Study on the development characteristics and spatial and temporal patterns of population ageing in 31 central cities in China

Background

Population ageing is inseparable from technological innovation, social progress and the development of human civilization, and constitutes a new element in the development of contemporary human history.

Objective

To dynamically analyses the developmental, structural and growth characteristics of population ageing in 31 provincial capitals and municipalities in China, using the data of the fifth national census in 2000 and the seventh national census in 2020.

Methods

The development characteristics and spatial and temporal patterns of population aging in the 31 cities were measured using the population aging index growth model, Theil's index, coefficient of variation, population aging index and other analytical methods.

Results

(1) From 2000 to 2020, the population aging rate of the 31 central cities generally increased, and the population aging level of the cities showed the characteristics of "East-Central-Northeast-West" to "Northeast-East-Central-West" decreasing. (2) Regional differences in the ratio of old to young are relatively high, while regional differences in the level of population ageing are relatively small. The level of population ageing is classified with the indicators of size structure, family structure and age structure in the first and third quadrants, and with the geographic concentration rate in the second and fourth quadrants. (3) China's population ageing has a T-shaped spatial distribution characteristic pointing along the coast - along the Yangtze Rivers.

Conclusion

The 31 central cities are the center of gravity of China's economy and have strong economic power in dealing with the challenges of population ageing, but how to make population ageing compatible with the economy and society, and then promote sustainable population development, is a topic that needs further attention in the study.

Making cities mental health friendly for adolescents and young adults

Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health1,2. Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities3. Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories4. We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people's ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

Ethnic disparities in cancer mortality in the capital and northeast of the State of São Paulo, Brazil 2001-17

PURPOSE

There is a paucity of studies investigating cancer disparities in groups defined by ethnicity in transitioning economies. We examined the influence of ethnicity on mortality for the leading cancer types in São Paulo, Brazil, comparing patterns in the capital and the northeast of the state.

METHODS

Cancer deaths were obtained from a Brazilian public government database for the Barretos region (2003-2017) and the municipality of São Paulo (2001-2015). Age-standardized rates (ASR) per 100,000 persons-years, by cancer type and sex, for five self-declared racial classifications (white, black, eastern origin (Asian), mixed ethnicity (pardo), and indigenous Brazilians), were calculated using the world standard population.

RESULTS

Black Brazilians had higher mortality rates for most common cancer types in Barretos, whereas in São Paulo, white Brazilians had higher rates of mortality from breast, colorectal, and lung cancer. In both regions, lung cancer was the leading cause of cancer death among white, black, and pardo Brazilians, with colorectal cancer deaths leading among Asian Brazilians. Black and pardo Brazilians had higher cervical cancer mortality rates than white Brazilians.

CONCLUSION

There are substantial disparities in mortality from different cancers in São Paulo according to ethnicity, pointing to inequities in access to health care services.

Disappearing cities on US coasts

The sea level along the US coastlines is projected to rise by 0.25-0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities1-3. However, these impacts may be exacerbated by coastal subsidence-the sinking of coastal land areas4-a factor that is often underrepresented in coastal-management policies and long-term urban planning2,5. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km2 is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000-273,000 people and 31,000-171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.

Human mobility networks reveal increased segregation in large cities

A long-standing expectation is that large, dense and cosmopolitan areas support socioeconomic mixing and exposure among diverse individuals1-6. Assessing this hypothesis has been difficult because previous measures of socioeconomic mixing have relied on static residential housing data rather than real-life exposures among people at work, in places of leisure and in home neighbourhoods7,8. Here we develop a measure of exposure segregation that captures the socioeconomic diversity of these everyday encounters. Using mobile phone mobility data to represent 1.6 billion real-world exposures among 9.6 million people in the United States, we measure exposure segregation across 382 metropolitan statistical areas (MSAs) and 2,829 counties. We find that exposure segregation is 67% higher in the ten largest MSAs than in small MSAs with fewer than 100,000 residents. This means that, contrary to expectations, residents of large cosmopolitan areas have less exposure to a socioeconomically diverse range of individuals. Second, we find that the increased socioeconomic segregation in large cities arises because they offer a greater choice of differentiated spaces targeted to specific socioeconomic groups. Third, we find that this segregation-increasing effect is countered when a city's hubs (such as shopping centres) are positioned to bridge diverse neighbourhoods and therefore attract people of all socioeconomic statuses. Our findings challenge a long-standing conjecture in human geography and highlight how urban design can both prevent and facilitate encounters among diverse individuals.

Global evidence of rapid urban growth in flood zones since 1985

Disaster losses are increasing and evidence is mounting that climate change is driving up the probability of extreme natural shocks1-3. Yet it has also proved politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local and national authorities4,5. However, locally determined patterns of urbanization and spatial development are key factors to the exposure and vulnerability of people to climatic shocks6. Using high-resolution annual data, this study shows that, since 1985, human settlements around the world-from villages to megacities-have expanded continuously and rapidly into present-day flood zones. In many regions, growth in the most hazardous flood zones is outpacing growth in non-exposed zones by a large margin, particularly in East Asia, where high-hazard settlements have expanded 60% faster than flood-safe settlements. These results provide systematic evidence of a divergence in the exposure of countries to flood hazards. Instead of adapting their exposure, many countries continue to actively amplify their exposure to increasingly frequent climatic shocks.

The global wildland-urban interface

The wildland-urban interface (WUI) is where buildings and wildland vegetation meet or intermingle1,2. It is where human-environmental conflicts and risks can be concentrated, including the loss of houses and lives to wildfire, habitat loss and fragmentation and the spread of zoonotic diseases3. However, a global analysis of the WUI has been lacking. Here, we present a global map of the 2020 WUI at 10 m resolution using a globally consistent and validated approach based on remote sensing-derived datasets of building area4 and wildland vegetation5. We show that the WUI is a global phenomenon, identify many previously undocumented WUI hotspots and highlight the wide range of population density, land cover types and biomass levels in different parts of the global WUI. The WUI covers only 4.7% of the land surface but is home to nearly half its population (3.5 billion). The WUI is especially widespread in Europe (15% of the land area) and the temperate broadleaf and mixed forests biome (18%). Of all people living near 2003-2020 wildfires (0.4 billion), two thirds have their home in the WUI, most of them in Africa (150 million). Given that wildfire activity is predicted to increase because of climate change in many regions6, there is a need to understand housing growth and vegetation patterns as drivers of WUI change.

Analysis of the spatial-temporal distribution characteristics of hepatitis E in Jiangsu province from 2005 to 2020

Objective

This study attempts to analyze the spatial clustering and spatial-temporal distribution characteristics of hepatitis E (HE) at the county (city and district) level in Jiangsu province to provide a scientific basis for the prevention and control of HE.

Method

The information on HE cases reported in the Chinese Center for Disease Control and Prevention Information System from 2005 to 2020 was collected for spatial autocorrelation analysis and spatial-temporal clustering analysis.

Result

From 2005 to 2020, 48,456 HE cases were reported in Jiangsu province, with an average annual incidence rate of 3.87/100,000. Male cases outnumbered female cases (2.46:1), and the incidence was highest in the 30-70 years of age group (80.50%). Farmers accounted for more than half of all cases (59.86%), and in terms of the average annual incidence, the top three cities were all in Zhenjiang city. Spatial autocorrelation analysis showed that Global Moran's I of HE incidence varied from 0.232 to 0.513 for the years. From 2005 to 2020, 31 counties (cities and districts) had high and statistically significant HE incidence, and two clustering areas were detected by spatial-temporal scanning.

Conclusion

HE incidence in Jiangsu province from 2005 to 2020 was stable, with age and gender differences, regional clustering, and spatial-temporal clustering. Further investigation of HE clustering areas is necessary to formulate corresponding targeted prevention and control measures.

Exploring the effect of city size on carbon emissions: Evidence from 259 prefecture-level cities in China

As a gathering place for human production activities, cities are the main places where energy consumption and carbon dioxide emissions occur. How to accurately measure city size and test the impact mechanism of city size on carbon emissions of different city levels is still controversial. This study uses the global nighttime light data to identify urban bright areas and built-up areas, and accordingly constructs the city size index of 259 prefecture level cities in China from 2003 to 2019. It avoids the problem of only considering the single index of population size or space size, and makes the measurement of city size more reasonable. We use a dynamic panel model to study the impact of city size on urban carbon emissions per capita, and discuss the heterogeneity of various cities under different population levels and economic development levels. The empirical results indicate that in the scale of cities in China showed a fluctuating growth trend in recent years. The city size index of most cities is clustered at medium and high values. The city size index of cities with different economic development levels and different population-scale levels shows obvious gradient differences but maintains an upward trend. The expansion of supercities (with a population of more than 5 million) introduces a drastic increase in carbon emissions. The carbon emissions growth caused by the expansion of cities that are classified as third tier and below is the smallest, while that caused by the expansion of cities that are classified as first-tier is the largest. The findings suggest differentiated emissions reduction suggestions for cities with different sizes.

Japanese Local Governments' Dissemination Activities for Advance Care Planning: A Descriptive Analysis of a Nationwide Survey during the COVID-19 Pandemic

This study aims to compare the awareness-raising activities between municipalities with and without focused anti-infection measures during the 2019 coronavirus disease (COVID-19) pandemic. Descriptive analysis was conducted using a nationwide self-administered questionnaire survey on municipalities' activities for residents and for healthcare providers and care workers (HCPs) in October 2022 in Japan. This study included 433 municipalities that had conducted awareness-raising activities before 2019 Fiscal Year. Workshops for residents were conducted in 85.2% of the municipalities, and they were more likely to be conducted in areas with focused anti-infection measures than those without measures (86.8% vs. 75.4%). Additionally, 85.9% of the municipalities were impacted by the pandemic; 50.1% canceled workshops, while 26.0% switched to a web-based style. Activities for HCPs were conducted in 55.2-63.7% of the municipalities, and they were more likely to be conducted in areas with focused anti-infection measures. A total of 50.6-62.1% of the municipalities changed their workshops for HCPs to a web-based style. Comparisons between areas with and without focused anti-infection measures indicated that the percentages of those impacted for all activities were not significantly different. In conclusion, awareness-raising activities in municipalities were conducted with new methods during the COVID-19 pandemic. Using information technology is essential to further promote such activities for residents.

External Factors Impacting Residents' Participation in Waste Sorting Using NCA and fsQCA Methods on Pilot Cities in China

Recycling waste is important as it can help to reduce environmental pollution caused by "waste siege". Source classification is an important part of the municipal solid waste (MSW) sorting process. The factors that prompt residents to participate in waste sorting have been debated by scholars in recent years; however, there are not many papers that focus on the complex relationships between them. This study reviewed the literature that concerns residents' participation in waste sorting, and it summarized the external factors that might influence residents' participation. Then, we focused on 25 pilot cities in China, and we analyzed the configuration impact of external factors on residents' participation using a necessary condition analysis (NCA) and a fuzzy-set qualitative comparative analysis (fsQCA). We found no consistency between variables, nor was there one single condition that caused residents to participate in waste sorting. There are two main methods (environment-driven and resource-driven) that can help achieve a high participation rate, and three methods that can cause a low participation rate. This study provides suggestions for the implementation of waste sorting in other cities in China, as well as developing countries, with an emphasis on the importance of public participation.

Quantification of industrial wastewater discharge from the major cities in Sichuan province, China

In this study, we used spatial autocorrelation, Environmental Kuznets Curve (EKC), and Logarithmic Mean Divisia Index model to study the spatial characteristics and driving factors of industrial wastewater discharge in Sichuan province (2003-2018). We showed that the amount of industrial wastewater discharge in Sichuan province for the period was reduced from 116,580 to 42,064.96 million tons as observed from the Moran index ranging from -0.310 to 0.302. We identified that the EKC type of Sichuan province was monotonically decreasing and six types of the EKC (monotonically decreasing, monotonically increasing, U, N, inverted U, and inverted N, shape) in 18 major cities. The technical effect (from -0.0964 to -8.8912) can reduce the discharge of industrial wastewater, while the economy effect (0.2948-5.882), structure effect (0.0892-4.5183), and population effect (from -0.0059 to 0.2873) can promote the industrial wastewater discharge. Our findings suggest that industrial wastewater discharge was reduced and changed from non-significant dissociation to non-significant agglomeration to non-significant dissociation during the study period. Furthermore, technical management upgrade is the primary driver in Sichuan province to reduce industrial wastewater discharge during this period.

Impact of COVID-19 lockdown on PM concentrations in an Italian Northern City: A year-by-year assessment

In the last century, the increase in traffic, human activities and industrial production have led to a diffuse presence of air pollution, which causes an increase of risk of several health conditions such as respiratory diseases. In Europe, air pollution is a serious concern that affects several areas, one of the worst ones being northern Italy, and in particular the Po Valley, an area characterized by low air quality due to a combination of high population density, industrial activity, geographical factors and weather conditions. Public health authorities and local administrations are aware of this problem, and periodically intervene with temporary traffic limitations and other regulations, often insufficient to solve the problem. In February 2020, this area was the first in Europe to be severely hit by the SARS-CoV-2 virus causing the COVID-19 disease, to which the Italian government reacted with the establishment of a drastic lockdown. This situation created the condition to study how significant is the impact of car traffic and industrial activity on the pollution in the area, as these factors were strongly reduced during the lockdown. Differently from some areas in the world, a drastic decrease in pollution measured in terms of particulate matter (PM) was not observed in the Po Valley during the lockdown, suggesting that several external factors can play a role in determining the severity of pollution. In this study, we report the case study of the city of Pavia, where data coming from 23 air quality sensors were analyzed to compare the levels measured during the lockdown with the ones coming from the same period in 2019. Our results show that, on a global scale, there was a statistically significant reduction in terms of PM levels taking into account meteorological variables that can influence pollution such as wind, temperature, humidity, rain and solar radiation. Differences can be noticed analyzing daily pollution trends too, as—compared to the study period in 2019—during the study period in 2020 pollution was higher in the morning and lower in the remaining hours.

Correlation between mobility in mass transport and mortality due to COVID-19: A comparison of Mexico City, New York, and Madrid from a data science perspective

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities’ local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don’t bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic.

Study on temporal and spatial differentiation of biocapacity in Shenyang from a multi-scale perspective

Biocapacity of a region exhibits spatial differences owing to the limitations of regional scale and natural conditions. Based on the multi-scale perspective, we comprehensively studied and analyzed the temporal and spatial differences of the biocapacity of a region in an attempt to establish the groundwork for optimizing urban development and its utilization framework. By adopting the ecological footprint model along with multi-scale difference evaluation method, the municipal and county scales are incorporated into a unified analysis framework in this paper, thereby facilitating the exploration of the temporal and spatial differences in the biocapacity of Shenyang-a city in China-from 2005 to 2019. The results demonstrated that: 1) At the municipal scale, the biocapacity per capita fluctuated between 1.35 hm2/person and 2.22 hm2/person. It revealed an "up-down-up" trend, which appeared consistent with the Kuznets cycle; at the county scale, the biocapacity depicted spatial differences, while those of downtown and surrounding districts/counties developed a two-level ascending hierarchical structure. 2) The time series of footprint size and depth first ascended and then declined, and can be classified into four types: closed type, inverted U-type, S-type, and M-type. Among them, S-type and M-type have the phenomenon of over-utilizing the stock capital. 3) For a long time, the regional difference of biocapacity has mostly dwelt on two scales with an evident scale effect, and the biocapacity of Liaozhong District was the worst.

Evaluation of tourism competitiveness and mechanisms of spatial differentiation in Xinjiang, China

Evaluation of tourism competitiveness is useful for measuring the level of regional tourism development. It is of great importance to understand the advantages and disadvantages of tourism development correctly and formulate corresponding development strategies. To investigate tourism competitiveness, this paper established an evaluation index system, including tourism development competitiveness, tourism resource competitiveness, and tourism-support competitiveness, for 14 prefectures and cities in Xinjiang in China. The characteristics and laws of spatial differentiation were analyzed. Factor analysis was applied to examine the spatial differentiation of regional tourism competitiveness. The results showed an obvious spatial differentiation in tourism competitiveness among the 14 prefectures and cities. In terms of development competitiveness, Yili and Urumqi constituted the spatial center, followed by Changji, Altay, and Ba Prefecture. As the provincial capital, Urumqi has political, economic, cultural, transportation, and geographic advantages, but its competitiveness is not prominent in terms of monopoly and efficiency. In terms of resource competitiveness, Yili is the core attraction, while Urumqi, Kashgar, Altay, and Ba Prefecture are dominant attractions. With respect to supporting competitiveness, Bo Prefecture has high value, followed by Urumqi City and Aksu. Hetian and Ke Prefecture have the lowest values. The comprehensive competitiveness of tourism is centered on Yili. Urumqi and Bo Prefecture are subcenters, and Changji, Altay, Ba Prefecture, Aksu, and Kashgar are characterized as multi-polar competition areas. Using the KMO and Bartlett's sphericity tests, the cumulative contribution variance of the eigenvalues of the eight factors extracted by the maximum variance rotation method was found to be 92.714%. Socio-economic conditions, tourism resources, infrastructure construction, regional cultural influence, ecological environment carrying capacity, tertiary industry development, tourism service level, and living security system are the main driving factors affecting the spatial differentiation of tourism competitiveness in Xinjiang. Analyzing the spatial evolution characteristics and the driving factors of the regional tourism competitiveness in Xinjiang, this paper seeks to promote the optimal allocation of tourism production factors in the macro regional system, and provide theoretical guidance and an empirical basis for the comprehensive and harmonic development of regional tourism.

Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (R_m) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (R_m) were consistently higher, and persisted for a greater duration, during the Zika epidemic (R_m = 1.0) than during the dengue epidemic (R_m < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, R_m, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.

Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code-level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. 2022;112(1):144-153. https://doi.org/10.2105/AJPH.2021.306540).

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

Markovian city-scale modelling and mitigation of micro-particles from tires

The recent uptake in popularity in vehicles with zero tailpipe emissions is a welcome development in the fight against traffic induced airborne pollutants. As vehicle fleets become electrified, and tailpipe emissions become less prevalent, non-tailpipe emissions (from tires and brake disks) will become the dominant source of traffic related emissions, and will in all likelihood become a major concern for human health. This trend is likely to be exacerbated by the heavier weight of electric vehicles, their increased power, and their increased torque capabilities, when compared with traditional vehicles. While the problem of emissions from tire wear is well-known, issues around the process of tire abrasion, its impact on the environment, and modelling and mitigation measures, remain relatively unexplored. Work on this topic has proceeded in several discrete directions including: on-vehicle collection methods; vehicle tire-wear abatement algorithms and controlling the ride characteristics of a vehicle, all with a view to abating tire emissions. Additional approaches include access control mechanisms to manage aggregate tire emissions in a geofenced area with other notable work focussing on understanding the particle size distribution of tire generated PM, the degree to which particles become airborne, and the health impacts of tire emissions. While such efforts are already underway, the problem of developing models to predict the aggregate picture of a network of vehicles at the scale of a city, has yet to be considered. Our objective in this paper is to present one such model, built using ideas from Markov chains. Applications of our modelling approach are given toward the end of this note, both to illustrate the utility of the proposed method, and to illustrate its application as part of a method to collect tire dust particles.

The role of urban trees in reducing land surface temperatures in European cities

Urban trees influence temperatures in cities. However, their effectiveness at mitigating urban heat in different climatic contexts and in comparison to treeless urban green spaces has not yet been sufficiently explored. Here, we use high-resolution satellite land surface temperatures (LSTs) and land-cover data from 293 European cities to infer the potential of urban trees to reduce LSTs. We show that urban trees exhibit lower temperatures than urban fabric across most European cities in summer and during hot extremes. Compared to continuous urban fabric, LSTs observed for urban trees are on average 0-4 K lower in Southern European regions and 8-12 K lower in Central Europe. Treeless urban green spaces are overall less effective in reducing LSTs, and their cooling effect is approximately 2-4 times lower than the cooling induced by urban trees. By revealing continental-scale patterns in the effect of trees and treeless green spaces on urban LST our results highlight the importance of considering and further investigating the climate-dependent effectiveness of heat mitigation measures in cities.

Molecular testing and analysis of disease spreading during the emergence of COVID-19 in Macaé, the Brazilian National Capital of Oil

The Brazilian strategy to overcome the spread of COVID-19 has been particularly criticized due to the lack of a national coordinating effort and an appropriate testing program. Here, a successful approach to control the spread of COVID-19 transmission is described by the engagement of public (university and governance) and private sectors (hospitals and oil companies) in Macaé, state of Rio de Janeiro, Brazil, a city known as the National Oil Capital. In 2020 between the 17th and 38th epidemiological week, over two percent of the 206,728 citizens were subjected to symptom analysis and RT-qPCR testing by the Federal University of Rio de Janeiro, with positive individuals being notified up to 48 h after swab collection. Geocodification and spatial cluster analysis were used to limit COVID-19 spreading in Macaé. Within the first semester after the outbreak of COVID-19 in Brazil, Macaé recorded 1.8% of fatalities associated with COVID-19 up to the 38th epidemiological week, which was at least five times lower than the state capital (10.6%). Overall, considering the successful experience of this joint effort of private and public engagement in Macaé, our data suggest that the development of a similar strategy countrywise could have contributed to a better control of the COVID-19 spread in Brazil. Quarantine decree by the local administration, comprehensive molecular testing coupled to scientific analysis of COVID-19 spreading, prevented the catastrophic consequences of the pandemic as seen in other populous cities within the state of Rio de Janeiro and elsewhere in Brazil.

Excess Deaths in Mexico City and New York City During the COVID-19 Pandemic, March to August 2020

Objectives. To estimate all-cause excess deaths in Mexico City (MXC) and New York City (NYC) during the COVID-19 pandemic. Methods. We estimated expected deaths among residents of both cities between March 1 and August 29, 2020, using log-linked negative binomial regression and compared these deaths with observed deaths during the same period. We calculated total and age-specific excess deaths and 95% prediction intervals (PIs). Results. There were 259 excess deaths per 100 000 (95% PI = 249, 269) in MXC and 311 (95% PI = 305, 318) in NYC during the study period. The number of excess deaths among individuals 25 to 44 years old was much higher in MXC (77 per 100 000; 95% PI = 69, 80) than in NYC (34 per 100 000; 95% PI = 30, 38). Corresponding estimates among adults 65 years or older were 1263 (95% PI = 1199, 1317) per 100 000 in MXC and 1581 (95% PI = 1549, 1621) per 100 000 in NYC. Conclusions. Overall, excess mortality was higher in NYC than in MXC; however, the excess mortality rate among young adults was higher in MXC. Public Health Implications. Excess all-cause mortality comparisons across populations and age groups may represent a more complete measure of pandemic effects and provide information on mitigation strategies and susceptibility factors. (Am J Public Health. 2021;111(10): 1847-1850. https://doi.org/10.2105/AJPH.2021.306430).

Analysis of the spatial distribution of Aedes albopictus in an urban area of Shanghai, China

BACKGROUND

Aedes albopictus is a vector of major arboviral diseases and a primary pest in tropical and temperate regions of China. In most cities of China, the current monitoring system for the spread of Ae. albopictus is based on the subdistrict scale and does not consider spatial distribution for analysis of species density. Thus, the system is not sufficiently accurate for epidemic investigations, especially in large cities.

METHODS

This study used an improved surveillance program, with the mosquito oviposition trap (MOT) method, integrating the actual monitoring locations to investigate the temporal and spatial distribution of Ae. albopictus abundance in an urban area of Shanghai, China from 2018 to 2019. A total of 133 monitoring units were selected for surveillance of Ae. albopictus density in the study area, which was composed of 14 subdistricts. The vector abundance and spatial structure of Ae. albopictus were predicted using a binomial areal kriging model based on eight MOTs in each unit. Results were compared to the light trap (LT) method of the traditional monitoring scheme.

RESULTS

A total of 8,192 MOTs were placed in the study area in 2018, and 7917 (96.6%) were retrieved, with a positive rate of 6.45%. In 2019, 22,715 (97.0%) of 23,408 MOTs were recovered, with a positive rate of 5.44%. Using the LT method, 273 (93.5%) and 312 (94.5%) adult female Ae. albopictus were gathered in 2018 and 2019, respectively. The Ae. albopictus populations increased slowly from May, reached a peak in July, and declined gradually from September. The MOT positivity index (MPI) showed significant positive spatial autocorrelation across the study area, whereas LT collections indicated a nonsignificant spatial autocorrelation. The MPI was suitable for spatial interpolation using the binomial areal kriging model and showed different hot spots in different years.

CONCLUSIONS

The improved surveillance system integrated with a geographical information system (GIS) can improve our understanding of the spatial and temporal distribution of Ae. albopictus in urban areas and provide a practical method for decision-makers to implement vector control and mosquito management.

Using the Robson classification to assess caesarean section rates in Brazil: an observational study of more than 24 million births from 2011 to 2017

BACKGROUND

Applying the Robson classification to all births in Brazil, the objectives of our study were to estimate the rates of caesarean section delivery, assess the extent to which caesarean sections were clinically indicated, and identify variation across socioeconomic groups.

METHODS

We conducted a population-based study using routine records of the Live Births Information System in Brazil from January 1, 2011, to December 31, 2017. We calculated the relative size of each Robson group; the caesarean section rate; and the contribution to the overall caesarean section rate. We categorised Brazilian municipalities using the Human Development Index to explore caesarean section rates further. We estimated the time trend in caesarean section rates.

RESULTS

The rate of caesarean sections was higher in older and more educated women. Prelabour caesarean sections accounted for more than 54 % of all caesarean deliveries. Women with a previous caesarean section (Group 5) made up the largest group (21.7 %). Groups 6-9, for whom caesarean sections would be indicated in most cases, all had caesarean section rates above 82 %, as did Group 5. The caesarean section rates were higher in municipalities with a higher HDI. The general Brazilian caesarean section rate remained stable during the study period.

CONCLUSIONS

Brazil is a country with one of the world's highest caesarean section rates. This nationwide population-based study provides the evidence needed to inform efforts to improve the provision of clinically indicated caesarean sections. Our results showed that caesarean section rates were lower among lower socioeconomic groups even when clinically indicated, suggesting sub-optimal access to surgical care.

Indoor and outdoor noise changes due to the COVID-19 lockdown and their effects on individuals' expectations and preferences

The COVID-19 pandemic significantly modified our urban territories. One of the most strongly affected parameters was outdoor noise, caused by traffic and human activity in general, all of which were forced to stop during the spring of 2020. This caused an indubitable noise reduction both inside and outside the home. This study investigates how people reacted to this new unexpected, unwanted and unpredictable situation. Using field measurements, it was possible to demonstrate how the outdoor sound pressure level clearly decreased. Furthermore, by means of an international survey, it was discovered that people had positive reaction to the lower noise level. This preference was generally not related to home typology or location in the city, but rather to a generalized wish to live in a quieter urban environment.

Efficiency of tourism development in China's major cities under the constraint of PM2.5

Climate / weather factors are important factors for tourists to choose tourist destinations. With the public's attention to the influence of haze, air quality will have a profound impact on the development of tourism in tourist destinations. Based on the Epsilon-based Measure (EBM) super-efficiency model and Global Malmquist-Luenberger index analysis method, this paper aims to study the tourism development efficiency of 58 major cities in China from 2001 to 2016 and analyse the total factor productivity in the development of urban tourism and the changing driving factors in consideration of the undesirable output of haze characterised by PM2.5 emission concentration. The study findings show that the overall efficiency of tourism development of 58 cities is not high in 2001-2016, but the tourism development efficiency of all cities is increasing year by year. Under the constraint of haze, the efficiency of urban tourism development is not directly proportional to the degree of urban development. The overall redundancy rate of each input index is slightly high, and the redundancy of PM2.5 emission concentration has a considerable effect on the efficiency of urban tourism development. The overall change trend in total factor productivity in the development of urban tourism is improved, mainly due to the improvement of technological progress factors. On this basis, the corresponding policy implications are concluded according to high-efficiency and high-quality development of tourism in 58 major cities.

A spatio-temporal analysis of scrub typhus and murine typhus in Laos; implications from changing landscapes and climate

BACKGROUND

Scrub typhus (ST) and murine typhus (MT) are common but poorly understood causes of fever in Laos. We examined the spatial and temporal distribution of ST and MT, with the intent of informing interventions to prevent and control both diseases.

METHODOLOGY AND PRINCIPLE FINDINGS

This study included samples submitted from 2003 to 2017 to Mahosot Hospital, Vientiane, for ST and MT investigation. Serum samples were tested using IgM rapid diagnostic tests. Patient demographic data along with meteorological and environmental data from Laos were analysed. Approximately 17% of patients were positive for either ST (1,337/8,150 patients tested) or MT (1,283/7,552 patients tested). While both diseases occurred in inhabitants from Vientiane Capital, from the univariable analysis MT was positively and ST negatively associated with residence in Vientiane Capital. ST was highly seasonal, with cases two times more likely to occur during the wet season months of July-September compared to the dry season whilst MT peaked in the dry season. Multivariable regression analysis linked ST incidence to fluctuations in relative humidity whereas MT was linked to variation in temperature. Patients with ST infection were more likely to come from villages with higher levels of surface flooding and vegetation in the 16 days leading up to diagnosis.

CONCLUSIONS

The data suggest that as cities expand, high risk areas for MT will also expand. With global heating and risks of attendant higher precipitation, these data suggest that the incidence and spatial distribution of both MT and ST will increase.

Industrial agglomeration and air pollution: A new perspective from enterprises in Atmospheric Pollution Transmission Channel Cities (APTCC) of Beijing-Tianjin-Hebei and its surrounding areas, China

Air quality in China has gradually been improving in recent years; however, the Beijing-Tianjin-Hebei (BTH) region continues to be the most polluted area in China, with the worst air quality index. BTH and its surrounding areas experience high agglomeration of heavy-polluting manufacturers that generate electric power, process petroleum and coal, and carry out smelting and pressing of ferrous metals, raw chemical materials, chemical products, and non-metallic mineral products. This study presents evidence of the air pollution impacts of industrial agglomeration using the Ellison-Glaeser index, Herfindahl-Hirschman index, and spatial autocorrelation analysis. This was based on data from 73,353 enterprises in "2+26" atmospheric pollution transmission channel cities in BTH and its surrounding areas (herein referred to as BTH "2+26" cities). The results showed that Beijing, Yangquan, Puyang, Kaifeng, Taiyuan, and Jinan had the highest Ellison-Glaeser index among the BTH "2+26" cities; this represents the highest enterprise agglomeration. Beijing, Langfang, Tianjin, Baoding, and Tangshan also showed a low Herfindahl-Hirschman index of pollutant emissions, which have a relatively high degree of industrial agglomeration in BTH "2+26" cities. There was an inverted U-shaped relationship between enterprise agglomeration and air quality in the BTH "2+26" cities. This means that air quality improved with increased industrial agglomeration up to a certain level; beyond this point, the air quality begins to deteriorate with a decrease in industrial agglomeration.

SARS-CoV-2 percent positivity and risk factors among people with HIV at an urban academic medical center

Since the onset of the COVID-19 pandemic, it has been unclear how vulnerable people with HIV (PwH) are to SARS-CoV-2 infection. We sought to determine if PwH are more likely to test positive for SARS-CoV-2 than people without HIV, and to identify risk factors associated with SARS-CoV-2 positivity among PwH. We conducted a cross-sectional study in which we collected electronic medical record data for all patients who underwent SARS-CoV-2 PCR testing at an academic medical center. Presence of HIV and other chronic diseases were based on the presence of ICD-10 diagnosis codes. We calculated the percent positivity for SARS-CoV-2 among PwH and among people without HIV. Among PwH, we compared demographic factors, comorbidities, HIV viral load, CD4 T-cell count, and antiretroviral therapy (ART) regimens between those who tested positive for SARS-CoV-2 and those who tested negative. Comparisons were made using chi squared tests or Wilcoxon rank sum tests. Multivariate models were created using logistic regression. Among 69,763 people tested for SARS-CoV-2, 0.6% (431) were PwH. PwH were not significantly more likely to test positive for SARS-CoV-2 than people without HIV (7.2% (31/431) vs 8.4% (5820/69763), p = 0.35), but were more likely to be younger, Black, and male (p-values < .0001). There were no significant differences in HIV clinical factors, chronic diseases, or ART regimens among PwH testing positive for SARS-CoV-2 versus those testing negative. In our sample, PwH were not more likely to contract SARS-CoV-2, despite being more likely to be members of demographic groups known to be at higher risk for infection. Differences between PwH who tested positive for SARS-CoV-2 and those who tested negative were only seen in Hispanic/Latino ethnicity (non-Hispanic or Latino vs unknown Hispanic or Latino ethnicity (OR 0.2 95% CI (0.6, 0.9)) and site of testing(inpatient vs outpatient OR 3.1 95% CI (1.3, 7.4)).

Mortality and morbidity of asthma and chronic obstructive pulmonary disease associated with ambient environment in metropolitans in Taiwan

Background

This study investigated risks of mortality from and morbidity (emergency room visits (ERVs) and outpatient visits) of asthma and chronic obstructive pulmonary disease (COPD) associated with extreme temperatures, fine particulate matter (PM_2.5), and ozone (O₃) by sex, and age, from 2005 to 2016 in 6 metropolitan cities in Taiwan.

Methods

The distributed lag non-linear model was employed to assess age (0–18, 19–39, 40–64, and 65 years and above), sex-cause-specific deaths, ERVs, and outpatient visits associated with extreme high (99^th percentile) and low (5^th percentile) temperatures and PM_2.5 and O₃ concentrations at 90^th percentile. Random-effects meta-analysis was adopted to investigate cause-specific pooled relative risk (RR) and 95% confidence intervals (CI) for the whole studied areas.

Results

Only the mortality risk of COPD in the elderly men was significantly associated with the extreme low temperatures. Exposure to the 90^th percentile PM_2.5 was associated with outpatient visits for asthma in 0–18 years old boys [RR = 1.15 (95% CI: 1.09–1.22)]. Meanwhile, significant elevation of ERVs of asthma for females aged 40–64 years was associated with exposure to ozone, with the highest RR of 1.21 (95% CI: 1.05–1.39).

Conclusions

This study identified vulnerable subpopulations who were at risk to extreme events associated with ambient environments deserving further evaluation for adaptation.

Vehicle trajectory prediction and generation using LSTM models and GANs

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

Excess demand prediction for bike sharing systems

One of the most crucial elements for the long-term success of shared transportation systems (bikes, cars etc.) is their ubiquitous availability. To achieve this, and avoid having stations with no available vehicle, service operators rely on rebalancing. While different operators have different approaches to this functionality, overall it requires a demand-supply analysis of the various stations. While trip data can be used for this task, the existing methods in the literature only capture the observed demand and supply rates. However, the excess demand rates (e.g., how many customers attempted to rent a bike from an empty station) are not recorded in these data, but they are important for the in-depth understanding of the systems’ demand patterns that ultimately can inform operations like rebalancing. In this work we propose a method to estimate the excess demand and supply rates from trip and station availability data. Key to our approach is identifying what we term as excess demand pulse (EDP) in availability data as a signal for the existence of excess demand. We then proceed to build a Skellam regression model that is able to predict the difference between the total demand and supply at a given station during a specific time period. Our experiments with real data further validate the accuracy of our proposed method.

Assessing a transmission network of Mycobacterium tuberculosis in an African city using single nucleotide polymorphism threshold analysis

Tuberculosis (TB) is the leading cause of death in humans by a single infectious agent worldwide with approximately two billion humans latently infected with the bacterium Mycobacterium tuberculosis. Currently, the accepted method for controlling the disease is Tuberculosis Directly Observed Treatment Shortcourse (TB-DOTS). This program is not preventative and individuals may transmit disease before diagnosis, thus better understanding of disease transmission is essential. Using whole-genome sequencing and single nucleotide polymorphism analysis, we analyzed genomes of 145 M. tuberculosis clinical isolates from active TB cases from the Rubaga Division of Kampala, Uganda. We established that these isolates grouped into M. tuberculosis complex (MTBC) lineages 1, 2, 3, and 4, with the most isolates grouping into lineage 4. Possible transmission pairs containing ≤12 SNPs were identified in lineages 1, 3, and 4 with the prevailing transmission in lineages 3 and 4. Furthermore, investigating DNA codon changes as a result of specific SNPs in prominent virulence genes including plcA and plcB could indicate potentially important modifications in protein function. Incorporating this analysis with corresponding epidemiological data may provide a blueprint for the integration of public health interventions to decrease TB transmission in a region.

Risk factors of suicidality among married adults: A cross-sectional survey in Rajshahi City, Bangladesh

Background

Suicide is a serious public health concern all over the world including Bangladesh. About 9% of the patients admitted with suicidal ideation (SI) or suicide attempt (SA) later complete suicide. To understand and prevent suicide, the study of SI and SA is necessary but research in this area is scanty in Bangladesh. Therefore, we studied suicidality (SI and SA) among married adults in Rajshahi City, Bangladesh.

Methods

This was a household cross-sectional study. A total of 708 married adults were selected for this study using a multi-stage random sampling. Suicidality was measured based on two factors: (i) suicidal ideation, and (ii) suicide attempt. Frequency distribution, Chi-square test and multiple binary logistic regression model were used in this study according to our objectives.

Results

The prevalence of suicidal ideation, suicide attempt, and suicidality was 5.8%, 3.4%, and 8.3% respectively among married adults. A multiple binary logistic regression model provided the following risk factors of suicidality: (i) joint family (AOR = 0.310, p<0.01), (ii) ≥26 years of age at the first marriage (AOR = 0.379, p<0.05), (iii) twice or more marriage (AOR = 0.214, p<0.01), (iv) conjugal life of ≥16 years (AOR = 0.410, p<0.05), (v) having no child (AOR = 6.343, p<0.01) and (vi) having 1–2 children (AOR = 6.190, p<0.01), (vii) medical comorbidity (AOR = 0.421, p<0.01), (viii) mental comorbidity (AOR = 0.253, p<0.01), (ix) stress-anxiety (AOR = 0.311, p<0.01), (x) family history of mental disorders (AOR = 0.059, p<0.01), (xi) family history of suicide/suicide attempt (AOR = 0.009, p<0.01), (xii) substance abuse (AOR = 0.065, p<0.01), (xiii) poor relationship with spouse (AOR = 0.209, p<0.01), and (xiv) poor relationship with other family members (AOR = 0.347, p<0.05).

Conclusion

The prevalence of suicidality is remarkable in Rajshahi city, Bangladesh. The government and non-government agencies can use the findings of this study to identify the vulnerable groups and undertake measures for preventing and reducing suicidality.

Assessing private provider perceptions and the acceptability of video observed treatment technology for tuberculosis treatment adherence in three cities across Viet Nam

Background

The World Health Organization recently recommended Video Observed Therapy (VOT) as one option for monitoring tuberculosis (TB) treatment adherence. There is evidence that private sector TB treatment has substandard treatment follow-up, which could be improved using VOT. However, acceptability of VOT in the private sector has not yet been evaluated.

Methods

We conducted a cross-sectional survey employing a theoretical framework for healthcare intervention acceptability to measure private provider perceptions of VOT across seven constructs in three cities of Viet Nam: Ha Noi, Ho Chi Minh City, and Hai Phong. We investigated the differences in private providers’ attitudes and perceptions of VOT using mixed ordinal models to test for significant differences in responses between groups of providers stratified by their willingness to use VOT.

Results

A total of 79 private providers completed the survey. Sixty-two providers (75%) indicated they would use VOT if given the opportunity. Between private providers who would and would not use VOT, there were statistically significant differences (p≤0.001) in the providers’ beliefs that VOT would help identify side effects faster and in their confidence to monitor treatment and provide differentiated care with VOT. There were also significant differences in providers’ beliefs that VOT would save them time and money, address problems faced by their patients, benefit their practice and patients, and be relevant for all their patients.

Conclusion

Private providers who completed the survey have positive views towards using VOT and specific subpopulations acknowledge the value of integrating VOT into their practice. Future VOT implementation in the private sector should focus on emphasizing the benefits and relevance of VOT during recruitment and provide programmatic support for implementing differentiated care with the technology.

The spatial coupling effect between urban street network's centrality and collection & delivery points: A spatial design network analysis-based study

The sustainable development of collection and delivery points and urban street network is an important consideration of logistic planners. Urban street networks have a significant impact on collection and delivery points’ location, but the spatial relationship between the centrality of urban street network and collection and delivery points has not been studied using spatial design network analysis. In a multiple centrality assessment model, we used point of interest and street network data to evaluate the location of two types of collection and delivery points and the centrality of streets in Nanjing city, based on four indicators: closeness, betweenness, severance, and efficiency. Then, kernel density estimation and spatial autocorrelation are used to study spatial patterns of distribution and centrality coupling effects of urban street network and collection and delivery points. The results show that the centrality of Nanjing streets has a big influence on the location of the collection and delivery points, and the directions of different types of centrality also vary. The location of the Cainiao Stations are largely related to closeness, followed by betweenness, severance, and efficiency. China Post Stations and street centrality have a weak correlation between efficiency and severance, but no correlation between closeness and betweenness. Our results can help logistics enterprises and urban planners to develop collection and delivery points’ network based on the urban street network.

Data-Driven Reopening of Urban Public Education Through Chicago's Tracking of COVID-19 School Transmission

Reopening in-person education in public schools during the coronavirus 2019 (COVID-19) pandemic requires careful risk-benefit analysis, with no current established metrics. Equity concerns in urban public schools such as decreased enrollment among largely Black and Latinx prekindergarten and special needs public school students already disproportionately impacted by the pandemic itself have added urgency to Chicago Department of Public Health's analysis of COVID-19 transmission. Close tracking within a large school system revealed a lower attack rate for students and staff participating in in-person learning than for the community overall. By combining local data from a large urban private school system with national and international data on maintaining in-person learning during COVID-19 surges, Chicago believes in-person public education poses a low risk of transmission when the operational burden imposed by the second wave has subsided.

Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study

Evidence in the literature suggests that air pollution exposure affects outcomes of patients with COVID-19. However, the extent of this effect requires further investigation. This study was designed to investigate the relationship between long-term exposure to air pollution and the case fatality rate (CFR) of patients with COVID-19. The data on air quality index (AQI), PM2.5, PM10, SO2 , NO2 , and O3 from 14 major cities in China in the past 5 years (2015-2020) were collected, and the CRF of COVID-19 patients in these cities was calculated. First, we investigated the correlation between CFR and long-term air quality indicators. Second, we examined the air pollutants affecting CFR and evaluated their predictive values. We found a positive correlation between the CFR and AQI (1, 3, and 5 years), PM2.5 (1, 3, and 5 years), and PM10 (1, 3, and 5 years). Further analysis indicated the more significant correlation for both AQI (3 and 5 years) and PM2.5 (1, 3, and 5 years) with CFR, and moderate predictive values for air pollution indicators such as AQI (1, 3, and 5 years) and PM2.5 (1, 3, and 5 years) for CFR. Our results indicate that long-term exposure to severe air pollution is associated with higher CFR of COVID-19 patients. Air pollutants such as PM2.5 may assist with the prediction of CFR for COVID-19 patients.

Excess Mortality Associated With the COVID-19 Pandemic-Los Angeles County, March-September 2020

OBJECTIVE

To more comprehensively estimate COVID-19-related mortality in Los Angeles County by determining excess all-cause mortality and pneumonia, influenza, or COVID (PIC) mortality.

DESIGN

We reviewed vital statistics data to identify deaths registered in Los Angeles County between March 15, 2020, and August 15, 2020. Deaths with an ICD-10 (International Classification of Diseases, Tenth Revision) code for pneumonia, influenza, or COVID-19 listed as an immediate or underlying cause of death were classified as PIC deaths. Expected deaths were calculated using negative binomial regression. Excess mortality was determined by subtracting the expected from the observed number of weekly deaths. The Department of Public Health conducts surveillance for COVID-19-associated deaths: persons who died of nontraumatic/nonaccidental causes within 60 days of a positive COVID-19 test result were classified as confirmed COVID-19 deaths. Deaths without a reported positive SARS-Cov-2 polymerase chain reaction result were classified as probable COVID-19 deaths if COVID-19 was listed on their death certificate or the death occurred 60 to 90 days of a positive test. We compared excess PIC deaths with the number of confirmed and probable COVID-19 deaths ascertained by surveillance.

SETTING

Los Angeles County.

PARTICIPANTS

Residents of Los Angeles County who died.

MAIN OUTCOME MEASURE

Excess mortality.

RESULTS

There were 7208 excess all-cause and 5128 excess PIC deaths during the study period. The Department of Public Health also reported 5160 confirmed and 323 probable COVID-19-associated deaths.

CONCLUSIONS

The number of excess PIC deaths estimated by our model was approximately equal to the number of confirmed and probable COVID-19 deaths identified by surveillance. This suggests our surveillance definition for confirmed and probable COVID-19 deaths might be sufficiently sensitive for capturing the true burden of deaths caused directly or indirectly by COVID-19.

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

The effect of eviction moratoria on the transmission of SARS-CoV-2

Massive unemployment during the COVID-19 pandemic could result in an eviction crisis in US cities. Here we model the effect of evictions on SARS-CoV-2 epidemics, simulating viral transmission within and among households in a theoretical metropolitan area. We recreate a range of urban epidemic trajectories and project the course of the epidemic under two counterfactual scenarios, one in which a strict moratorium on evictions is in place and enforced, and another in which evictions are allowed to resume at baseline or increased rates. We find, across scenarios, that evictions lead to significant increases in infections. Applying our model to Philadelphia using locally-specific parameters shows that the increase is especially profound in models that consider realistically heterogenous cities in which both evictions and contacts occur more frequently in poorer neighborhoods. Our results provide a basis to assess eviction moratoria and show that policies to stem evictions are a warranted and important component of COVID-19 control.

An analytical process of spatial autocorrelation functions based on Moran's index

A number of spatial statistic measurements such as Moran’s I and Geary’s C can be used for spatial autocorrelation analysis. Spatial autocorrelation modeling proceeded from the 1-dimension autocorrelation of time series analysis, with time lag replaced by spatial weights so that the autocorrelation functions degenerated to autocorrelation coefficients. This paper develops 2-dimensional spatial autocorrelation functions based on the Moran index using the relative staircase function as a weight function to yield a spatial weight matrix with a displacement parameter. The displacement bears analogy with the time lag in time series analysis. Based on the spatial displacement parameter, two types of spatial autocorrelation functions are constructed for 2-dimensional spatial analysis. Then the partial spatial autocorrelation functions are derived by using the Yule-Walker recursive equation. The spatial autocorrelation functions are generalized to the autocorrelation functions based on Geary’s coefficient and Getis’ index. As an example, the new analytical framework was applied to the spatial autocorrelation modeling of Chinese cities. A conclusion can be reached that it is an effective method to build an autocorrelation function based on the relative step function. The spatial autocorrelation functions can be employed to reveal deep geographical information and perform spatial dynamic analysis, and lay the foundation for the scaling analysis of spatial correlation.

Dengue in Dhaka, Bangladesh: Hospital-based cross-sectional KAP assessment at Dhaka North and Dhaka South City Corporation area

Dengue, the most important mosquito-borne viral disease of humans is a recurring global health problem. In Bangladesh, dengue outbreaks are on the increase despite the efforts of government and it is not clear what the understanding of the general Dhaka population towards dengue fever is. Knowledge, attitude and practice (KAP) studies are essential guides in public health interventions. Hence, using KAP, this study aims to assess patient-perspectives with regards to factors associated with dengue, as well as investigate the associated factors between the two corporations in Dhaka. A Hospital-based cross-sectional study of 242 fever patients from two city-corporations in Dhaka (Dhaka North City Corporations, DNCC (n = 91, 37.6%) and Dhaka South City Corporation, DSCC (n = 151, 62.4%) was conducted using pre-tested KAP items. Wilcoxon's Rank Sum was used to determine the KAP by DNCC, DSCC and both corporations and multivariate Poisson regression analyses. The two corporations were analysed separately due to the differences in income distribution, concentration of slums, hospitals and clinics. The study found that more than half of the study population were knowledgeable about dengue (mean percentage scores was 52%), possess an appropriate and acceptable attitude towards the disease (69.2%), and about two thirds of the respondents (71.4%) engaged in practices towards its prevention. After adjusting for the potential cofounders, the factors associated with KAP about dengue fever varied between DNCC and DSCC; with duration of residency and use of mosquito nets were associated with knowledge in the north while income class and age were associated with knowledge and attitude in the south. In the pooled analysis (combining both corporations), knowledge of dengue was associated with good practice towards dengue fever among the respondents. The duration of residence in Dhaka (10+ years), not using mosquito nets and length of time spent in the hospital (7+ days) due to dengue, and decreased knowledge (Adjusted coefficient (β) = -0.01, 95%CI: -0.02, -0.01) were associated with attitude towards dengue in DNCC. On the other hand, middle-high income class, age (40+ years) and increased knowledge were associated with practice towards dengue in DSCC (β = 0.02, 95%CI: 0.01, 0.03). Efforts to increase knowledge about dengue fever through education by the administrations of both corporations would benefit from targeting these high-risk groups for a more sustainable outcome.

Characteristics and influencing factors of Airbnb spatial distribution in China's rapid urbanization process: A case study of Nanjing

As in other countries, short-term rentals for tourism services are growing rapidly in China's tourist cities, which are mainly operated through Airbnb. This paper explores whether the spatial distribution of Airbnb in China's rapid urbanization process exhibits characteristics, paths, and drivers that are different from those of cities in other countries. Airbnb is a model for the global sharing economy, but it is increasingly influenced by other functions and facilities in cities as it grows. In this paper, the zero-expansion negative binomial regression was used to study the factors affecting the spatial distribution of Airbnb in Nanjing, China. The results showed that the spatial distribution of Airbnb listings was correlated with the distribution of cultural attractions, universities, public transport accessibility, shopping centers, and business apartments. By analyzing the driving forces of Airbnb's development in Nanjing, this paper found that a large number of business apartments developed in cities were essential providers of Airbnb listings, and affected its spatial distribution. The gap between short-term and long-term rentals was also correlated with the distribution of Airbnb. In addition, similar to the previous literature findings, the increase in the proportion of professional hosts changes the original intention of Airbnb for sharing and communication. Our empirical results applies to the current situation of Airbnb in Chinese cities, which is conducive to the government's more intelligent management and effective promotion of the Airbnb market. Our findings also provide positive references for urban renewal policies and public participation methods in China.

User demands analysis of Eco-city based on the Kano model-An application to China case study

Eco-cities have witnessed rapid growth in these years worldwide. As the Eco-cities entering operation stage gradually, more and more researchers have found that users (who are living or working in the Eco-cities) satisfaction is one of the most important factors to determine the success or failure of Eco-cities. Therefore, it is very important to investigate the user demands to attract more citizens willing to live or work in the Eco-cities, which will make the development of Eco-cities more sustainable and solid. The recent researches on user demands investigation and analysis in the Eco-cities mainly focused on understanding the user need itself, yet lack of research on the relationship between the user demand and user satisfaction. This paper initially introduced the Kano model analysis method to the research field of user demands in Eco-city, to explore the relationship between the user demand and user satisfaction. After proposing user demands library in Eco-city (including Land use, Ecological environment, Green building, Energy utilization, etc.), the user demands classification and importance analysis methods of Eco-city were proposed based on Kano model. The questionnaire survey for users of two Eco-cities in China as case study was conducted, consisted of user demand items questionnaire based on the Kano model and a questionnaire on the importance of the user demand items. By utilizing the integration of quantitative analysis methods based on the Kano model and Analytic Hierarchy Process (AHP) method, the final ranking of user demands importance was obtained. Comparing with the existing literatures in terms of user demands research for Eco-city, the user demands analysis method based on Kano model of this paper, is able to reveal the influence degree of user satisfaction towards the facilities and services provided in the Eco-city. The user demands analysis method can be used for other researchers worldwide to investigate and quantitively analyze user demands according to their local development situation and preference of Eco-city. The user demands analysis results obtained through this method, can benefit different stages of Eco-city.

The heartbeat of the city

Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or “heartbeat” of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City. On aggregate, our findings show that the heartbeats of crime and crashes follow a similar pattern. We observe valleys during the night and peaks in the evening, where the intensity during a peak is 7.5 times the intensity of valleys in terms of crime and 12.3 times in terms of road accidents. Although distinct types of events, crimes and crashes reach their respective intensity peak on Friday night and valley on Tuesday morning, the result of a hyper-synchronised society. Next, heartbeats are computed for city neighbourhood ‘tiles’, a division of space within the city based on the distance to Metro and other public transport stations. We find that heartbeats are spatially heterogeneous with some diffusion, so that nearby tiles have similar heartbeats. Tiles are then clustered based on the shape of their heartbeat, e.g., tiles within groups suffer peaks and valleys of crime or crashes at similar times during the week. The clusters found are similar to those based on economic activities. This enables us to anticipate temporal traces of crime and crashes based on local amenities.

Associations between Food Policy Councils and Policies That Support Healthy Food Access: A National Survey of Community Policy Supports

Food policy councils (FPCs) are one form of community coalition that aims to address challenges to local food systems and enhance availability, accessibility, and affordability of healthy foods for local residents. We used data from the 2014 National Survey of Community-Based Policy and Environmental Supports for Healthy Eating and Active Living, a nationally representative survey of US municipalities (n = 2029), to examine the prevalence of FPCs and cross-sectional associations between FPCs and four types of supports for healthy food access (approaches to help food stores, practices to support farmers markets, transportation-related supports, and community planning documents). Overall, 7.7% of municipalities reported having a local or regional FPC. FPCs were more commonly reported among larger municipalities with ≥50,000 people (29.2%, 95% Confidence Interval (CI): 21.6, 36.8) and western region municipalities (13.2%, 95% CI: 9.6, 16.8). After multivariable adjustment, municipalities with FPCs had significantly higher odds of having all four types of supports, compared to those without FPCs (adjusted odds ratio (aOR) range: 2.4–3.4). Among municipalities with FPCs (n = 156), 41% reported having a local government employee or elected official as a member, and 46% had a designated health or public health representative. Although FPCs were uncommon, municipalities that reported having a local or regional FPC were more likely to report having supports for healthy food access for their residents.