Urban Data Research Articles

Implementing A resistance-relief approach into evaluating urban disaster management capacity: A case study of Xuzhou

In the field of disaster management, urban planning is a crucial tool for reducing the vulnerability of areas prone to natural disasters. Assessing the disaster management efficacy of urban planning is essential. However, the theoretical evaluation frameworks and precise assessment methods currently available are quite limited. Therefore, this study developed a new Resistance-Relief (2Rs) approach to evaluate disaster management capabilities in different regions. Based on the “2Rs” evaluation framework, an indicator system was developed to assess disaster management capabilities at the level of urban planning and construction. On this basis, a directed weighted graph model was established to comprehensively evaluate a city's disaster resistance and disaster relief capabilities, addressing the accuracy and comprehensiveness issues commonly found in single-dimensional evaluations. Furthermore, an indicator sensitivity analysis method based on the cloud model was proposed to analyze the sensitivity of evaluation indicators and the robustness of evaluation results. Using actual urban planning and construction data, this method was applied to assess seven disaster management zones in Xuzhou City, China. The results revealed inadequacies in the city's disaster preparedness and mitigation construction, as well as key factors affecting Xuzhou's disaster management capabilities. This new method contributes to further improvements in both disaster risk management and urban disaster prevention and mitigation planning.

Enhancing Urban Data Analysis: Leveraging Graph-Based Convolutional Neural Networks for a Visual Semantic Decision Support System.

The persistent increase in the magnitude of urban data, combined with the broad range of sensors from which it derives in modern urban environments, poses issues including data integration, visualization, and optimal utilization. The successful selection of suitable locations for predetermined commercial activities and public utility services or the reuse of existing infrastructure arise as urban planning challenges to be addressed with the aid of the aforementioned data. In our previous work, we have integrated a multitude of publicly available real-world urban data in a visual semantic decision support environment, encompassing map-based data visualization with a visual query interface, while employing and comparing several classifiers for the selection of appropriate locations for establishing parking facilities. In the current work, we challenge the best representative of the previous approach, i.e., random forests, with convolutional neural networks (CNNs) in combination with a graph-based representation of the urban input data, relying on the same dataset to ensure comparability of the results. This approach has been inspired by the inherent visual nature of urban data and the increased capability of CNNs to classify image-based data. The experimental results reveal an improvement in several performance indices, implying a promising potential for this specific combination in decision support for urban planning problems.

Engaged dialogue through data: For methodological pluralism in critical urban studies

This commentary builds on Loretta Lees’ concept of engaged dialogue in critical urban studies to argue for more engaged dialogue through data. This type of dialogue would entail building critical theory from a more diverse and nuanced base of information, using mixed or plural methodologies. It could mean communicating with, about and through data with public-facing tools that incorporate participatory elements through the user interface. It would also require ongoing critical interpretation of the politics and political economy of urban data.

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

AbstractContemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

Enhancing Public Space Accessibility and Inclusivity in Residential Neighbourhoods: A Methodological Framework and Pilot Application

The paper adds to the debate on urban transformation, which plays a significant role in improving quality of urban life and liveability of public spaces, as also stated in UN Sustainable Development Goal No. 11. The research aims to test a methodology for identifying an abacus of urban regeneration interventions to enhance attractive and critical nodes through accessibility and inclusivity. The quality of the system of connections between polarities, the promotion of active mobility, zero soil consumption and the renaturalisation of urban spaces are fundamental criteria to define urban regeneration solutions. The proposed approach develops on two levels: a GIS-based spatial analysis of urban data and a participatory process. The innovative planning process is tested on the San Leonardo neighbourhood in the Municipality of Parma, Italy, which allows us to verify the potentials and limitations of the procedure, identifying future work perspectives. Moreover, the application allows us to check the replicability in other urban contexts, possibly envisaging other applications to more case studies. Our results consist of the identification of the vocations and the critical aspects of nodes and axes to regenerate urban space in medium-sized and large cities’ suburbs.

Trends in pain undertreatment among lung cancer patients at the EOL: Analysis of urban city medical insurance data in China.

Cancer-related pain is one of the common priority symptoms in advanced lung cancer patients at the end-of-life (EOL). Alleviating pain is undoubtedly a critical component of palliative care in lung cancer. Our study was initiated to examined trends in opioid prescription-level outcomes as potential indicators of undertreated pain in China. This study used data on 1330 patients diagnosed with lung cancer of urban city medical insurance in China who died between 2014 and 2017. Opioid prescription-level outcomes were determined by annual trends of the proportion of patients filling an opioid prescription, the total dose of opioids filled by decedents, and morphine milligram equivalents per day (MMED) at the EOL (defined as the 60 days before death). We further analyzed monthly changes in the number of opioid prescriptions filled, MMED, and mean daily dose of opioids per prescription (MDDP) of the last 60 days of life by year at death and age, respectively. A total of 959 patients with exact dates of death were included, with 432 cases (45.06%; 95% CI: 44.36%-45.77%) receiving at least one opioid prescription at the EOL. The declining trends were shown in the proportion of patients filling any opioid prescription, the total dose of opioids filled by decedents and MMED, with an annual decrease of 0.341% (p = 0.01), 104.23 mg (p = 0.011) and 2.84 mg (p = 0.014), respectively. Within the 31-60 days to the 0-30 days of life, the MMED declined 6.08 mg (95% CI: -7.14 to -5.03; p = 0.000351), while the number of opioid prescriptions rose 0.66 (95% CI: 0.160-1.16; p = 0.025). Like the MMED, the MDDP fell 4.11 mg (95% CI: -5.86 to -2.37; p = 0.005) within the last month before death compared to the previous month. Terminal lung cancer populations in urban China have experienced reduced access to opioids at the EOL. The clinicians did not prescribe a satisfactory dose of opioids per prescription, while the patients suffered increasing pain in the last 30 days of life. Sufficient opioid analgesic administration should be advocated for lung cancer patients during the EOL period.

A data‐driven adaptive geospatial hotspot detection approach in smart cities

AbstractHotspot detection from geo‐referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi‐density urban data. In this article, a data‐driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small‐scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo‐information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.

A survey of sustainable development of intelligent transportation system based on urban travel demand

<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

Effectiveness of comprehensive nursing intervention program (CNIP) on knowledge and practice regarding the prevention of non-insulin-dependent diabetes mellitus (NIDDM) among adults.

There are estimated 72.96 million cases of diabetes in the adult population of India. The prevalence in urban areas ranges between 10.9% and 14.2%, and the prevalence in rural India ranges between 3.0% and 7.8% among the population aged 20 years and above with a much higher prevalence among individuals aged over 50 years. This study aimed to determine the effectiveness of comprehensive nursing intervention program (CNIP) on knowledge and practice regarding the prevention of non-insulin-dependent diabetes mellitus (NIDDM) among adults. A quasi-experimental one-group pretest-posttest design was chosen for the study. Adults were selected using a simple random sampling technique by the lottery method; of 120 eligible adults, 60 were selected to participate in the study. The study was conducted in an urban community, Bangalore, and data were collected from March 25, 2022, to April 23, 2022. CNIP consisted of education, demonstration of exercise, and information booklet. The pretest and posttest were collected using the structured knowledge questionnaire for assessing the level of knowledge and non-observational checklist for assessing the level of practice among adults. Data were analyzed by descriptive and inferential statistics. There was significant improvement in the mean scores of knowledge from 52.3 to 82.2 and practice mean score from 45.5 to 68.4. The paired t-test value of knowledge was 3.88, and practice was 2.97, which was significant at P < 0.05 level. The study concluded that CNIP had a great role in increasing the level of knowledge and practice among adults in the prevention of NIDDM.

Trading our way to a greener future: how environmental rights trading reduces urban carbon emissions in China-empirical analysis based on China's emission rights trading.

Environmental rights trading (ERT) is considered one of the crucial instruments for environmental governance in China, accelerating urban low-carbon sustainable development. This study utilized the Chinese Emissions Trading System (ETS), which has been implemented most widely, longest, and most typical, to represent ERT and examine its contribution to urban carbon emission reduction. Through a quasi-natural experiment perspective, urban panel data were collected from 2003 to 2019, and a time-varying difference-in-differences model and moderating models were developed. The study indicates that ERT can significantly reduce carbon emissions in cities, with robust results. In addition, the carbon reduction effect of ERT varies by city region, type, and size. This phenomenon is more obvious in central and western cities and resource-based cities. Finally, a framework to strengthen the carbon reduction effect of ERT was creatively constructed. The strengthening effects of green innovation, industrial structure, and clean energy are evident. This helps provide evidence for ERT layout and extension, especially in countries with high carbon reduction needs such as China. ERT affects not only policymakers but also a greener future for every organization and individual in the city.

Algiers master plan, land use and forced relocation: Monitoring change with a spatial decision support system

Over the past two decades, Algerian authorities have made significant investments in the housing sector, leading to substantial changes in Algiers' urban landscape. The implementation of forced housing relocation programs, such as the Program for the Resorption of Precarious Housing (RHP), has been pivotal in transforming Algiers into the first African city without slums, recognized by the United Nations in 2016. However, the selection of receiving sites has presented challenges for the authorities, as the actual implementation deviated from the initial plans outlined in the PDAU master plan. This study focuses on examining the site selection process for relocation in Algiers and delineating the disparities between planned and implemented land mobilization. Using an integrated approach, the analysis targets an in-depth study of the PDAU documents. It draws on a comprehensive data collection process, complemented by interviews with all stakeholders which have been engaged in either in the relocation operations in Algiers or in the elaboration of the PDAU. Furthermore, the study is supported with by a large cartographic overlay of the PDAU investigated sites, which allows a better understanding of the subject. Similar studies conducted worldwide have shown that failures in master planning often result from difficulties in execution and compliance, which are common issues in developing countries. In Algiers, the slow process of land mobilization has compelled authorities to adopt unplanned alternative solutions, making land management a blind spot in the planning practice and hindering the implementation of the PDAU as well as limiting the monitoring of urban expansion and development. Additionally, the study shows that despite attempts to incorporate a Spatial Decision Support System (SDSS) within the PDAU, its execution was to a large extent clearly ineffective. The paper contends that failures in urban land management and data collection constitute weak links impeding spatial development. This has shed light on deficiencies in the cadastral system in Algiers, with ambiguities in the legal framework of land management. The study also shows that the strict adherence to master plans can be challenging when dealing with relocation operations due to various factors. Addressing these issues is crucial for the elaboration of more effective relocation programs. By learning from the gaps and weaknesses in the selection process and implementing improved strategies, Algerian authorities can optimize the outcomes of relocation projects and promote more sustainable urban development.

Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Spatial-temporal evolution characteristics and critical factors identification of urban resilience under public health emergencies

Public health emergencies have a massive impact on the safety of people's lives and property and the sustainable development of cities. Much scholarly attention has focused on the index system, measurement method, and operation mechanism of urban resilience. But those debates typically overlook the particularity of public health emergencies and the new features of urban data and dynamic intelligence. Building on this literature, this study takes 26 cities in the Yangtze River Delta Urban Agglomerations from 2000 to 2022 as research samples to scientifically propose a six-dimensional evaluation index system (economic, social, ecological, infrastructure, organizational, and institutional) by meta-analysis. This study also discusses the spatial-temporal static (dynamic) evolution law in depth based on the comprehensive weight of the CRITIC method and entropy method, and spatial Markov chain. Finally, with the help of combining the interpretative structural model and the complex network, critical factors are quantitatively identified from two aspects of node local and network global. This study proposes a novel approach to understanding urban resilience in public health emergencies. Furthermore, the evaluation index system, spatial-temporal evolution characteristics, and critical factor identification provide theoretical references and policy recommendations in developing safe development strategies for high-quality, safe, and resilient cities.

Diurnal variation in the urban thermal environment and its relationship to human activities in China: a Tencent location-based service geographic big data perspective.

The main factor of the formation and deterioration in China's urban thermal environment is human activity, which is difficult to describe and measure. A new perspective on the effect of human activity on the urban thermal environment can be obtained by examining the interaction between location-based service (LBS) data and the urban thermal environment in China. However, relevant research is still limited. In this study, we used Tencent LBS data, Terra/Aqua MODIS land surface temperature (LST) data, and land use data to investigate the relationship between LBS and the urban thermal environment, specifically the LST and surface urban heat island intensity (SUHII) across China and its provinces. Our results showed that (1) in summer, the heat island effect was an issue in 94% of the urban areas in China, which was worse during the day. The high- and low-value periods of LBS data on a given day coincided with the acquisition times of MODIS LST products during the day and at night, respectively. (2) During both the day and at night, there was a significant connection between LBS data and the urban thermal environment in China. The highest correlation coefficient (r) between LBS data and the LST could reach 0.55 (p < 0.01) at the provincial level, and the highest correlation coefficient (r) between LBS data and the SUHII could reach 0.78 (p < 0.01) at the provincial level. (3) The urban thermal environment diurnal difference and LBS data exhibited a significant relationship. The ΔLBS diurnal differences were significantly positively related to the SUHII diurnal differences in China. The overall study findings revealed that LBS data constitute an important parameter to represent the human activity intensity when investigating the formation of the urban thermal environment in China.

Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land–ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

The Impact of Climate Change and Urbanization on Groundwater Levels: A System Dynamics Model Analysis

Climate change and population growth have placed increasing stress on groundwater resources. Effective management of groundwater resources is crucial for promoting sustainable development, especially in areas heavily reliant on groundwater supply. However, sustainable groundwater management is more complex due to various factors affecting key drivers and their feedback interactions. In this regard, this study aims to examine the key driving forces and their interplay concerning fluctuations in groundwater levels within a study area in Illinois, United States, which heavily relies on groundwater for water supply. To achieve this objective, a system dynamics (SD) simulation model was developed, utilizing hydrology, climate, and urbanization data spanning from 2010 to 2020. Through calibration and validation of the SD model with historical data, the model demonstrated reliable simulation capabilities for groundwater levels. Additionally, sensitivity analysis and exploration of water resource scenarios were conducted to evaluate the critical factors influencing groundwater levels. Results revealed that population dynamics, agricultural land, and groundwater recharge were analyzed as key drivers influencing the change in groundwater level for the study area, among the climate and urbanization factors that had different contributions to the groundwater dynamics. A 10% increase in population, agricultural land, and groundwater recharge led to a decrease of approximately 5.5% and increases of 4.5% and 4.27% in groundwater levels, respectively. The results of the four-scenario analysis indicated that both climate change and urbanization exerted significant and adverse impacts on the groundwater level in the study area. These scenarios represented prominent and worst-case situations, highlighting the potential challenges and risks associated with the combination of these factors, which resulted in about a 77 mm decrease in groundwater levels by 2030. Overall, given limited data availability for SD model parameterization, the study emphasizes the need to consider the effects of both urbanization and climate change on groundwater supplies in decision-making for sustainable groundwater management, as well as the need for adequate management measures to maintain the long-term usage of groundwater resources.

Municipal and Urban Renewal Development Index System: A Data-Driven Digital Analysis Framework

Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts.

Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data

Accurately predicting air pollutants, especially in urban areas with well-defined spatial structures, is crucial. Over the past decade, machine learning techniques have been widely used to forecast urban air quality. However, traditional machine learning approaches have limitations in accuracy and interpretability for predicting pollutants. In this study, we propose a convolutional neural network (CNN) model to predict the spatial distribution of CO concentration in Nanjing urban area at 10 m resolution. Our model incorporates various factors as input, such as building height, topography, emissions, and is trained against the outputs simulated by the parallelized large-eddy simulation model (PALM). The PALM model has 48 different scenarios that varied in emissions, wind speeds, and wind directions. The results display a strong consistency between the two models. Furthermore, we evaluate the performance of our model using a 10-fold cross-validation and out-of-sample cross-validation approach. This yields a robust correlation (with both R2 > 0.8) and a low RMSE between the CO predicted by the PALM and CNN models, which demonstrates the generalization capability of our CNN model. The CNN can extract crucial features from the resulted weight contribution map. This map indicates that the CO concentration at a location is more influenced by nearby buildings and emissions than distant ones. The interpretable patterns uncovered by our model are related to neighborhood effects, wind speeds, directions, and the impact of orientation on urban CO distribution. The model also shows high prediction accuracy (R > 0.8) when applied to another city. Overall, the integration of our CNN framework with the PALM model enhances the accuracy of air quality predictions, while enabling a fluid dynamic laws interpretation, providing effective tools for air quality management.

Measuring the quality and accessibility of urban greenery using free data sources: A case study in Bratislava, Slovakia

The importance of assessing urban greenery is growing, especially in the context of global warming and the emergence of urban heat islands. The paper proposes a set of measurements to evaluate the quality and accessibility of greenery based on freely available geographic data: Sentinel-2 satellite images, Urban Atlas, and Open Street Map. The quality (or availability) of urban greenery is derived from Sentinel-2 images by using the spectral Forest Index (FI). The proposed Neighborhood Green Quality Indexes (NGQI0/30, NGQI350) evaluate greenery in the near and far surroundings of the spatial unit by averaging the FI values. The Accessibility to Green Space Index (AGSI) measures accessibility to green spaces (GS) based on the number of theoretical functional levels within reach and the average normalised distance to the nearest GS across levels. We also propose to separately assess accessibility to parks with amenities through the Accessibility to Urban Parks Index (AUPI). Such areas attract specific groups of the population, such as elderly people and mothers with children, who are among the most vulnerable during heat waves. The proposed set of measurements can be applied to a square grid and to buildings. A mutual comparison of the proposed measurements in Bratislava, the capital of Slovakia, confirmed that each measurement captures different characteristics of urban greenery. The comparison of FI with two other widely used green quality indicators showed that FI achieves the highest correlation with the land surface temperature during hot summer days. When evaluating the precision of the NGQI0/30 calculation, Sentinel-2-derived FI proved to be a sufficient substitute for high-resolution urban greenery data sources. Since the proposed measurements do not require complex calculations and hard-to-obtain data, they are easily applicable to other cities.

A Python package for the local multiscale analysis of spatial point processes (LomPy)

The consideration of multiscale characteristics has emerged as a popular component of geospatial data analysis and modeling. However, the practical implementation of such analysis tasks involves time-consuming and computationally intensive processes that require the integration of knowledge and methods from different disciplines (e.g., quantitative geography, signal processing, and natural sciences) and in which large amounts of data have to be processed. Yet, to date, there is few open-source software that enables an efficient and transparent computational workflow. This paper introduces a Python package for the local multiscale analysis of spatial point processes (LomPy). LomPy is specifically designed for processing and analyzing data that is either irregularly spaced or has large data holes over the spatial territory: a common and methodologically challenging property of geoprocessing operations. The first main function of the package computes the multiresolution quantities, while the second applies them to the extraction of fractal and multifractal features at arbitrarily “local” spatial scales. The third function extends the univariate analysis option to multivariate settings. Powerful tools for regression modeling, density estimation, and visualization are also provided. It should be emphasized that the package is efficient on both vector and raster data structures, ensuring a wide range of applicability in urban data science and beyond.