuracy of the model. Therefore, various linear and non-linear kernels, including radial basis function (RBF), sigmoid (SIG), polynomial (PL) and linear (LN) kernels, were used across two phases: 1) in combination with GA, and 2) without GA present. The simulated maps resulting from each combination were evaluated using a recently modified version of the receiver operating characteristics (ROC) tool called the total operating characteristic (TOC) tool. The proposed approach was applied to simulate urban growth in Rasht County, which is located in the north of Iran. As a result, an SVR-GA-RBF model achieved the highest area under curve (AUC) value at 94% while the lowest AUC was achieved when using the SVR-LN model at 71%. The results show that the synergy between GA and SVR can effectively optimize the variables selection process used when developing an LCC model, and can enhance the predictive accuracy of SVR.

Several adaptive systems have been proposed that are based on the concepts of smart cities, which can be successfully adapted to natural disasters or other public safety concerns. Since these systems are embedded in a critical and dynamic environment, it is really important to have an infrastructure that is capable of providing real-time environmental information. This paper discusses two research questions that arise from adaptive ubicomp systems: (i) what are the key requirements to provide a reliable WSN-based system (e.g. a river monitoring system)? and (ii) how can an adaptable and reliable WSN-based system be developed? This paper seeks to respond to the former question with the aid of the RESS standard platform. The latter question is answered by employing a generic approach for adaptation. The term “critical systems”, means that any error may result in the loss of human life. We devised the RESS standard after deploying the WSN-based river monitoring system in Brazil for five years. Our prototype underwent several trials, sometimes leading to failure or damage, before we came up with a more reliable solution, which is outlined in this article. Finally, while our RESS platform is policy-free, it is extensible/adaptable and hence can naturally be adapted to new policies.

Cellular automata (CA) models are extensively applied in urban growth modeling in different forms (i.e., pixel or patch). Studies have reported that the patch-based approach can achieve a more realistic urban landscape. However, they are subjected to uncertainties due to a variety of stochastic processes involved, which weakens their effectiveness on urban planning or decision making. Here, we propose a new patch-based urban growth model with heuristic rules that employed logistic CA model with a watershed segmentation algorithm (Segmentation-Patch-CA). The segment objects derived from features of urban CA model were regarded as potential patches for conversion, through defining a utility function that considered both the suitability and heterogeneity of pixels within the patch. Thereafter, two different urban growth types, i.e., organic growth and spontaneous growth, were identified and simulated separately by introducing a landscape expansion index (LEI) that built on neighborhood density analysis. The proposed Segmentation-Patch-CA was applied to Guangzhou City, China. Our results revealed that the proposed model produced a more realistic urban landscape (96.00% and 97.38%) than pixel-based (45.14% and 74.82%) for two modeling periods 2003–2008 and 2008–2012, respectively, when referring to an assembled indicator that closely related to urban patterns (e.g., shape, size, or distribution). Meanwhile, it also achieved a good performance when comparing to other patch-based urban CA models but with less uncertainty. Our model provided a very flexible framework to incorporate patches using segments or self-growth based on pixels, which is very helpful to future urban planning practices.

Through the perspective of tourism, a city as a tourist destination usually consists of multiple tourist attractions such as natural or cultural scenic spots. These attractions scatter in city spaces following some specific forms: clustered in some regions and dispersed in others. It is known that users organize their tours in a city not only according to the distance between different attractions but also according to other factors such as time constraints, expenses, interests, and the similarities between different attractions. Hence, users' travel tours can help us gain a better understanding about the relationships among different attractions at the city scale. In this paper, a methodological framework is developed to detect tourists' spatial-temporal behaviors from social media data, and then such information is used to extract and analyze city's tourism districts. We believe that this city space division will make significant contributions to the fields of urban planning, tourism facility providing, and scenery area constructing. A typical tourism city in China—Huangshan—is selected as our study area for experiments.

This paper compares six land use change (LUC) models, including artificial neural networks (ANNs), support vector regression (SVR), random forest (RF), classification and regression trees (CART), logistic regression (LR), and multivariate adaptive regression splines (MARS). These models were used to simulate urban growth in the megacity of Tehran Metropolitan Area (TMA). These LUC models were integrated with cellular automata (CA) and validated using a variety of goodness-of-fit metrics. The results showed that the percent correct metrics (PCMs) varied between 54.6% for LR and 59.6% for MARS, while the area under curve (AUC) ranged from 67.6% for LR to 74.7% for ANNs. The results also showed a considerable difference between the spatial patterns within the error maps. The results of this comparative study will enable decision makers and scholars to better understand the performance of the models when reducing the number of misses and false alarms is a priority.

Flood watermarks stipulate peak water depths from a flood event, indicating a magnitude of inundation that took place. Such information is invaluable for instantiation and validation of urban flood models. However, collecting and processing such data from land surveys can be costly and time-consuming. New remote sensing and data processing technologies offer improved opportunities to address these issues. The present paper deals with the new structure from motion (SfM) technology and its application in extracting flood watermarks. For this purpose, the first of its kind, side-view SfM surveys with two mobile units were utilised. Survey works were carried out in the vicinity of Ayutthaya heritage area (Thailand) and data obtained were used for setting up numerical models and simulations of the 2011 flood event. The work undertaken demonstrates the significant capability of SfM technology for extraction of flood watermarks. With such technology, it was possible to indicate facades, low-level structures, and susceptible openings, which in turn have improved schematizations of two-dimensional (2D) flood models. The resulting model simulations were found to be more accurate (i.e., more close to the measurements of flood watermarks) than those obtained from models with conventional top-view light detection and ranging (LiDAR) data.

Much of the physical activity and built environment literature has focused on composite walkability indices based on the D variables– design , density , diversity , destination accessibility , and distance to transit . This literature, however, has largely ignored the microscale streetscape features that affect the pedestrian experience. Five street level urban design qualities were recently identified and defined for quantitative measures although these measures are mostly through subjective field observation. View related features such as long sight line and proportion of sky have not yet been objectively measured due to the limitation of data and method. This study uses both 2D and 3D GIS to objectively measure street level urban design qualities in Buffalo, New York and tests their correlation with observed pedestrian counts and Walk Scores. Our results showed that 3D GIS helped to generate objective measures on view related features. These objective measures can help us better understand the influence of street level urban design features on walkability for designing and planning healthy cities.

Provenance, a metadata component referring to the origin and the processes undertaken to obtain a specific geographic digital feature or product, is crucial to evaluate the quality of spatial information and help in reproducing and replicating geospatial processes. However, the heterogeneity and complexity of the geospatial processes, which can potentially modify part or the complete content of datasets, make evident the necessity for describing geospatial provenance at dataset, feature and attribute levels. This paper presents the application of W3C PROV, which is a generic specification to express provenance records, for representing geospatial data provenance at these different levels. In particular, W3C PROV is applied to feature models, where geospatial phenomena are represented as individual features described with spatial (point, lines, polygons, etc.) and non-spatial (names, measures, etc.) attributes. This paper first analyses the potential for representing geospatial provenance in a distributed environment at the three levels of granularity using ISO 19115 and W3C PROV models. Next, an approach for applying the generic W3C PROV provenance model to the geospatial environment is presented. As a proof of concept, we provide an application of W3C PROV to describe geospatial provenance at the feature and attribute levels. The use case presented consists of a conflation of the U.S. Geological Survey dataset with the National Geospatial-Intelligence Agency dataset. Finally, an example of how to capture the provenance resulting from workflows and chain executions with PROV is also presented. The application uses a web processing service, which enables geospatial processing in a distributed system and allows to capture the provenance information based on the W3C PROV ontology at the feature and attribute levels.

The aim of this paper is to study the impacts of a reservation system for on-street parking. Such a system provides drivers looking for on-street parking with information on available parking spaces, thereby possibly reducing the need to cruise for parking and the accompanying negative externalities. The performance of the proposed system is studied using a highly detailed spatial agent-based simulation. The results of the simulations show that users of a reservation system benefit in terms of reduced search time and reduced walking distance under virtually all simulated circumstances. However, societal benefits are not as clear-cut. The benefit in search time for the users of the system comes at a cost to the regular drivers, which see a nearly identical increase in search time. In contrast, the positive impact on walking distance hardly influences walking distance for regular drivers. Hence, we conclude that the introduction of a reservation system for on-street parking results in a more efficient distribution of available parking spaces among drivers searching for parking.