A MODIS/ASTER airborne simulator (MASTER) imagery for urban heat island research

Thermal imagery is widely used to quantify land surface temperatures to monitor the spatial extent and thermal intensity of the urban heat island (UHI) effect. Previous research has applied Landsat images, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, Moderate Resolution Imaging Spectroradiometer (MODIS) images, and other coarse- to medium-resolution remotely sensed imagery to estimate surface temperature. These data are frequently correlated with vegetation, impervious surfaces, and temperature to quantify the drivers of the UHI effect. Because of the coarse- to medium-resolution of the thermal imagery, researchers are unable to correlate these temperature data with the more generally available high-resolution land cover classification, which are derived from high-resolution multispectral imagery. The development of advanced thermal sensors with very high-resolution thermal imagery such as the MODIS/ASTER airborne simulator (MASTER) has investigators quantifying the relationship between detailed land cover and land surface temperature. While this is an obvious next step, the published literature, i.e., the MASTER data, are often used to discriminate burned areas, assess fire severity, and classify urban land cover. Considerably less attention is given to use MASTER data in the UHI research. We demonstrate here that MASTER data in combination with high-resolution multispectral data has made it possible to monitor and model the relationship between temperature and detailed land cover such as building rooftops, residential street pavements, and parcel-based landscaping. Here, we report on data sources to conduct this type of UHI research and endeavor to intrigue researchers and scientists such that high-resolution airborne thermal imagery is used to further explore the UHI effect

Impact of tree locations and arrangements on outdoor microclimates and human thermal comfort in an urban residential environment

Trees serve as a valuable asset in the urban built environment. In an arid city like Phoenix, trees are one of the primary urban green infrastructures to ameliorate extreme heat stress. Because of the cost of water and space in the desert residential environment, designing the optimal tree arrangement to maximize overall thermal benefits for residential neighborhoods is important and necessary. In this research, we first simulated a real neighborhood with current tree arrangement in ENVI-met (a holistic three-dimensional model for the simulation of surface-plant-air interactions), and validated the reliability of ENVI-met models by comparing the simulated results with systematic temperature collection transects. Further, we evaluated and compared differences in outdoor microclimates and human thermal comfort by simulating different tree layouts (clustered, equal interval, or dispersed) in the same neighborhood. Tree benefits at individual building scale and neighborhood scale are also compared and discussed. Based on the simulation, an equal interval two trees arrangement provided the most microclimate and human thermal comfort benefits in the neighborhood due to the importance of shading in the hot arid desert environment, following by clustered tree arrangement without canopy overlap. These findings will help policy makers and urban planners offer better guidelines for planting and establishing residential trees to mitigate extreme heat in the hot arid residential environment

GIS and urban data science

With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research

Long-term air pollution exposure impact on COVID-19 morbidity in China

Although previous studies have proved the association between air pollution and respiratory viral infection, given the relatively short history of human infection with the severe acute respiratory syndrome coronavirus (SARS-CoV-2), the linkage between long-term air pollution exposure and the morbidity of 2019 novel coronavirus (COVID-19) pneumonia remains poorly understood. To fill this gap, this study investigates the influences of particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2) and carbon monoxide (CO) on COVID-19 incidence rate based on the prefecture-level morbidity count and air quality data in China. Annual means for ambient PM2.5, PM10, SO2, NO2, CO and O3 concentrations in each prefecture are used to estimate the population’s exposure. We leverage identical statistical methods, i.e., Spearman’s rank correlation and negative binomial regression model, to demonstrate that people who are chronically exposed to ambient air pollution are more likely to be infected by COVID-19. Our statistical analysis indicates that a 1 μg m-3 increase of PM2.5, PM10, NO2 and O3 can result in 1.95% (95% CI: 0.83 to 3.08% ), 0.55% (95% CI: -0.05 to 1.17% ), 4.63% (95% CI: 3.07 to 6.22% ) rise and 2.05% (95% CI: 0.51 to 3.59 % ) decrease of COVID-19 morbidity. However, we observe nonsignificant association with long-term SO2 and CO exposure to COVID-19 morbidity in this study. Our results’ robustness is examined based on sensitivity analyses that adjust for a wide range of confounders, including socio-economic, demographic, weather, healthcare, and mobility-related variables. We acknowledge that more laboratory results are required to prove the etiology of these associations

A Multidimensional Urban Land Cover Change Analysis in Tempe, AZ

Rapid population growth leading to significant conversion of rural to urban lands requires deep understanding on how the human population interacts with the built-environment. Our research goal is to explore methodologies on how to analyze multidimensional urban change with the consideration of time, space, and landscape patterns. Using NAIP high resolution satellite images and LIDAR data, we were able to derive land cover classification maps and normalized height difference at different time periods. Then we performed the 2D, 3D and landscape pattern change analysis for a case study area. The research results show that a combination of 2D, 3D and landscape pattern change analysis can provide a comprehensive understanding of urban change, and the results will help urban planners and decision makers to better understand the status of urban transformation and design city for the future

A geographically weighted regression approach to understanding urbanization impacts on urban warming and cooling: a case study of Las Vegas

A surface urban heat island (SUHI) effect is one of the most significant consequences of urbanization. Great progress has been made in evaluating the SUHI with cross-sectional studies performed in a number of cities across the globe. Few studies; however, have focused on the spatiotemporal changes in an area over a long period of time. Using multi-temporal remote sensing data sets, this study examined the spatiotemporal changes of the SUHI intensity in Las Vegas, Nevada, over a 15-year period from 2001 to 2016. We applied the geographically weighted regression (GWR) and advanced statistical approaches to investigating the SUHI variation in relation to several important biophysical indicators in the region. The results show that (1) Las Vegas had experienced a significant increase in the SUHI over the 15 years, (2) Vegetation and large and small water bodies in the city can help mitigate the SUHI effect and the cooling effect of vegetation had increased continuously from 2001 to 2016, (3) An urban heat sink (UHS) was identified in developed areas with low to moderate intensity, and (4) Increased surface temperatures were mainly driven by the urbanization-induced land conversions occurred over the 15 years. Findings from this study will inspire thoughts on practical guidelines for SUHI mitigation in a fast-growing desert city

Weakly-Supervised Semantic Segmentation of Airborne LiDAR Point Clouds in Hong Kong Urban Areas

Semantic segmentation of airborne LiDAR point clouds of urban areas is an essential process prior to applying LiDAR data to further applications such as 3D city modeling. Large-scale point cloud semantic segmentation is challenging in practical applications due to the massive data size and time-consuming point-wise annotation. This paper applied weakly-supervised Semantic Query Network and sparse points annotation pipeline to practical airborne LiDAR datasets for urban scene semantic segmentation in Hong Kong. The experiment result obtained the overall accuracy over 84% and the mean intersect over union over 75%. The capacity of intensity and return attributes of LiDAR data to classify the vegetation and construction was explored and discussed. This work demonstrates an efficient workflow of large-scale airborne LiDAR point cloud semantic segmentation in practice

Articulating strategies to address heat resilience using spatial optimization and temporal analysis of utility assistance data of the Salvation Army Metro Phoenix

Long-term community resilience, which privileges a long-view look at chronic, slow-moving issues affecting communities, has begun to draw more attention from researchers and policymakers. In the Valley of the Sun, resilience to heat is both a necessity and a way of life. Solutions are ubiquitous but nevertheless still in demand over the long, hot summers in the Phoenix, Arizona metropolitan area. Residents heavily rely on air conditioning (AC) for relief from heat stress, illness, and to prevent indoor heat-related deaths. However, paying for the electricity to keep homes cool can be expensive and the electric bills can be cost prohibitive for many low-income individuals and families. Local government agencies, non-governmental organizations (NGOs), and charitable organizations have programs that provide financial assistance for qualified applicants offering limited relief from electricity costs. To better understand the utility assistance landscape in the Phoenix metropolitan area as a contributor to heat resilience among vulnerable communities, we created a collaborative team of individuals from the university and the Salvation Army, one of the more than 80 organizations that provides emergency economic aid for low-income families to pay high-cost electricity bills, to articulate insights about systemic efficiencies and efficacies, from a data-informed perspective. We utilized exploratory data analysis and advanced spatial analytical methods with the Salvation Army, to build a shared understanding of knowledge gaps and verified hunches. Our collaborative research confirms that minority groups (African American and Native American) disproportionately require assistance. Meanwhile, 30% of the travel time and distance to intake interviews could be saved by switching from zip code-based assignment systems to address-based assignment systems. Budgeting across empirically identified temporal patterns of need could offer resilience benefits to the most vulnerable. As a result of this community research partnership, data from the Salvation Army reveals the character and dimension of critical challenges within the utility assistance system as a whole, informs both immediate solutions and builds a knowledge base for transforming future operations for the organization, while it shapes broader conversations across the community of service providers about heat resilience in both spatial and temporal terms

Analysis The Influencing Factors of Urban Traffic Flows by Using Emerging Urban Big Data

This research applies spatial Durbin model to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. The results show that the overall built environment within a buffer area has more significant impact on urban traffic flow compared to the nearby location within a few meters. Areas with more young and white dwellers are associated with more traffic flows. With the influence of COVID-19, residents prefer to spend their daily life in their local neighborhood rather than having long distance travel. The initial findings from this research provide evidence of developing 20-minute city via active travel for achieving net-zero carbon target

Assessing the cooling benefits of tree shade by an outdoor urban physical scale model at Tempe, AZ

Urban green infrastructure, especially shade trees, offers benefits to the urban residential environment by mitigating direct incoming solar radiation on building facades, particularly in hot settings. Understanding the impact of different tree locations and arrangements around residential properties has the potential to maximize cooling and can ultimately guide urban planners, designers, and homeowners on how to create the most sustainable urban environment. This research measures the cooling effect of tree shade on building facades through an outdoor urban physical scale model. The physical scale model is a simulated neighborhood consisting of an array of concrete cubes to represent houses with identical artificial trees. We tested and compared 10 different tree densities, locations, and arrangement scenarios in the physical scale model. The experimental results show that a single tree located at the southeast of the building can provide up to 2.3 °C hourly cooling benefits to east facade of the building. A two-tree cluster arrangement provides more cooling benefits (up to 6.6 °C hourly cooling benefits to the central facade) when trees are located near the south and southeast sides of the building. The research results confirm the cooling benefits of tree shade and the importance of wisely designing tree locations and arrangements in the built environment