2026-08-18

7 arXiv categories· 96 journals· 2411 candidates — 15 worth your time· 107 without an open abstract

Headline

Step-by-step QGIS tutorials for mapping Sustainable Development Goal datasets

Today, together

tag shift
Human Mobility and Location-Based Analysis on 4 items, against a 26-day average of 1.0; Traffic control and management on 3 items, against a 26-day average of 0.81; Urban Design and Spatial Analysis on 3 items, against a 26-day average of 0.69
shares references
no items share references today counted over the 15 papers published above
on the same shoulders
6 of the day's papers cite Travel and the Built Environment (Reid Ewing, Robert Cervero 2010) counted over the 82 of this day's 122 named papers whose reference lists we have — standing on the same older work is not the same as being about the same thing
first appearance
American Society for Photogrammetry and Remote Sensing; CGS i3; IQ Samhällsbyggnad; and 3 more none of them on a paper we published in the 67 days before this issue

Two counts, not one counted twice: every row here but “on the same shoulders” counts the papers published above, and that one counts every paper this day named — those and the ones under Also published today, whose abstracts we could not read.

Four of today's papers carry the tag "Urban Transport and Accessibility," exploring economic distance structures in 109 U.S. cities, unequal capacities for mobility adaptation under oil price shocks, personalized interventions targeting travel and energy use after relocation, and a transit-oriented development typology along a bus rapid transit corridor. Outside this group, another paper introduces a QGIS technical supplement to reconstruct cartographic workflows for sustainable development goal maps. Additionally, a study presents a diffusion-based generative framework to simulate multi-attribute activity-travel sequences, while another paper utilizes Sentinel-2 and Landsat 8 imagery to analyze land cover and vegetation changes in the Dhaka District of Bangladesh.

codedatapublished: Abstracts of the ICA

Lily Houtman, Gareth Baldrica-Franklin, Robert E. Roth
Abstracts of the ICA

Pennsylvania State University

Geographic Information Systems Studies · Environmental and Social Impact Assessments · Land Use and Ecosystem Services · Pennsylvania State University · University of Wisconsin–Madison

Reintroducing a QGIS technical supplement for Mapping for a Sustainable World

This technical supplement (v2.0) provides step-by-step tutorials in the open-source QGIS software to reconstruct the cartographic workflows used to design three Sustainable Development Goal (SDG) maps from the United Nations and International Cartographic Association publication, Mapping for a Sustainable World.

Why it matters — It translates theoretical cartographic design principles into practical, step-by-step instructions, allowing students and practitioners with no prior GIS experience to learn how to map complex global indicator datasets.

Abstracts of the ICA · pdf · doi · code

published: Transportation Research Part C Emerging Technologies

Hong Ye, Yatao Zhang, Konrad Schindler, et al.
Transportation Research Part C Emerging Technologies · was arXiv

Lund University

Human Mobility and Location-Based Analysis · Lund University · GeoInformation (United Kingdom) · ETH Zurich · University College London · American Society for Photogrammetry and Remote Sensing

Deep generative model for human mobility behavior

The researchers developed MobilityGen, a diffusion-based generative framework that simulates multi-attribute activity-travel sequences over days to weeks. The model links behavioral attributes with environmental context to reproduce scaling laws for location visits, activity time allocation, and the joint evolution of travel mode and destination choices.

Why it matters — It provides a generative capability to simulate individual-level, context-dependent mobility patterns at large spatial scales, enabling fine-grained analyses of urban space access, co-presence dynamics, and social segregation.

Transportation Research Part C Emerging Technologies · doi

published: Transportation

Tanja Scheffler, Christian A. Klöckner, Eva Heinen
Transportation

Norwegian University of Science and Technology

Urban Transport and Accessibility · Place Attachment and Urban Studies · Environmental Education and Sustainability · energy consumption data · Norwegian University of Science and Technology · Institute for Biomedical Engineering

Targeting travel and energy use after relocation: effects of a personalised intervention moderated by relocation disruptiveness

A randomized controlled trial tested a personalized, web-based carbon footprint feedback intervention on 212 recent movers in Norway three months post-relocation. The intervention reduced weekly car trips by 20% and overnight car trips by 32% compared to a control group, though it had no significant effect on flight frequency or electricity consumption. The reduction in weekly car use was strongest among participants moving from apartments to houses, while those moving from houses to apartments showed small, short-lived effects, and those moving between the same dwelling types showed no change.

Why it matters — The study provides empirical evidence for the habit discontinuity hypothesis, demonstrating that residential relocation creates a window of opportunity where targeted interventions can successfully curb car use. It reveals that the effectiveness of such interventions depends heavily on the direction of the housing transition, showing they can help maintain low-car habits even when individuals move into more car-dependent suburban housing types.

Caveat: The study relies on a relatively small sample size of 212 participants in a single national context, and the observed behavioral changes did not extend to non-travel energy domains like household electricity use.

Transportation · pdf · doi

published: Discover Cities

Akbar Hamidi, Zahra Rasoulzade, Abolfazl Ghanbari, et al.
Discover Cities

University of Tehran

Urban Transport and Accessibility · Transportation Planning and Optimization · Urban Design and Spatial Analysis · spatial analysis · land use data · University of Tehran

Typology of transit-oriented development to promote sustainable urban structure

This study analyzed the spatial characteristics of Transit-Oriented Development (TOD) along the Bus Rapid Transit (BRT) corridor in Tabriz, Iran, using a 600-meter buffer around each station. Evaluating four dimensions—density, diversity, design, and distance to transit—via ArcGIS and K-means clustering, the researchers identified four distinct TOD typologies ranging from highly active, mixed-use zones to areas completely lacking TOD characteristics.

Why it matters — It provides a localized empirical classification of transit-land use integration in a rapidly growing Middle Eastern metropolitan area, identifying specific underperforming zones that are ripe for targeted redevelopment.

Caveat: The analysis is restricted to a single transit corridor in one city, which may limit the direct applicability of the specific typologies to other urban contexts.

Discover Cities · pdf · doi

preprint

Muhammad Masud Tarek, Md. Alamgir Hossain, Md. Samiul Islam, et al.
arXiv cs.LG

Land Use and Ecosystem Services · Remote Sensing in Agriculture · Remote-Sensing Image Classification · random forest · satellite imagery · geemap

Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh

This study analyzed land cover and vegetation changes in the Dhaka District of Bangladesh between 2019 and 2024 using Sentinel-2 and Landsat 8 satellite imagery. Using Google Earth Engine, the researchers compared Decision Tree, K-Nearest Neighbors, and Random Forest classifiers to map land cover, finding that Random Forest achieved the highest accuracy. The analysis revealed a 59.5% expansion in urban built-up areas alongside declines of 8.46% in vegetation and 7.77% in water bodies over the five-year period.

Why it matters — It provides precise, quantified evidence of rapid ecological degradation and urban sprawl in Dhaka, establishing a machine learning workflow that can be used for timely land-use monitoring and policy enforcement.

Caveat: The analysis is restricted to a single administrative district, and the abstract does not disclose the specific accuracy metrics or kappa statistics achieved by the preferred Random Forest model.

arXiv · pdf

preprint

Zihao Zhang, Yuanbo Zhang, Xiaolei Ma, et al.
arXiv stat.AP

Global Energy and Sustainability Research · Atmospheric and Environmental Gas Dynamics · Urban Transport and Accessibility · causal inference · China · United States

Oil price shocks reveal unequal capacities for mobility adaptation

Using the 2026 US-Iran oil shock as a natural experiment, this study analyzed 1.7 trillion point-of-interest visits across 122,000 neighborhoods in China and the United States using a hierarchical panel regression discontinuity design. The analysis found that mobility range contracted in nearly 75% of neighborhoods, with the magnitude of decline driven primarily by exposure to energy-intensive travel, baseline travel distance, and car dependence.

Why it matters — The study demonstrates that fuel-price spikes function as urban stress tests, exposing structural inequalities in how different neighborhoods adapt to rising travel costs, where some communities absorb the financial burden while others are locked into rigid travel patterns they cannot reorganize.

arXiv · pdf

preprint

Emilienne Lardy, Eric Ballot, Mariam Lafkihi
arXiv stat.AP

CGS i3

Urban and Freight Transport Logistics · Maritime Ports and Logistics · Traffic control and management · statistical modeling · IQ Samhällsbyggnad

Urban logistics dynamics: a user-centric approach to traffic modelling and kinetic parameter analysis

This study models urban logistics traffic dynamics from a vehicle-centric perspective by analyzing high-frequency speed data to characterize driving cycles. Using the Art.Kinema framework, the researchers applied Factor Analysis and Generalized Linear Models to predict vehicle kinetic parameters based on exogenous contextual factors including time, day, road type, orientation, slope, and weather conditions.

Why it matters — It provides an open, transparent alternative to proprietary 'black box' routing APIs, allowing supply chain managers to estimate energy consumption and trip durations using accessible environmental and temporal variables.

arXiv · pdf

preprint

Md Rafid Islam, Rafsan Jany, Md. Siyam Bhuiyan, et al.
arXiv physics.soc-ph

Atmospheric chemistry and aerosols · Air Quality and Health Impacts · COVID-19 impact on air quality

Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes

This study analyzed 25 years of hourly air quality data from 2000 to 2025, comprising over 3.19 million records across eight pollutants. The monitoring network expanded from a single station in Dhaka to cover 103 cities by 2022, revealing a highly seasonal AQI that peaks in January at 162.26 and drops to 61.47 in July. K-means clustering identified four distinct regional pollution regimes, highlighting a rapidly worsening Dhaka-Narsingdi cluster that increases by an average of 1.84 AQI annually, with PM2.5 and PM10 driving the overall variation.

Why it matters — It establishes the first long-term, multi-city baseline of air quality dynamics across Bangladesh, demonstrating that a seemingly flat national pollution trend is actually an artifact of network expansion masking severe, localized degradation in the capital region.

Caveat: The apparent lack of a long-term trend in the composite national AQI is biased by the historical expansion of the monitoring network from one station to 103 stations over the study period.

arXiv · pdf

preprint

Hossein Z. Saghazadeh, Yonas Ayalew, Reza Ahmari, et al.
arXiv cs.AI

Air Traffic Management and Optimization · Transportation and Mobility Innovations · Aviation Industry Analysis and Trends

Demand-Driven Vertiport Siting and Discrete-Event Fleet Simulation for On-Demand Urban Air Mobility Network Design

This paper develops a demand-driven framework for designing urban air mobility networks by combining K-means clustering for vertiport siting with a discrete-event simulation of eVTOL fleet operations. Tested on commuter and passenger activity data in the Greater Los Angeles area, the model simulates multi-vehicle dispatch, battery swaps, and deadhead relocation, scaling from a network of four stations and four vehicles up to sixteen stations and twelve vehicles under high demand.

Why it matters — It demonstrates that while larger eVTOL fleets improve arrival regularity and completion times, they cannot eliminate deadhead flights caused by spatial demand imbalances, showing that urban air mobility is only time-competitive for long or highly congested trips where non-flight processing times do not dilute the speed advantage.

Caveat: The findings are based on a simulated network in a single metropolitan area using a point-mass performance model, which may not capture the micro-scale weather or airspace constraints of actual operations.

arXiv · pdf

preprint

Charitha Nandepu, Lohitha Kalepu, Gabriele Ciavarella, et al.
arXiv eess.SY

Transportation Planning and Optimization · Infrastructure Maintenance and Monitoring · Traffic control and management

Optimal Scheduling of Road Maintenance Jobs Considering Impact on Traffic Flows

This study develops data-driven surrogate models to approximate equilibrium arc flows directly from origin-destination demand, bypassing the computationally expensive repeated solutions of traditional traffic assignment models during road capacity reductions. The surrogate models use optimization-based equilibrium solutions as ground truth and were validated using a real-world case study of traffic data from the Newark, New Jersey area.

Why it matters — It provides a computationally scalable alternative for evaluating traffic flows under capacity constraints, making it feasible to embed equilibrium traffic assignment directly into network-level road maintenance scheduling frameworks.

Caveat: The surrogate model's performance and scalability were demonstrated only on a single case study in the Newark area.

arXiv · pdf

preprint

Wei-Peng Nie, Xiao-Yong Yan, Bin Jia, et al.
arXiv physics.soc-ph

Human Mobility and Location-Based Analysis · Urban Transport and Accessibility · Urban Design and Spatial Analysis · United States

Economic Distance Structures Urban Mobility in 109 U.S. Cities

Using large-scale mobility records across 109 U.S. cities, this study analyzes urban flows through the lens of economic distance, defined as the income gap between a trip's origin and destination. The analysis reveals a universal structural boundary where daily flows concentrate within a narrow economic distance of 0.25 quantiles. Gravity modeling demonstrates that this boundary is shaped both by physical residential clustering and an independent economic-distance friction, resulting in four distinct mobility regimes.

Why it matters — The study establishes that urban mobility is constrained by a universal, asymmetric economic ceiling, proving that upward mobility into wealthier areas faces a uniform structural barrier across all studied cities while downward mobility varies by location.

Caveat: The findings rely on mobile device records as a proxy for physical mobility, which may underrepresent populations without smartphones or those with different device usage patterns.

arXiv · pdf

preprint

Piotr C. Kaminski
arXiv physics.soc-ph

Urban Design and Spatial Analysis · Human Mobility and Location-Based Analysis · Data Visualization and Analytics · network analysis · openstreetmap

Spectral Fingerprints of Street-Network Morphology: A Size-Adjusted Graph-Laplacian Descriptor of Urban Fabric

The study introduces a size-adjusted graph-Laplacian descriptor to encode local street-network morphology into a compact, fixed-dimensional spectral fingerprint. Evaluated on the street network of Poznan, Poland using 1,908 continuity-based strokes, the method derives two size-corrected scalar metrics: the Mesh Index (which correlates with OpenStreetMap functional diversity at r ~ 0.19) and the Connectivity Resilience Index. The resulting Mesh Index is nearly orthogonal to classical space-syntax integration (r = 0.06), demonstrating that it captures distinct structural information.

Why it matters — It provides a machine-learning-ready representation of urban fabric that distinguishes between different morphological tissue types without supervision, overcoming a major limitation where streets with identical centrality metrics are falsely assumed to have identical surrounding local structures.

Caveat: The descriptor is designed to characterize the types of activities a street's spatial position affords, rather than predicting economic values like property prices.

arXiv · pdf

preprint

Peilin Li, Pengfei Chen, Jingyu Wang, et al.
arXiv cs.CV

Automated Road and Building Extraction · Urban Green Space and Health · Urban Transport and Accessibility · street view imagery · satellite imagery · Singapore

Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context

The researchers developed CVLNet, a cross-view learning network that predicts five dimensions of street-level human perception using AlphaEarth satellite embeddings and urban contextual data, eliminating the need for street-view imagery (SVI) during inference. Evaluated across Singapore, Kuala Lumpur, Jakarta, and Manila, the model achieved a median road-segment-level Adjusted R² of 0.76, outperforming baseline models by 5.9% to 11.3%. By applying this model, the study mapped streetscape perception across 100% of the road networks in these four cities, expanding coverage from the 13% to 31% of roads that actually have SVI available.

Why it matters — It establishes that satellite remote sensing and contextual data can substitute for street-view imagery to map subjective human experiences of the built environment. This allows planners to assess environmental exposure inequalities across entire metropolitan road networks, even in regions where street-level photography is sparse or outdated.

Caveat: The model relies on labels generated by a pretrained SVI-Percept model as its ground truth, meaning its predictions are subject to any biases or errors inherent in the original street-view perception model.

arXiv · pdf

preprint

Eduardo Graells-Garrido, Daniela Opitz, Francisco Rowe, et al.
arXiv cs.SI

Hate Speech and Cyberbullying Detection · Migration, Refugees, and Integration · Populism, Right-Wing Movements · spatial analysis · Santiago

Hate speech toward migrants on a citizen reporting platform concentrates in neighborhoods undergoing demographic change

The study analyzed over 550,000 geolocated reports from Chile's largest citizen reporting platform, SOSAFE, to map hate speech against migrants in Santiago. Using a fine-tuned Spanish hate speech classifier validated against human labels, the researchers found that reports mentioning migrants have a higher probability of containing hate speech. This exclusionary discourse concentrates in neighborhoods undergoing rapid demographic change where post-2010 arrivals make up more than a third of the population, rather than in established migrant enclaves.

Why it matters — It demonstrates that digital citizen reporting platforms act as sites of 'digital bordering' where exclusionary behavior is amplified, proving that anti-migrant sentiment is driven by rapid demographic transitions at a neighborhood scale rather than the mere presence of long-term migrant communities.

Caveat: The findings are based on data from a single citizen reporting application in Santiago, which may reflect the specific demographics and biases of that platform's user base.

arXiv · pdf

preprint

Davide Andrea Guastella, Eladio Montero Porras, Evangelos Pournaras, et al.
arXiv cs.AI

Traffic Prediction and Management Techniques · Traffic control and management · Human Mobility and Location-Based Analysis

Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing

The researchers developed a simulation-based data augmentation method that replaces physical traffic sensors with virtual ones at surrogate road network locations. Tested in Brussels and Namur, Belgium, using calibrated and synthetic models, the method uses a graph-search heuristic to select surrogate locations that maintain vehicle-flow continuity and traffic-metric similarity while enforcing spatial displacement. The resulting augmented datasets successfully preserved bimodal daily demand profiles and local traffic dynamics.

Why it matters — It provides a way to expand traffic sensor coverage and train machine learning models without the high deployment costs and privacy constraints of physical hardware, while ensuring models do not need to be retrained when physical infrastructure changes.

Caveat: The method's validation relies on simulated and calibrated models of only two Belgian cities, rather than direct deployment on real-world physical sensor networks.

arXiv · pdf

Still cited

Reid Ewing, Robert Cervero (2010), Travel and the Built Environment 1 of today's items cite it · 103 of 3240 in the archive stand on it

Also published today

These appeared today in journals we track. Their abstracts are not openly available, so we cannot summarise them.