City Scale Research Articles

Beyond the Road: A Regional Perspective on Traffic Congestion in Metro Atlanta

Traffic congestion not only affects traffic flow but also influences public perception of congested regions. While analyzing congestion at the road section level can help identify engineering solutions, it often fails to reveal broader spatial patterns and trends at the regional or macro scale unless summarized effectively. This study aims to address these challenges by focusing on regional-scale traffic congestion amounts measured by distanceTime metrics. A 12–month dataset, sampled every 10 min, was analyzed to identify spatial patterns, temporal trends, regional variations, and predictive models in the Metro Atlanta area. The results show that congestion is the most severe and increasing at key urban corridors like Brookhaven–Sandy Springs, the downtown connector, Druid Hills–Decatur, and Johns Creek–Cumming, aligning with recent urban developments. Cities such as Alpharetta, Dunwoody, Brookhaven, Austell, Stone Mountain, East Point, Lake City, Morrow, Fairburn, and Jonesboro show high increasing trends in congestion. Predictive modeling with the long short-term memory (LSTM) method shows promising results for short-term forecasts, though variability in data requires further optimization for certain cities. This research is significant because it demonstrates that congestion amounts measured by distanceTime metrics can be used for assessing regional characteristics broadly at a metropolitan city scale. The findings and methodologies identified in this research might support urban and transportation planning efforts in metropolitan planning organizations, such as the Atlanta Regional Commission, by identifying congestion amounts and trends at both the regional and road scales.

Spatial and Habitat Determinants of Small-Mammal Biodiversity in Urban Green Areas: Lessons for Nature-Based Solutions

In an increasingly anthropogenic world, urban green areas are critical for human well-being because of the ecosystem services they offer. However, the management of these areas often prioritizes economic, architectural, and aesthetic needs over ecological functionality, undermining the benefits they ought to provide. Actions to restore the functional ecological processes of urban green areas have thus become increasingly urgent. This study investigated the impact of urban green area characteristics on the community composition of an indicator animal group to inform Nature-Based Solutions (NBSs) for urban biodiversity and sustainability enhancement. We analyzed data on small mammal community composition from three Italian metropolitan cities (Milan, Florence, and Rome), focusing on the distribution pattern of synanthropic and non-synanthropic species. Small mammal surveys were carried out using hair tubes in sampled areas located along a gradient of green area size and fragmentation. Multiple Linear Mixed Models assessed the influence of spatial and habitat green area characteristics on species richness. Synanthropic species richness was positively associated with manicured urban parks, while non-synanthropic species richness was higher in woodland green areas characterized by high shrub cover. Through a Linear Mixed Model and NODF-based Nestedness analyses, we found that competitive exclusion and selective extinction/colonization processes did not significantly influence community composition. These insights emphasize the major influence of habitat composition in supporting functional communities of urban small mammals. To move toward resilient urban ecosystems, NBSs must primarily be implemented at a local scale by creating green patches with high habitat quality, and secondarily, they should be embedded in an interconnected and functional network at a city scale.

Airborne multi-seasonal LiDAR and hyperspectral data integration for individual tree-level classification in urban green spaces at city scale

Airborne multi-seasonal LiDAR and hyperspectral data integration for individual tree-level classification in urban green spaces at city scale

Systematic identification of factors influencing the spatial distribution of soil organic matter in croplands within the black soil region of Northeastern China across multiple scales

Cultivated soil organic matter (SOM) plays a critical role in maintaining soil health and promoting ecosystem services. However, the multi-scale influences on SOM changes in cropland and their interactions in the Black Soil Region of Northeastern China (BSR-NEC) remain unclear. Existing studies mainly focus on a single scale and lack a systematic multi-scale analysis. In this study, we systematically examined the multi-scale influences on SOM spatial variation in cropland within BSR-NEC using spatial autocorrelation analysis (SA), random forest models (RF), geodetector (GD), and structural equation modeling (SEM). We integrated multi-scale SOM spatial distribution influences across grid scale (GS), watershed scale (WS), and prefecture level city scale (PLS) to unveil significant differences in the spatial distribution of SOM in cropland across regions in BSR-NEC. The research indicated that: (1) the proportion of cropland with SOM content greater than 30 g/kg in BSR-NEC was 68.31 %, where the spatial distribution of SOM was more uniform at the watershed scale; (2) the Moran indices of the spatial distribution of SOM content all indicated that cropland SOM showed a trend of spatial concentration, with the grid scale (GS) > watershed scale (WS) > prefecture level city scale (PLS), mainly manifested in the form of “high-high (HH)” and “low-low (LL)” aggregation types; (3) at multiple scales, temperature and cropland type are always the main factors driving the changes of cropland SOM in BSR-NEC; (4) factors such as precipitation, elevation, and distance from urban centers exhibited varying impacts on SOM; moreover, interactions between any two factors typically resulted in nonlinear enhancements. By developing region-specific management strategies that target these key factors, our findings contribute to advancing precision and efficiency in cropland protection, aligning with the goals of the United Nations Sustainable Development Goals (SDG2030) to promote sustainable land use, improve soil health, and enhance ecosystem resilience.

Transforming CCTV cameras into NO2 sensors at city scale for adaptive policymaking

Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London’s cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London’s mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.

The micro-geography of knowledge exchanges in Montreal: Questioning the importance of the neighbourhood scale in an age of virtual communications

Observation and theory confirm that economic activity can benefit from spatial agglomeration and clustering. Typically this has been analysed at the region or city scale, but recently micro-local and neighbourhood dynamics have drawn attention. Most studies first observe agglomeration, then infer or theorise processes that drive it; these inferred processes have become embedded in urban policy thinking. One such process is localised knowledge exchange, believed to be encouraged by spatial proximity and third spaces such as cafes and parks. In this study of Montreal firms, we directly explore the importance that firms attach to different scales and places at which knowledge exchange occurs. Overall, micro-local and local scales are considered less important than metropolitan and wider scales; third spaces are not considered important, except by marketing innovators; and there is no connection between innovation and the importance of local scale for knowledge acquisition. However, results are not homogeneous across urban context, economic sector or innovation profile: the association between micro-local knowledge exchange and geographical location is complex and cannot be generalised across neighbourhoods or firms.

Assessing institutional sanitation and its impact at a citywide level: an exploration of school sanitation in the Accra Metropolitan Area, Ghana

IntroductionExploring sanitation at a city scale is crucial due to approaches such as Citywide Inclusive Sanitation (CWIS). Sanitation impacts individuals, households and the whole city, as recognised by CWIS. The Shit Flow Diagram Graphic (SFDG) is the primary tool for citywide situation analysis. However, current SFDGs assume that individuals use only home toilets, overlooking the complexity of toilet usage, including public and workplace facilities. Our understanding of citywide sanitation flows is incomplete if analyses ignore the other toilets that people use. This study explored the impact of one type of institutional sanitation, school sanitation, on citywide sanitation flows.MethodsTo do this an overview and analysis of school sanitation at a citywide level was needed and a method to split the school pupils’ excreta flows was developed. Data was collected from secondary sources (e.g. from Ghana Statistical and Educational Services), structured observations (n = 26), and interviews with key informants (n = 15), headteachers (n = 26) and students (n = 39) from across Accra Metropolitan Area, Ghana. This data was used to construct three SFDGs, which were compared using Trend Graphs (graphs used to compare the state of sanitation across the different stages of the sanitation value chain).Results and DiscussionThe findings indicated that school sanitation was less safely managed than household sanitation and that SFDGs could be used assess school-level sanitation. Method development was crucial to accurately partition pupils’ excreta flows. While annual flows from schools appeared insignificant citywide, this seasonal flow could adversely affect public and environmental health during school terms. Therefore, it may be more important to consider the school population as a whole when thinking about the impact of school sanitation. This study highlights the importance of understanding where people spend their day and how this is related to the different sanitation systems they use; it demonstrates the need to move beyond household sanitation. This study successfully demonstrates the level of information that is needed regarding people’s sanitation practices which enables their excreta flows to be split between different facilities. It could be used as a guide for future studies and the further development of methods to explore this topic.

Metropolitan geographies of urban commons: mapping socio-economic alternatives in the Metropolitan Area of Barcelona

ABSTRACT Urban commons literature has grown in diversity and theoretical sophistication. Methodologically, however, it has consistently privileged in-depth case studies at the neighborhood or city scale. This approach reproduces bounded and administratively defined notions of the “urban” and risks neglecting the interconnections between “commons” initiatives and wider economic, social, cultural, and political processes. Moreover, the place-specific articulation of urban commons as an alternative mode of socio-economic organization has rarely been studied as a metropolitan process. In our study, we combine the Marxist, institutional, and environmental justice traditions of urban commons scholarship to advance debates beyond methodological “cityism”. The paper offers a discussion of methods and insights from the assemblage of a database of over 1,100 geolocated citizens’ initiatives in the Metropolitan Area of Barcelona in Catalonia (Spain). Through a reflexive approach, we identify a subset of 364 urban commons across social and solidarity economy practices, social innovation, and peer-to-peer production, and analyze their prosumer approach, transformative socio-environmental orientation, and capacity to provide alternatives to the state and/or the market. We conclude by arguing for the need to move beyond the municipal scale to address complex and interconnected territorial processes of emergence in social movements and policies for urban commons.

Research on the synergizing the reduction of pollution and carbon emissions of new-type urbanization

Abstract Based on the data of 282 Chinese cities from 2011 to 2018, this article analyzes and explains the synergizing the reduction of pollution and carbon emissions (PCE) of new-type urbanization (NTU) by employing the Differences-in-Differences (DID) model. The results exhibit that the NTU has a significant promoting effect on reducing atmospheric pollutant emissions and improving carbon emission efficiency, demonstrating significant synergistic effects in reducing PCE. This effect varies greatly due to differences in city areas, urban-rural income gap and city scale. Mechanism analysis shows that the NTU mainly affects the synergizing the reduction of PCE by promoting land use structure adjustment and enhancing the level of green technology innovation. Further research indicates that the NTU has a great spatial spillover effect on PCE. This article provides a comprehensive exploration and evaluation of the synergizing the reduction of PCE under the NTU policy. The research findings provide policy insights for reducing air pollutants and carbon dioxide emissions, as well as for advancing the construction of NTU more effectively.

City-scale industrial tank detection using multi-source spatial data fusion

ABSTRACT This paper focuses on the automatic detection of industrial storage tanks in urban areas using deep learning-based algorithms. Industrial storage tanks are critical for storing raw materials, finished products, and intermediate products in industries such as petroleum, chemical, and metallurgy. However, they can leak and cause environmental damage, making it important to monitor them in cities. The challenge lies in the large number and dispersed distribution of these tanks throughout the city. To address this, high-resolution remote sensing images and deep learning algorithms are used to improve the accuracy of industrial storage tank detection at the city scale. We construct a city scale industrial storage tank object detection dataset using high-resolution remote sensing images and explore the effect of deep learning object detection algorithm optimization on industrial storage tank detection. Techniques including ResNet50, FPN, and dilated convolution are utilized in this work for improving the model detection accuracy. Furthermore, we construct multiple city industrial storage tank datasets based on high-resolution remote sensing images enriched by integrating point-of-interest data and land use data of each city. The integration from multiple data sources provides an accurate and efficient solution for the identification of industrial storage tanks within urban areas.

I see therefore we are: the potential for aggregating individual future visions into a collective imaginary through artificial intelligence (AI)

Purpose This analysis draws on ethnographic research in a Northern UK city where a series of engagement activities produced hand-drawn sketches about the future. This paper shows how different groups revealed contradictory aspirations for the area with growth-focused politicians and planners projecting affluent prosperity rather than the modest, family-oriented social stability sought by local people. Design/methodology/approach Reconciling multiple perspectives is the greatest challenge of civic leadership. This paper considers how the emerging potential of artificial intelligence (AI) could support a structured imagining process for masterplanners to aggregate aspirational sketches at scale and so develop a closer relationship between citizen ideas for the future and civic decision makers’ own strategies and action plans. Findings This paper argues for a collective intelligence paradigm that aggregates individual futures thinking at city scale. Urban masterplanning strategies provide an organising structure to allow a city to emerge as a flourishing of multiple aspirations all at once. Research limitations/implications Rather than having individualism or collectivism as binary alternatives, generative AI offers an intriguing process for combining individual aspirations into a pluralist endeavour. Practical implications Engaging with citizens on the ground, in their homes and community spaces is the only way to uncover what is important to them. Ethnography provides that. Social implications Although AI’s use of aggregated data is collective, intelligence comes from nuanced and ethnographic engagement with the data. Originality/value The principle of the emergent city (Symons 2017) provides a conceptual approach to amalgamating the individual with the collective.

Urban tree species classification using multisource satellite remote sensing data and street view imagery

ABSTRACT Urban tree species distribution plays an important role in supporting both the urban forest carbon stocks estimation and sustainable urban ecological planning. Practical constraints, such as high labor costs and limited data coverage, yet present a major challenge in rapid census and classification of urban tree species at city scale. This paper aims to address this requirement by developing a large-scale urban tree species classification model through the combination of Global Ecosystem Dynamics Investigation (GEDI) data, GF-5 images, GF-6 images and Street View Imagery (SVI). The model employs Random Forest (RF) algorithm and incorporates reference data from both conventional forest inventory, comprising 866 samples and a large collection of virtual tree inventory plot automatically generated from SVI, totaling 8681 samples. From the results, the model achieves an overall accuracy of 77.89% and Kappa coefficient of 0.75. The model shows better recognition for tree species with unique crown morphology and clustered distributions. The finding indicates that satellite datasets can provide statistically significant contributions to urban tree species classification. Visible light spectrum, leaf-area and tree height metrics, as well as climate and geographic location metrics, exhibit notable value in the classification. Overall, this study can provide a viable research pathway for extending urban tree species classification to a larger scale.

Linking Plant Diversity and Urban Uses at the City-Block Scale to Inform Urban Planning

A challenge for urban ecology is to reduce biotic homogenisation by promoting plant diversity from local to city scales. As ecological and social components constantly interact in cities, an urban landscape characterisation reflecting socio-spatial functioning seems essential. However, spatially explicit description of cities at a relevant scale for urban planning are uncommon in ecological studies. Here, we explored a new approach based on the city-block scale, common in urban geography and planning, to directly link urban uses and patterns of herbaceous plant communities. We characterised all city blocks of a medium-sized French city (Blois). We inventoried grassland and meadows in 129 city blocks (10% of the whole city) for seven public and private urban uses (collective housing, individual housing, industrial, public service, park, land reserve, and road verge). We measured alpha diversity, community composition, regional originality of urban uses, and beta diversity between them. Urban land reserved for future development and parks harbour unique community composition within the city. Collective and individual housings have the same average alpha diversity, but the variability in community composition was higher for individual housing blocks. School and industrial city blocks have important alpha diversity and regional originality. Road verges have the highest alpha diversity but low regional originality and many common urban and regional species. Large green spaces with original communities should be protected during urban densification. The verticalization of residential housing could be an efficient means of internal urban densification if the lowest level of management intensity is promoted to maintain diversified vegetation. Some little-studied uses (schools, industrial city blocks) present opportunities to impede urban homogenisation.

Starting from Scratch: The Articulated Development of a Smart City in Limerick, Ireland

This paper analyses the situated practice of developing a smart city in Limerick, Ireland. It maps out, at a city scale, how the development of the smart city was planned, organised, and governed, as well as ongoing challenges. It addresses two of the principal gaps in the smart city literature, namely, the scarcity of in-depth case studies based on extensive fieldwork and the shortage of studies on smart city development on brownfield sites. Source material was gathered through desk research and interviews with key stakeholders. Limerick adopted an articulated strategic approach to smart city development. The local government’s dedicated smart city unit played a vital role in planning, managing, and implementing smart city operations. The local government did not centralise the smart city development process. Over time, there has been a gradual shift towards the development of Quadruple Helix collaborations and a balance between top-down and bottom-up approaches. The paper also identifies the challenges that might restrain Limerick’s smart city ambitions. These include financial, budgetary, technological, and human resources challenges. It also identifies the challenge of digital exclusion and the need for greater citizen involvement in smart city development.

Investigating interaction between the port-city relationship and the urban road network structure: a case study of China

ABSTRACT Ports, with their transport focus, are considered as the key nodes of the global supply chain and are viewed as an important driving force for urban development. On the other side, the road network structure of cities ensures the sustainable operation and development of ports. The interaction between the port-city relationship and the urban road network structure is explored in this paper. The port-city relationship is examined using the Comprehensive Relative Concentration Index (CRCI) model and the data collected from 26 China’s coastal port cities. The impact of port-city interaction on the urban road network structure is analyzed by a series of coupling coordination degree (CCD) models based on the entropy weight method. It was found that: (1) The coordination degree of port development and city development can measure the port-city relationship; (2) The interaction between the port-city relationship and the urban road network structure can be classified into three types by comparing the port scale and the city scale, namely large, equal, and small; and (3) The urban road network structure is relatively worse for the cities with a larger port scale than the city scale and better for the cites with a smaller port scale than the city scale.

Evaluating Inequity in Access to Park-Based Physical Activity at the Sub-District Scale: A Case Study in Xianyang, China

Urban densification has heightened residents’ demand for equitable access to urban park services, particularly those that support physical activity (PA). This access is crucial for public health and sustainable urban development. We developed the Park-Based Physical Activity Composite Attractiveness Score (PCAS) to assess the supply and service equity of parks at the sub-district scale in the central city of Xianyang, western China. The average PCAS for all parks in the study area was 46, with 69% scoring below the benchmark of 60, indicating that the parks fail to meet residents’ PA needs. We found a significant imbalance in park distribution. The 14 sub-districts we measured had an average supply score of 42 and a demand score of 49, demonstrating a clustering of park services in areas with natural resources and higher socioeconomic status. While the overall park service level was found to be reasonably equitable (Gini coefficient of 0.38), a large gap in the sub-dimensional indicators point to inequities in park services. The following scenario optimization suggested that adding new parks could more effectively enhance equity and residents’ PA levels than simply increasing park facilities. Our results provide valuable insights for urban park planning and policy-making, and contribute to the development of more equitable and accessible park services to achieve public health objectives.

A Novel Spherical Shortest Path Planning Method for UAVs

As a central subdivision of the low-altitude economy industry, industrial and consumer drones have broad market application prospects and are becoming the primary focus of the low-altitude economy; however, with increasing aircraft density, effective planning of reasonable flight paths and avoiding conflicts between flight paths have become critical issues in UAV clustering. Current UAV path planning often concentrates on 2D and 3D realistic scenes, which do not meet the actual requirements of realistic spherical paths. This paper has proposed a Gradient-Based Optimization algorithm based on the State Transition function (STGBO) to address the spherical path planning problem for UAV clusters. The state transition function is applied on the scale of medium and high-dimensional cities, balancing the stability and efficiency of the algorithm. Through evolution and comparisons with many mainstream meta-heuristic algorithms, STGBO has demonstrated superior performance and effectiveness in solving Medium-Altitude Unmanned Aerial Vehicle (MUAV) path planning problems on three-dimensional spherical surfaces, contributing to the development of the low-altitude economy.

«ҚАЛАЛЫҚ ЖЫЛУ АРАЛДАРЫНЫҢ» АЛМАТЫ ҚАЛАСЫНЫҢ ДАМУЫНА ӘСЕР ЕТУІ

Global energy consumption in buildings is steadily increasing due to rapid urbanization, climate change and other driving factors. Currently, when designing buildings, insufficient attention is paid to the problem of urban heating networks and air pollution in a densely built urban environment; it is necessary to create an integrated approach to solutions for adapting people's lives. It is no longer enough to satisfy the energy needs of societies, but it is necessary to do this while meeting overreaching sustainable goals which impose ever so stricter requirements for efficiency improvements, and environmental and other impact reductions. Based on the assumption that climate conditions, building energy efficiency and urban design are strictly related to each other, many recent studies have also demonstrated the need to map building energy consumption at the city scale rather than at the building level. An urban heat island (UHI) can have a significant impact on a building's energy consumption by increasing cooling demand and decreasing heating demand. However, the effect of UHI on building energy consumption has not been well studied due to the challenges associated with quantifying the temperature change caused by HOT and estimating building energy consumption. In this research, we reviewed the existing literature to improve the understanding of the impact of HOT on building energy consumption. It was found that HOT can lead to a 19.0% increase in cooling energy consumption and an 18.7% decrease in heating energy consumption.

Short-Term Traffic Flow Prediction: Techniques and Methods

With the development of the economy, the expansion of the city scale, the increase in the population of urban residents and the increase in the per capita car ownership, the traffic congestion problem has an increasing impact on urban operation. Especially during the peak period of commuting traffic and holidays in large cities, most cities are facing the serious problem of road congestion. In order to facilitate the travel of residents and reduce air pollution, it is very important to keep the main road of the city smooth and rapid passage of vehicles. Therefore, cities should vigorously develop short-time traffic flow prediction, and accurate prediction of short-time traffic flow is the key to realizing intelligent urban traffic operation and efficient traffic flow management improvement. This paper will talk about the time series prediction model of short-time traffic flow prediction methods and advantages and disadvantages to provide auxiliary support for solving the traffic congestion problem, provide efficient travel path decision-making guidance for urban residents, facilitate the saving of urban people's travel costs, and also provide effective police deployment decision-making information for the traffic management department.

Nature-based solutions as urban adaptation to climate risk: Framework for economic evaluation as decision support tool

Integration of Nature-based solution (NBS) as adaptation in planning and policy remains a challenge due to lack of adequate information on economic feasibility. This is mainly due to non-availability of economic evaluation framework for informed decision. Present study tries to address this by examining the status of evaluation frameworks through a systematic review of peer-reviewed articles published between 2015 and 2023. Based on the synthesis of the evidence a five-step framework, exclusively for economic evaluation of NBS as urban adaptation has been developed. Using this a novel, holistic, just, equitable and inclusive cyclical decision tool has been proposed. The review confirmed the lack of economic assessment and a holistic evaluation framework. The evaluation framework has been backed by operational guidance by providing comprehensive recommendations on the methodologies, tools and techniques and indicators and metrics that can be used for execution of each step. The proposed cyclical decision tool facilitates equity and justice by having provisions for ensuring equal participation of each stakeholder in decision making. It specifically ensures incorporation of plurality of knowledge and in particular value of the ecosystem services (ESs) from NBS. The tool has applicability across the urban spatial scale in cities of developed and developing economies.