Urban Mapping Research Articles

Informal public space: exploring street vending in Tehran

ABSTRACT The shaping of public spaces in global South cities generally involves informal actors and activities competing for access, use, and visibility, with street vending being one of the most prevalent forms of urban informality. Despite the crucial role of street vendors in shaping the streetscapes and promoting employment productivity, they often face little dignity and tolerance from authorities. This paper presents findings derived from a case study in Tehran, using observation, photography, fieldwork notes, archival records, and urban mapping to investigate the dynamics of street vending in relation to human/non-human entities, including functional mix, ground-floor storefronts, pedestrian flows, and gender. Empirical evidence suggests that street vendors are attracted to high volumes of pedestrian traffic, acting as external stimuli or social catalysts that impact people’s relationships, especially women’s. The paper reveals a strong link between the predominance of street vending and increased levels of women’s activities, albeit with negligible use of public space by women vendors. The paper provides empirical evidence that street vending is a male-dominated occupation in the studied area. These findings offer insights for developing context-specific, spatially grounded interventions that recognise the diversity of street vendors and enable their activities to thrive in the public realm.

Urban inundation mapping by coupling 1D − 2D models and model comparison

Urban inundation mapping is important for early warning, management, and drainage network planning. Urban flood simulations typically utilize a 1D model focused on pipe nodes, whereas 2D simulations are required over a land area. In this paper, our focus is on their coupling, so that a dynamic 1D-2D model is obtained. We couple the Storm Water Management Model (SWMM), a widely utilized urban drainage model, with 2D models to simulate urban inundation. The 1D-2D coupled inundation model uses the 1D model output, which represents the overflow volume of sparsely distributed pipe nodes. After coupling, surface inundation distribution maps are obtained. We developed a physically-based 2D overflow diffusion model (2D-DM) and compared it with two geostatistical models: Ordinary co-kriging (OCK) and Regression Kriging (RK). The study area is the central area of Haining City in China, where a severe storm occurred on August 3, 2018. The 2D-DM performed best (RMSE = 0.232), followed by RK (RMSE = 0.286), and OCK (RMSE = 0.361), but did not provide uncertainty measures, in contrast to the geostatistical models. We conclude that 1D-2D coupling was done successfully, with the different 2D models showing different qualities. To enhance urban inundation mapping, it is suggested to couple the 1D model with both physical and statistically based 2D models for a more comprehensive assessment of potential inundation areas and depths.

Estimating coverage and capacity of high frequency mobile networks in ultradense urban areas

High frequency communications (mmWave and TeraHz) in urban areas require a higher density of base stations compared to pre-5G mobile networks, but open the way to a quantum leap in increased throughput and reduced latency. However, we currently have no indication of how much we need to densify the deployment, and on the trade-off between the density of base stations and the performance improvement. This paper studies the problem of base stations placement to guarantee coverage to vehicles and pedestrians in urban areas when using high frequency communications. Our novel methodology takes advantage of vehicular traffic simulations and precise urban maps to generate a realistic demand model for vehicles and pedestrians in urban areas. We use a bounded error heuristic to find the maximal coverage that can be achieved with a given density of base stations, primarily using line-of-sight communications. We implemented the heuristic using CUDA libraries on Nvidia GPUs and evaluated the coverage in an urban area in the city of Luxembourg, for which vehicular traffic patterns are available. We focus on coverage and capacity analysis for the mmWave frequency, but the results are easily extended to TeraHz communications. Our results are the first to show that a reasonably low density (15 base stations per km2) is sufficient to provide coverage for vehicles in urban environments. However, optimizing on vehicles or on pedestrians are competing objectives: the operator needs to choose which one to target based on its business model when designing the network infrastructure. Our algorithms, code and open data can be used to perform this task and reproduce our results in different settings.

An exploratory framework for mapping, mechanism, and management of urban soundscape quality: From quietness to naturalness

BackgroundDespite growing attention from researchers and governments, challenges persist in comprehensively assessing urban sound quality by integrating both quietness and naturalness aspects. GoalsThis study aimed to develop an innovative soundscape quality index that concurrently evaluates quietness and naturalness in urban soundscapes. Our objectives included conducting urban soundscape quality mapping, analyzing influential mechanisms, and identifying priority zones for sound environment management. ApproachesWe collected sound pressure level (SPL) and raw audio data, from which we computed a normalized difference soundscape index (NDSI). With a dataset comprising 28 explanatory variables encompassing land use, built environment, vegetation characteristics, and temporal factors, we employed the random forest (RF) model to predict 10 indicators, including eight SPL-related indices, NDSI, and the QNS (quietness and naturalness soundscape) index. Crucially, we utilized SHAP (SHapley Additive exPlanations) values to interpret the RF model. FindingsSpatial variations in quietness and naturalness were evident, closely associated with road networks and vegetation, respectively, with discernible temporal variations. The top three variables influencing QNS were distance to major roads, normalized difference vegetation index (NDVI), and proportion of tree coverage. Moreover, interaction effects highlighted dual negative or synergistic promoting effects on QNS from factors such as road width, human disturbance, vegetation configurations, and land cover. Notably, these mechanisms were successfully applied to six typical tourist attractions in Xiamen city, where five types of management zones were mapped based on priority considerations of population density and soundscape quality. Interestingly, natural soundscape reserves were highly correlated with city parks, high-risk zones predominantly overlapped with road networks, and potential zones comprised inner communities between streets. SignificanceThe framework demonstrated effectiveness in mapping, exploring mechanisms, and guiding management strategies for the urban sound environment.

Urban flood susceptibility mapping using frequency ratio and multiple decision tree-based machine learning models

Urban flood susceptibility mapping using frequency ratio and multiple decision tree-based machine learning models

Spatial constraints in cellular automata-based urban growth models: A systematic comparison of classifiers and input urban maps

Spatial constraints are fundamental to integrating the spatial suitability to urbanization into Cellular Automata-based (CA) urban growth models, but there is a lack of consensus on the optimal methods for this purpose. This study compared the performance of three probabilistic classifiers to generate suitability surfaces for CA-based urban growth models: Logistic Regression using Generalized Linear Model (LR-GLM), Logistic Regression using Generalized Additive Model (LR-GAM), and Random Forest (RF). The study also evaluated the sensitivity of these classifiers to the input urban map adopted as a dependent variable. For this analysis, seven maps were tested: the historical urban map containing the entire extent of the urban footprint, and six additional maps containing only the recently urbanized areas over timeframes ranging from one year up to two decades. The comparison evaluated the goodness of fit of the suitability surfaces and the spatial accuracy of the urban growth simulations, using five large Brazilian cities as case study areas. The results revealed that the RF classifier significantly outperformed the LR-based classifiers. However, this overperformance was more prominent when incorporating the new urban cells over the last one to two decades of growth as input urban maps. In addition, the sensitivity analysis of the input urban maps emphasized the benefits of calibrating the classifier using the recently urbanized cells rather than the historical urban extent. We consistently observed these results concerning classifiers and input urban maps across all five case study areas. Thus, the RF classifier combined with a training dataset containing the newly urbanized areas over at least the last 10 years systematically resulted in the suitability surfaces with the highest predictability among all tested scenarios.

Remote Sensing-Enabled Urban Growth Simulation Overlaid with AHP-GIS-Based Urban Land Suitability for Potential Development in Mersin Metropolitan Area, Türkiye

This study delves into the integration of analytic hierarchy process (AHP) and geographic information system (GIS) techniques to identify suitable areas for urban development in six districts within the Mersin Metropolitan Area of Turkey. The specific aim is to generate an urban land use suitability map, in order to facilitate informed decision-making for urban development. Drawing on open Landsat satellite imagery and employing the random forest (RF) algorithm, the study spans a fifteen-year period, over which land use/land cover (LULC) changes are measured. Furthermore, a novel approach is introduced by incorporating the urban land use suitability map into an urban growth simulation model developed using a logistic regression (LR) algorithm. This simulation forecasts urban growth up to 2027, enabling planners to evaluate potential development areas against suitability criteria. Findings reveal spatial patterns of land suitability and projected urban growth, aiding decision-makers in selecting optimal areas for development while preserving ecological integrity. Notably, the study emphasizes the importance of considering various factors such as topography, accessibility, soil capability, and geology in urban planning processes. The results showcase significant proportions of the study area as being moderately to highly suitable for urban development, alongside notable shifts in LULC classes over the years. Additionally, the overlay analysis of simulated urban growth and land suitability maps highlights areas with contrasting suitability levels, offering valuable insights for sustainable urban growth strategies. By overlaying the urban land suitability map with a simulated LULC map for 2027, it is revealed that 2247.3 hectares of potential new urbanization areas demonstrate very high suitability for settlement, while 7440.12 hectares exhibit very low suitability. By providing a comprehensive framework for assessing urban land suitability and projecting future growth, this research offers practical implications for policymakers, urban planners, and stakeholders involved in Mersin’s development trajectory, ultimately fostering more sustainable and resilient urban landscapes.

Spatiotemporal dynamic mapping of heat exposure risk for different populations in city based on hourly multi-source data

Explicit dynamic and continuous mapping of heat exposure risk (HER) is a vital support for the long-term livability and sustainable management of cities but is rare given the current lack of precisely matching risk-component data. Here, geographic data (rooftops and elevation) and demographic data, particularly the time-matched air temperature data and mobile signaling data both covering 744 periods in one complete month, were used to develop an HER evaluation framework for urban normal heat scenarios and map hour-by-hour outdoor HER with 250 m spatial resolution across Xiamen, China. The results suggested that the mapping analysis was relevant in recognizing the spatial heterogeneity and temporal variability of HER, as well as the population differences in the HER pattern existing under specific spatiotemporal situations for various age (youth/adulthoods/retirement) and gender (male/female) groups. Notably, contributing to better comprehending the HER configuration, high-resolution spatiotemporal mapping was proved to be effective in clearly identifying the detailed time-series evolution of HER by the novel discovery that the definition of 08:00–21:00 as daytime (21:00–08:00 as nighttime) was more consistent with the actual situation of Xiamen than the definition according to sunrise/sunset. These methods and findings provide new knowledge for the zoning prevention and adaptation strategies of HER.

Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco

Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco

The Spatial Impact of Crowds in the Chilean Social Explosion: A Case Study of Postpolitical Democracy

This paper introduces an innovative methodology for estimating crowd sizes at significant events, particularly focusing on protests. This method combines aerial photography with urban mapping to enhance accuracy in measuring participant numbers. Originating from the analysis of extensive social protests in Chile, this approach was notably applied to what was reported as the largest march, where mainstream media claimed attendance exceeded one million. However, utilizing this new methodology, we recalculated the figure to be 354,167, challenging previous estimations. This research offers a reliable, repeatable tool for quantifying attendance at large gatherings, shedding light on the dynamics of public mobilization and space utilization. It provides a framework for assessing the comparative mobilization strength of various social movements and can extend to other large public events like concerts and sports, helping to gauge public engagement levels. Accurate crowd size estimations can inform policymakers, leading to more informed decisions and fostering a dialogue-based relationship between authorities and the public. By understanding protest scales and impacts, policies can be more aligned with societal needs, promoting a more inclusive and participatory democratic environment. This article, therefore, not only presents a methodological advancement but also underscores its applicability in enhancing democratic governance and public policy formulation.

A multimodal data fusion model for accurate and interpretable urban land use mapping with uncertainty analysis

Urban land use patterns can be more accurately mapped by fusing multimodal data. However, many studies only consider socioeconomic and physical attributes within land parcels, neglecting spatial interaction and uncertainty caused by multimodal data. To address these issues, we constructed a multimodal data fusion model (MDFNet) to extract natural physical, socioeconomic, and spatial connectivity ancillary information from multimodal data. We also established an uncertainty analysis framework based on a generalized additive model and learnable weight module to explain data-driven uncertainty. Shenzhen was chosen as the demonstration area. The results demonstrated the effectiveness of the proposed method, with a test accuracy of 0.882 and a Kappa of 0.858. Uncertainty analysis indicated the contributions in overall task of 0.361, 0.308, and 0.232 for remote sensing, social sensing, and taxi trajectory data, respectively. The study also illuminates the collaborative mechanism of multimodal data in various land use categories, offering an accurate and interpretable method for mapping urban distribution patterns.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Efficiency evaluation of low impact development practices on urban flood risk

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2–22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

Spatial Modelling of Urban Wind Characteristics: Review of Contributions to Sustainable Urban Development

Wind, a renewable resource with growing importance in the contemporary world, is considered a capable tool for addressing some of the problems linked with rapid urbanization, unsustainable development, and climate change. As such, understanding modelling approaches to wind characteristics in cities becomes crucial. While prior reviews delve into the advancements in reduced-scale models and computational fluid dynamics simulations, there is scant literature evaluating large-scale spatial modelling of urban wind environments. This paper aims to consolidate the understanding of spatial modelling approaches to wind characteristics in cities by conducting a systematic literature review with the PRISMA protocol to capture the contributions to sustainable urban development. The reviewed articles are categorized under two distinctive approaches: (a) studies adopting the wind morphometric approach, encompassing theoretical foundations, input factors, and computation methods and (b) studies adopting the urban climate mapping approach, centering on the amalgamation of wind with urban microclimate analysis. The findings suggest that wind morphometric methodologies hold considerable promise due to their straightforward calculations and interpretability. Nonetheless, issues related to data precision and accuracy challenge the validity of these models. This review also probes into the implications of these two distinctive approaches for urban planning and policymaking, advocating for more sustainable urban development.

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping.

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert’s opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation.

Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human–nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km2 of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m’s great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.

Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments

Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation.

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Estimating peak flow of Farahzad River in Tehran under climate change and debris flow scenarios: A novel approach and its implications for urban flood hazard mapping

Abstract Urban river flooding is a serious threat to cities that have altered their river buffer zones due to urbanization and climate change. This study aims to estimate the peak flow of urban rivers by considering the effects of climate change and debris flow on flood hazard. A novel approach is proposed that integrates hydrological, sedimentological, and hydraulic method and models to account for the influence of sediment volume, woody debris, and culvert blockage on peak flow estimation and flow parameters. The approach is applied to the Farahzad River basin in Tehran, Iran, using future data downscaled from a global climate model under the RCP8.5 scenario for 50- and 100-year return periods. The results show a significant increase in the peak flood discharge by nearly 3.2 times, the flood zone by 10–20%, the flood velocity by 15–30%, and the inundation depth by 10–40% due to climate change and debris flow scenarios. The study demonstrates the importance of considering multiple factors in estimating the peak flow of urban rivers and provides a useful tool for urban flood risk management.