Combining expert and crowd-sourced training data to map urban form and functions for the continental US

LCZ4r package R for local climate zones and urban heat islands

The LCZ4r is a novel toolkit designed to streamline Local Climate Zones (LCZ) classification and Urban Heat Island (UHI) analysis. Built on the open-source R statistical programming platform, the LCZ4r package aims to improve the usability of the LCZ framework for climate and environment researchers. The suite of LCZ4r functions is categorized into general and local functions ( https://bymaxanjos.github.io/LCZ4r/index.html ). General functions enable users to quickly extract LCZ maps for any landmass of the world at different scales, without requiring extensive GIS expertise. They also generate a series of urban canopy parameter maps, such as impervious fractions, albedo, and sky view factor, and calculate LCZ-related area fractions. Local functions require measurement data to perform advanced geostatistical analysis, including time series, thermal anomalies, air temperature interpolation, and UHI intensity. By integrating LCZ data with interpolation techniques, LCZ4r enhances air temperature modeling, capturing well-defined thermal patterns, such as vegetation-dominated areas, that traditional methods often overlook. The openly available and reproducible R-based scripts ensure consistent results and broad applicability, making LCZ4r a valuable tool for researchers studying the relationship between land use-cover and urban climates.

Integrating crowdsourced data in the built environment studies: A systematic review

The integration of crowdsourced data has become central to contemporary built environment studies, driven by the rapid growth in digital technologies and participatory approaches that characterize modern urbanism. Despite its potential, a systematic framework for its analysis remains underdeveloped. This review, conducted in accordance with the PRISMA protocol, examines the use of crowdsourced data in shaping the built environment, scrutinizing its applications, crowdsourcing techniques, methodologies, and comparison with other big data forms. From 226 relevant studies, this study uncovers the evolving thematic landscape of crowdsourced data through a longitudinal analysis (2013-2024), revealing the driving forces that have shifted its representation over time. Additionally, by examining the cultural dimensions and contextual variability, this study demonstrates how identical data is interpreted in markedly different ways across diverse geographic and social contexts. These findings underscore the inmportance of adopting context-sensitive and culturally aware approaches to effectively leverage crowdsourced data in the built environment research. The novelty of this review lies in reframing crowdsourced data not merely as an application tool but as a lens for understanding broader cultural and technological shifts, offering both theoretical and practical insights into its role in the built environment. By advancing our understanding of the unique contributions of crowdsourced data and its complementary role to other big data types, this review provides actionable recommendations for urban planners and policymakers. Ultimately, these findings promote more inclusive and sustainable urban development, fostering cities that better respond to the needs of diverse populations.

The High-resolution Urban Meteorology for Impacts Dataset (HUMID) daily for the Conterminous United States

Many current gridded surface meteorological datasets are inadequate for quantifying near-surface spatiotemporal variability because they do not fully represent the impacts of land surface heterogeneity. Of note, explicit representation of the spatial structure and magnitude of local urban warming are usually lacking. Here we enhance the representation of spatial meteorological variability over urban areas in the conterminous United States (CONUS) by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. We also synthesize in situ meteorological data including local mesonets to create a 1 km grid spacing model-observation fusion product spanning 1981-2018 over the CONUS at daily temporal resolution. Daily maximum, minimum, and mean values for a variety of temperature estimates, humidity, and surface energy budget terms, among others, are included. This High-resolution Urban Meteorology for Impacts Dataset (HUMID) will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology.

Large disagreements in estimates of urban land across scales and their implications

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts.

Assessing spatiotemporal population density dynamics from 2000 to 2020 in megacities using urban and rural morphologies

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban-rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban-rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level.

An assessment of WRF-urban schemes in simulating local meteorology for heat stress analysis in a tropical sub-Saharan African city, Lagos, Nigeria

Megacities, such as Lagos, Nigeria, face significant challenges due to rapid urbanization and climate change, resulting in a higher intensity of the urban heat island effect, coupled with high population density, making the city fall under the category of moderate to high heat stress/risk. Despite this, very few studies have analyzed the urban impact on heat stress over the coastal city, albeit with poor resolution data. In this study, we assessed the performance of an integrated high-resolution WRF-urban scheme driven by the readily available urban canopy information of the local climate zone (LCZ) to simulate local meteorological data for analyzing the spatiotemporal pattern of heat stress over the megacity. Our results show that the WRF-BEP scheme outperformed the other evaluated urban schemes, reducing the normalized root mean squared error by 25%. Furthermore, using humidex, we found a generally high incidence of intense discomfort in highly urbanized areas and noted the significant influence of urban morphology on the pattern of heat stress, particularly at night due to the combined effect of urban warming and higher relative humidity. The most socioeconomically disadvantaged urban areas, LCZ7, were most affected, with "hot" heat stress conditions observed over 90% of the time. However, during the afternoon, we found reduced heat stress in the core urban areas which might be due to the shading effect and/or cold air advection. Our findings would be relevant in the development of the urgently needed climate/heat adaptation plans for the city and other sub-Saharan African cities.

Mapping urban form into local climate zones for the continental US from 1986-2020

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people's activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling and cloud-enabled modeling - an approach that could be generalized to other countries and continents. Our model achieved good performance: 0.76 overall accuracy (0.55-0.96 class-wise F1 scores). To our knowledge, this is the first high-resolution, longitudinal LCZ map for the continental US. Our work may be useful for a variety of fields including earth system science, urban planning, and public health.

Does size matter? Modelling the cooling effect of green infrastructures in a megacity during a heat wave

The vulnerability of urban ecosystems to global climate change becomes a key issue in research and political agendas. Urban green infrastructures (UGIs) are widely considered as a nature-based solution to mitigate climate change and adapt to local urban climate anomalies in cities. However, UGI-induced cooling effect depends on the size, location and geometry of green spaces, and such dependencies remain overlooked. This research aimed to investigate the cooling effect of UGIs of different size under extreme conditions of 2021 summer heat wave for the case of Moscow megacity (Russia) using a numerical mesoclimatic model COSMO. UGIs objects were assigned to one of the four size categories (S, M, L and XL) based on their area. Their cooling effects at the local, non-local and city scales were evaluated based on comparison between the model outcomes for the realistic land cover and simulations for which UGI of a particular size category were replaced by the built-up areas typical for their surroundings. The highest cooling effect was observed for XL size UGIs, which reduced the local heat-wave-averaged air temperatures by up to 3.4 °C, whereas for the S size UGIs it did not exceed 2 °C. The cooling effectiveness for XL category was higher than for S category by 23 % inside the green spaces (locally), by 40-90 % in the buffer zones around the green space (non-locally) and by 35 % for the whole city. More effective cooling of large UGIs is partially explained by their stronger park breeze effect, i.e., impact on the airflow increasing the divergence over green spaces. However, when standardized to the population affected by cooling, the M size UGIs made the strongest contribution to the thermal environment where people live and work. The stronger non-local cooling induced by the largest UGI objects cannot compensate for their remoteness from the built environment.

Impact of different roofing mitigation strategies on near-surface temperature and energy consumption over the Chicago metropolitan area during a heatwave event

This study examined the impact of cool roofs, green roofs, and solar panel roofs on near-surface temperature and cooling energy demand through regional modeling in the Chicago metropolitan area (CMA). The new parameterization of green roofs and solar panel roofs based on model physics has recently been developed, updated, and coupled to a multilayer building energy model that is fully integrated with the Weather Research and Forecasting model. We evaluate the model performance against with observation measurements to show that our model is capable of being a suited tool to simulate the heatwave event. Next, we examine the impact by characterizing the near-surface air temperature and its diurnal cycle from experiments with and without the different rooftops. We also estimate the impact of the rooftop on the urban island intensity (UHII), surface heat flux, and the boundary layer. Finally, we measure the impact of the different rooftops on citywide air-conditioning consumption. Results show that the deployment of the cool roof can reduce the near-surface temperature most over urban areas, followed by green roof and solar panel roof. The cool roof experiment was the only one where the near-surface temperature trended down as the urban fraction increased, indicating the cool roof is the most effective mitigation strategy among these three rooftop options. For cooling energy consumption, it can be reduced by 16.6 %, 14.0 %, and 7.6 %, when cool roofs, green roofs, and solar panel roofs are deployed, respectively. Although solar panel roofs show the smallest reduction in energy consumption, if we assume that all electricity production can be applied to cooling demand, we can expect almost a savings of almost half (46.7 %) on cooling energy demand.

The extreme heat wave of July-August 2021 in the Athens urban area (Greece): Atmospheric and human-biometeorological analysis exploiting ultra-high resolution numerical modeling and the local climate zone framework

Greece was affected by a prolonged and extreme heat wave (HW) event (July 28-August 05) during the abnormally hot summer of 2021, with the maximum temperature in Athens, the capital of the country, reaching up to 43.9 °C in the city center. This observation corresponds to the second highest maximum temperature recorded since 1900, based on the historical temperature time series of the National Observatory of Athens weather station at Thissio. In the present study, a multi-scale numerical modeling system is used to analyze the urban climate and thermal bioclimate in the Athens urban area (AUA) in the course of the HW event, as well as during 3 days prior to the heat wave and 3 days after the episode. The system consists of the Weather Research and Forecasting model, the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model) and the human-biometeorological model RayMan Pro, and incorporates the local climate zone (LCZ) classification scheme. The system's validation results demonstrated a robust modeling set-up, characterized by high capability in capturing the observed magnitude and diurnal variation of the urban meteorological and heat stress conditions. The analysis of two- and three-dimensional fields of near-surface air temperature, humidity and wind unraveled the interplay of geographical factors (surface relief and proximity to the sea), background atmospheric circulations (Etesians and sea breeze) and HW-related synoptic forcing with the AUA's urban form. These interactions had a significant impact on the LCZs heat stress responsiveness, expressed using the modified physiologically equivalent temperature (mPET), between different regions of the study area, as well as at inter- and intra-LCZ level (statistically significant differences at 95 % confidence interval), providing thus, urban design and health-related implications that can be exploited in human thermal discomfort mitigation strategies in AUA.

Gridded land use data for the conterminous United States 1940-2015

Multiple aspects of our society are reflected in how we have transformed land through time. However, limited availability of historical-spatial data at fine granularity have hindered our ability to advance our understanding of the ways in which land was developed over the long-term. Using a proprietary, national housing and property database, which is a result of large-scale, industry-fuelled data harmonization efforts, we created publicly available sequences of gridded surfaces that describe built land use progression in the conterminous United States at fine spatial (i.e., 250 m × 250 m) and temporal resolution (i.e., 1 year - 5 years) between the years 1940 and 2015. There are six land use classes represented in the data product: agricultural, commercial, industrial, residential-owned, residential-income, and recreational facilities, as well as complimentary uncertainty layers informing the users about quantifiable components of data uncertainty. The datasets are part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) and enable the creation of new knowledge of long-term land use dynamics, opening novel avenues of inquiry across multiple fields of study.

Sentinel-Based Adaptation of the Local Climate Zones Framework to a South African Context

The LCZ framework has become a widely applied approach to study urban climate. The standard LCZ typology is highly specific when applied to western urban areas but generic in some African cities. We tested the generic nature of the standard typology by taking a two-part approach. First, we applied a single-source WUDAPT-based training input across three urban areas that represent a gradient in South African urbanization (Cape Town, Thohoyandou and East London). Second, we applied a local customized training that accounts for the unique characteristics of the specific area. The LCZ classification was completed using a random forest classifier on a subset of single (SI) and multitemporal (MT) Sentinel 2 imagery. The results show an increase in overall classification accuracy between 17 and 30% for the locally calibrated over the generic standard LCZ framework. The spring season is the best classified of the single-date imagery with the accuracies 7% higher than the least classified season. The multi-date classification accuracy is 13% higher than spring but only 9% higher when a neighborhood function (NF) is applied. For acceptable performance of the LCZ classifier in an African context, the training must be local and customized to the uniqueness of that specific area.

Patch-Based Local Climate Zones Mapping and Population Distribution Pattern in Provincial Capital Cities of China

Accurate urban morphology provided by Local Climate Zones (LCZ), a universal surface classification scheme, offers opportunities for studies of urban heat risk, urban ventilation, and transport planning. In recent years, researchers have attempted to generate LCZ maps worldwide with the World Urban Database and Access Portal Tools (WUDAPT). However, the accuracy of LCZ mapping is not satisfactory and cannot fulfill the quality demands of practical usage. Here, we constructed a high-quality sample dataset from Chinese cities and presented a patch-based classification framework that employs chessboard segmentation and multi-seasonal images for LCZ mapping. Compared with the latest WUDAPT method, the overall accuracy for all LCZ types (OA) and urban LCZ types (OAu) of our framework increased by about 10% and 9%, respectively. Furthermore, based on the analysis of population distribution, we first gave the population density of different built-up LCZs of Chinese cities and found a hierarchical effect of population density among built-up LCZs in different size cities. In summary, this study could serve as a valuable reference for producing high-quality LCZ maps and understanding population distribution patterns in built-up LCZ types.

Inference of Local Climate Zones from GIS Data, and Comparison to WUDAPT Classification and Custom-Fit Clusters

A GIS-based approach is used in this study to obtain a better LCZ map of Berlin in comparison to the remote-sensing-based WUDAPT L0 approach. The LCZ classification of land use/cover can be used, among other applications, to characterize the urban heat island. An improved fuzzy logic method is employed for the purpose of classification of the zone properties to yield the GIS-LCZ map over 100 m × 100 m grid tiles covering the Berlin region. The zone properties are calculated from raster and vector datasets with the aids of the urban multi-scale environmental predictor (UMEP), QGIS and Python scripts. The standard framework is modified by reducing the threshold for the zone property impervious fraction for LCZ E to better detect paved surfaces in urban areas. Another modification is the reduction in the window size in the majority filter during post-processing, compared to the WUDAPT L0 method, to retain more details in the GIS-LCZ map. Moreover, new training areas are generated considering building height information. The result of the GIS-LCZ approach is compared to the new training areas for accuracy assessment, which shows better overall accuracy compared to that of the WUDAPT L0 method. The new training areas are also submitted to the LCZ generator and the resulting LCZ-map gives a better overall accuracy value compared to the previous (WUDAPT) submission. This study shows one shortcoming of the WUDAPT L0 method: it does not explicitly use building height information and that leads to misclassification of LCZs in several cases. The GIS-LCZ method addresses this shortcoming effectively. Finally, an unsupervised machine learning method, k-means clustering, is applied to cluster the grid tiles according to their zone properties into custom classes. The custom clusters are compared to the GIS-LCZ classes and the results indicate that k-means clustering can identify more complex city-specific classes or LCZ transition types, while the GIS-LCZ method always divides regions into the standard LCZ classes.

Simulation of the Air Quality in Southern California, USA in July and October of the Year 2018

A numerical investigation of the air quality in Southern California, USA in the year 2018 is presented using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). In July, a heat wave occurred, and in October, Santa Ana conditions prevailed; these conditions and their impact on air quality are the scope of the present numerical study.The high spatial resolution in the simulation includes two nested domains of 1 km and 3 km, respectively. Local climate zones land use categories are combined with the complex urban model building effect parameterization coupled with the building energy model (BEP+BEM) and the detailed MOZCART-T1 chemical reaction mechanism, which is the MOZART-T1 mechanism for trace gases with GOCART aerosols. Thus, the model is suitable to compare simulation results to in situ and satellite measurements of O3, NO2, CH4, and CO. The meteorology is captured well by the model. Comparison of simulation results with observations shows a good agreement of NO2 and ozone, whereas CO mixing ratios are generally underestimated. This hints at missing emissions in the 2017 National Emissions Inventory (NEI) dataset. Both the heat wave and the Santa Ana winds increase the air pollution with gas-phase species in Los Angeles. In both cases, nighttime boundary layer heights are small, which causes emissions to reside near the ground. During Santa Ana winds, NOx removal on aerosols is reduced. Methane mixing ratios are modeled very well at most stations in Los Angeles, but predictions of low emissions near the University of California cause inaccuracies at that location. Modeled and observed PM2.5 agree well on low-pollution days, but high-pollution events are generally missed by the model. During the heat wave, both modeled and observed PM2.5 concentrations exceed the recommended NAAQS National Ambient Air Quality Standards value of 12.5 g/m3. The present modeling approach serves as a base for the study and prediction of special weather events and their impact on air pollution.

Hierarchical Urban Land Mappings and Their Distribution with Physical Medium Environments Using Time Series of Land Resource Images in Beijing, China (1981–2021)

Rapid urban expansion and structural changes are taking place in China’s capital city, Beijing, but without an update of urban land features in a timely manner our understanding of the new urban heterogeneity is restricted, as land-background data is indispensable for bio-geophysical and bio-geochemical processes. In this plain region, the investigations of multi-scale urban land mappings and physical medium environmental elements such as slope, aspect, and water resource services are still lacking, although Beijing can provide an exemplary case for urban development and natural environments in plains considering the strategic function of China’s capital city. To elucidate these issues, a remote-sensing methodology of hierarchical urban land mapping was established to obtain the urban land, covering structure and its sub-pixel component with an overall accuracy of over 90.60%. During 1981–2021, intense and sustained urban land expansion increased from 467.13 km2 to 2581.05 km2 in Beijing, along with a total growth rate of 452.53%. For intra-urban land structures, a sharp growth rate of over 650.00% (i.e., +1649.54 km2) occurred in terms of impervious surface area (ISA), but a greening city was still evidently observed, with a vegetation-coverage rate of 8.43% and 28.42% in old and newly expanded urban regions, respectively, with a more integrative urban ecological landscape (Shannon’s Diversity Index (SHDI) = −0.164, Patch Density (PD) = −8.305). We also observed a lower rate of ISA (0.637 vs. 0.659) and a higher rate of vegetation cover (0.284 vs. 0.211) in new compared to old urban regions, displaying a higher quality of life during urban expansion. Furthermore, the dominant aspect of low, medium, and high density ISA was captured with the north–south orientation, considering the sunlight conditions and traditional house construction customs in North China, Over 92.00% of the ISA was distributed in flat environment regions with a slope of less than 15°. When the water-resource service radius shifted from 0.5 km to 0.5–1 km and 1–2 km, high density vegetation displayed a dependence on water resources. Our results provide a new survey of the evolution of hierarchical urban land mapping during 1981–2021 and reveals the relationship with physical medium environments, providing an important reference for relevant research.

Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO₂, PM_2.5, O₃, CO, PM₁₀, SO₂) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R² increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R² = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

Urban-Scale Evaluation of Cool Pavement Impacts on the Urban Heat Island Effect and Climate Change

We implemented a context-sensitive and prospective framework to assess the global warming potential (GWP) impacts of cool pavement strategies on specific roads for different cities. The approach incorporates several interconnections among different elements of the built environment, such as buildings and urban road segments, as well as the transportation fleet, using specific building and pavement information from an urban area. We show that increasing pavement albedo lowers urban air temperatures but can adversely affect the building energy demand in the areas with high incident radiation exposure. The heating energy savings and the radiative forcing effect improve the GWP savings in cold and humid climate conditions. The total GWP savings intensity is sensitive to the city morphology and road traffic. The probabilistic results show that cool pavement strategies can offset 1.0-3.0% and 0.7-6.0% of the total GHG emissions of the U.S. cities Boston and Phoenix, respectively, for a 50-year analysis period. The worldwide range of savings can be as large as 5.0-44.7 Gt of CO₂ eq. A paradigm shift in pavement strategy selection is required in most neighborhoods.

Development of a Web GIS for small-scale detection and analysis of COVID-19 (SARS-CoV-2) cases based on volunteered geographic information for the city of Cologne, Germany, in July/August 2020

BACKGROUND

Various applications have been developed worldwide to contain and to combat the coronavirus disease-19 (COVID-19) pandemic. In this context, spatial information is always of great significance. The aim of this study is to describe the development of a Web GIS based on open source products for the collection and analysis of COVID-19 cases and its feasibility in terms of technical implementation and data protection.

METHODS

With the help of this Web GIS, data on this issue were collected voluntarily from the Cologne area. Using house perimeters as a data basis, it was possible to check, in conjunction with the Official Topographic Cartographic Information System object type catalog, whether buildings with certain functions, for example residential building with trade and services, have been visited more frequently by infected persons than other types of buildings. In this context, data protection and ethical and legal issues were considered.

RESULTS

The results of this study show that the development of a Web GIS for the generation and evaluation of volunteered geographic information (VGI) with the help of open source software is possible. Furthermore, there are numerous data protection and ethical and legal aspects to consider, which not only affect VGI per se but also affect IT security.

CONCLUSIONS

From a data protection perspective, more attention needs to be paid to the intervention and post-processing of data. In addition, official data must always be used as a reference for the actual spatial consideration of the number of infections. However, VGI provides added value at a small-scale level, so that valid information can also be reliably derived in the context of health issues. The creation of guidelines for the consideration of data protection, ethical aspects, and legal requirements in the context of VGI-based applications must also be considered. Trial registration The article does not report the results of a health care intervention for human participants.

A Method for Modeling Urban Water Infrastructures Combining Geo-Referenced Data

Water distribution networks are the backbone of any municipal water supply. Their task is to supply the population regardless of the respective demand. High resilience of these infrastructures is of great importance and has brought these infrastructures into the focus of science and politics. At the same time, the data collected is highly sensitive and often openly unavailable. Therefore, researchers have to rely on models that represent the topology of these infrastructures. In this work, a model is developed that allows the topology of an urban water infrastructure to be mapped using the example of Cologne, Germany by combining freely available data. On the one hand, spatial data on land use (local climate zones) are used to disaggregate the water demand within the city under consideration. On the other hand, the parallelism of water and urban transportation infrastructures is used to identify the topology of a network by applying optimization methods. These networks can be analyzed to identify vulnerable areas within urban structures.

Evaluating the Urban Canopy Scheme TERRA_URB in the COSMO Model for Selected European Cities

The increase in built surfaces constitutes the main reason for the formation of the Urban Heat Island (UHI), that is a metropolitan area significantly warmer than its surrounding rural areas. The urban heat islands and other urban-induced climate feedbacks may amplify heat stress and urban flooding under climate change and therefore to predict them correctly has become essential. Currently in the COSMO model, cities are represented by natural land surfaces with an increased surface roughness length and a reduced vegetation cover, but this approach is unable to correctly reproduce the UHI effect. By increasing the model resolution, a representation of the main physical processes that characterize the urban local meteorology should be addressed, in order to better forecast temperature, moisture and precipitation in urban environments. Within the COSMO Consortium a bulk parameterization scheme (TERRA_URB or TU) has been developed. It parametrizes the effects of buildings, streets and other man-made impervious surfaces on energy, moist and momentum exchanges between the surface and atmosphere, and additionally accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU implements an impervious water-storage parameterization, and the Semi-empirical Urban canopy parametrization (SURY) that translates 3D urban canopy into bulk parameters. This paper presents evaluation results of the TU scheme in high-resolution simulations with a recent COSMO model version for selected European cities, namely Turin, Naples and Moscow. The key conclusion of the work is that the TU scheme in the COSMO model reasonably reproduces UHI effect and improves air temperature forecasts for all the investigated urban areas, despite each city has very different morphological characteristics. Our results highlight potential benefits of a new turbulence scheme and the representation of skin-layer temperature (for vegetation) in the model performance. Our model framework provides perspectives for enhancing urban climate modelling, although further investigations in improving model parametrizations, calibration and the use of more realistic urban canopy parameters are needed.

Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?

Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (Pf PR_2-10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling Pf PR_2-10. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower Pf PR_2-10 (5%-30%) than rural areas (15%-40%). The Pf PR_2-10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher Pf PR_2-10. Informal settlements-represented by the LCZ 7 (lightweight lowrise)-have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies.

Impact of Urban Canopy Parameters on a Megacity’s Modelled Thermal Environment

Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations.