Fast-developing Cities Research Articles

Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh

Municipal solid waste(MSW) management has been a growing problem in fast-developing cities. A considerable amount of solid waste is generated daily and disposed anywhere, which creates an unhealthy environment. This study aims to develop a model to determine household solid waste (HSW) generation using multiple linear regression and identify suitable landfill sites to ensure proper MSW disposal in Rangpur City, Bangladesh. Socioeconomic variables data like average monthly income, educational level, family size, age of family head, and average HSW generation per day were collected from 381 respondents through stratified random sampling with a 95% confidence level. Multi-criteria decision analysis (MCDA) was performed using variables like surface water, slope, road network, and land use through GIS and remote sensing to find suitable landfill sites. Results of the model show no multicollinearity as the variance inflation factor was estimated to be less than 2 for each independent variable. Furthermore, the model provides a moderate overall fit because of the coefficient of determination (R2 = 0.661), which denotes the independent variables' predictive capability. The results also demonstrate that family size and education are the most critical variables in predicting waste generation because of the values of coefficients 122.39 and - 184.72, respectively. This study also illustrated suitable landfill sites through MCDA, which can be a useful resource for the city authority to ensure environmental sustainability by implementing effective strategies for proper MSW management.

Trip Generation for shopping malls in Developing Cities

This study develops trip generation models for shopping malls in Mumbai, one of the fast-developing cities. A total of eighteen shopping malls are selected to develop trip generation models using regression analysis. The factors that found significant in weekdays and weekend trip attraction models include built-up area, number of screen and seats in multiplex, number of kiosk and stores, number of parking spaces and gross floor area. Based on the video data collected at two shopping malls, it was found that the peak period is from 6 PM to 9 PM. The findings of this study can be used analyze the impacts of newly constructed shopping malls and plan transportation infrastructure in and around a shopping mall.

Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data

The dynamic monitoring of home and workplace distribution is a fundamental building block for improving location-based service systems in fast-developing cities worldwide. Inferring these places is challenging; existing approaches rely on labor-intensive and untimely survey data or ad hoc heuristic assignment rules based on the frequency of appearance at given locations. Motivated by the regularities in human behavior, we propose a novel method to infer the home, workplace, and third place based on an individual’s spatial-temporal patterns inferred from Call Detail Records. To capture the individual regularity, our method develops, for each person-location, the probability distribution that the person will appear in that location at a specific time of day using geo-temporal travel patterns a panel of individuals. To reveal the collective regularity, we apply eigen-decomposition to the matrix of historical geo-temporal data. Unsupervised machine learning techniques are then used to extract commonalities across locations for different groups of travelers, making inferences, such as home and workplace. Testing the methodology on real-world data with known location labels shows that our method identifies home and workplace with significant accuracy, improving upon the best practices in the literature by 79% and 34%, respectively. The methodology proposed is computationally efficient and is highly scalable to other real-world applications with historical tracking data. It provides a basis to improve location-based services, such as mobile commerce, social events recommendations, and urban transit design.

Life between the city and the village: Scaling analysis of service access in Indian urban slums

Abstract The emergence of India as an urbanized nation is one of the most significant socioeconomic and political processes of the 21st century. An essential feature of India’s urbanization has been the growth and persistence of slums in its fast-developing cities. Whether living conditions in Indian slums constitute a path to human development or a poverty trap to much of their population is therefore an issue of vital importance. Here, we analyse data from the Census of India using the framework of urban scaling to systematically characterize the relative properties of Indian urban slums, focusing on attributes of neighborhoods such as access to basic services like water, sanitation, and electricity. We find that slums in larger cities attract more migrants and offer, on average, higher levels of service access than those in smaller cities. We posit that these outcomes are a consequence of both agglomeration effects in larger cities and sub-linear scaling of infrastructure as predicted by urban scaling theory. We also find consistent under-performance in service access in slums, in comparison with non-slum neighborhoods in the same cities. However, urban slums, on average, offer greater access to services than rural areas. This situation, which we quantify systematically, may help explain why larger Indian cities have remained attractive to rural populations in terms of living standards, beyond the need for an economic income premium. Finally, based on the analysis of scaling residuals, we construct a nationwide urban geography of slums, essentially finding that public service delivery in the slums of northern, central, and eastern Indian cities is, on average, poorer than slums in cities of southern and western India. Overall, these findings suggest that urban policy needs to confront two distinct kinds of urban inequity – across neighbourhoods within cities, and across city scales and levels of development.

Risks related to heavy metal pollution in urban construction dust fall of fast-developing Chinese cities.

Urban construction is a major contributor to air pollution, but few studies have examined heavy metal pollution in urban areas caused by construction dust fall. We measured the concentrations of Cr, Ni, Cu, Zn, Pb, Cd, and Hg and particle size distribution in dust fall from various construction activities in seven fast-developing cities in China and conducted a health risk assessment. Mean metal concentrations in construction dust fall were on the order of Zn (246.3mg/kg)>Cr (94.2mg/kg)>Pb (56.5mg/kg)>Cu (53.6mg/kg)>Ni (22.8mg/kg)>Cd (0.68mg/kg)>Hg (0.08mg/kg). Cu and Zn were positively correlated in areas of subway and building construction, and Pb and Cd were positively correlated in areas of road construction, likely because of the materials specific to these activities. Enrichment factors for heavy metals at all sampling sites were on the order of Cd (10.4)>Zn (6.37)>Cu (4.25)>Pb (3.84)>Hg (2.41)>Cr (2.02)>Ni (1.32). The enrichment factors for all metals except Zn indicated that heavy metal pollution was highest in road construction, followed by building and subway construction. Non-carcinogenic risks to children (hazard index >1) were 1.01-1.08 in four of the seven sampling sites, indicating possible risk from deposition of construction dust fall. In contrast, the hazard index for adults was <1at the seven cities and total carcinogenic risks (<1×10-6) were at acceptable levels. An integrated ecological risk assessment demonstrated that heavy metal particles in construction dust fall in two of the cities (Shijiazhuang and Qingdao) were likely to be suspended in the atmosphere. Our study of heavy metal pollution in construction dust fall provides data on ecological and human health impacts and suggests that extensive measures are required to control construction dust fall in China.

Dynamic material flow and stock analysis of residential buildings by integrating rural–urban land transition: A case of Shanghai

In rapidly developing countries, such as China, considerable quantities of construction materials have been mobilized with the fast expansion of urban areas. The rural to urban land use transition, especially the upgrade and redevelopment of original rural areas, is a noted driver of material flows. This rural–urban land transition should be incorporated into material flow and stock analyses to provide a more accurate analysis. In this study, a dynamic material flow and stock model that integrates the historical rural–urban land transition was developed to explore the quantity of material stocks and demolition waste from residential buildings in Shanghai—the largest megacity in China from 1950 to 2100. Our results show that the material stocks from residential buildings in Shanghai increased 41-fold from 1950 to 2010, about 957 MMT (million metric tons), and is estimated to be saturated around 2040. Material stocks have experienced asynchronized growth in rural areas, central urban areas, and rural–urban land transition zones (RULT zones) in Shanghai. Until 2040, the RULT zones in Shanghai will be the largest material repository (62%), followed by central urban areas (20%), and rural areas (18%). The amount of demolition waste, which accounted for 10 MMT in 2010, is expected to peak at 29 MMT in 2060s. This suggests the need for a deliberate investment plan to increase waste treatment capacity. In addition, the dominant component of demolition waste will shift from brick to concrete after 2020s. The RULT zones will contribute two-thirds of demolition waste to Shanghai until the 2060s. If we do not consider the reality of the physical status of buildings in RULT zones, the demolition waste will be underestimated by the maximum of 57% in 2003 in urban areas. The key findings on the trend of construction and demolition (C&D) waste generation and the significant contribution of RULT zones can be used as a reference for the strategic planning of treatment facilities. This research framework can be also used to estimate the amount of C&D waste in other fast-developing cities in China and other countries.

Community-Engaged Research for the Promotion of Healthy Urban Environments: A Case Study of Community Garden Initiative in Shanghai, China.

The importance of community gardens in a healthy urban environment has been extensively documented, while the garden building involving communities has not been much explored in fast-developing cities. This study examines community engagement in garden building activities in a rapid urbanization context, aiming to explore the application of community-engaged research methods for the promotion of neighbourhood environments. The Community Garden Initiative consisting of an array of progressive actions is formulated by the research team, featuring a process of increasing involvement of community members and decreasing intensity of external interventions. These activities have been launched based on community-university partnerships in Shanghai since 2014, synchronising with a transformation of urban regeneration paradigm in China where people-oriented approaches are more emphasized. Five actions covering 70 community gardens are analysed through surveys on participants’ attitudes and perceptions towards the activities. The results of the study presented people’s rapid acceptance of participation in public affairs, reflected possible measures to promote public participation, and confirmed the positive impacts of the garden building on the neighbourhood environmental health as well as on the community-building. Taking into account that residents generally lack the consciousness and capacities required to implement actions at the initial stage of community engagement, we proposed in the conclusion to start with external interventions and capacity buildings carried out by professionals as a supplement to the ‘community-driven’ principle of CBPR methods.

Life Between the City and the Village: Comparative Analysis of Service Access in Indian Urban Slums

The emergence of India as an urbanized nation is one of the most significant socioeconomic and political processes of the 21st century. An essential feature of India’s urbanization has been the growth and persistence of informal settlements (slums) in its fast-developing cities. Whether living conditions in Indian urban slums constitute a path to human development or a poverty trap is therefore an issue of vital importance. Here, we characterize census data using the framework of urban scaling to systematically characterize the relative properties of Indian urban slums, focusing on attributes of neighbourhoods such as access to basic services like water, sanitation, and electrical power. We find that slums in larger cities offer systematically higher levels of service access than those in smaller cities. Perhaps as expected, we also find consistent underperformance in service access in slums in comparison with non-slum neighbourhoods in the same cities. However, urban slums, on average, offer greater access to services than neighbourhoods in rural areas. This situation, which we quantify systematically, may help explain why Indian larger cities have remained attractive to rural populations in terms of living standards, beyond the need for an economic income premium.

Comparing and modeling sedimentary profiles of elemental carbon and polycyclic aromatic hydrocarbons between early- and newly-urbanized areas in Shanghai

Rapid urbanization created unique urban environment with a characteristic of dramatic modification of land cover, consequently causing profound perturbations in the transport and fate of pollutants in urban ecosystem. Taking a hyper-urbanization city (Shanghai) as an example to reveal the influence of urbanization development on pollutant footprint, this study reconstructed and compared historical evolutions of elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) based on two lake sediment cores (DSL: Dianshan Lake; LXL: Luxun Lake) from early- and newly-urbanized areas, respectively. Historical fluxes of EC and total PAH (Σ16PAHs) showed similar and sharply fluctuant increases occurring after the 1950s in the DSL core later than the LXL core after the 1900s. In modern times (after 2000), the mean fluxes of EC and Σ16PAHs in the LXL core were 2.68- and 1.38-fold greater than those in the DSL core, respectively, indicating the stronger influence from more intensive human activities and longer industrial history in early urbanized area. Based on the significant correlations among socioeconomic factors with EC and Σ16PAH fluxes, the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) models were successfully constructed, revealing that significance of these driving factors were in the order of population > the proportion of heavy industry > coal consumption > gross domestic product (GDP) per capita > vehicle amount. In general, the obvious discrepancy in historical stage and intensity of sedimentary EC and PAH accumulations implied that some newly fast-developing cities still have a chance to adjust urban development strategy to avoid more serious pollution.

Examining the transition processes in the Pearl River Delta polycentric mega-city region through the lens of corporate networks

This paper presents an analysis of the shifting spatial organization of the Pearl River Delta (PRD), a large-scale urbanized region bordering Hong Kong that includes major cities, such as Guangzhou and Shenzhen, alongside a range of other fast-developing cities and towns. Our methodology measures and compares the different networking components of the PRD's spatial organization and uses data on the geography of firms' networks as observed in the links between locations of headquarters and subsidiaries in 2001, 2008 and 2013. We examine whether there has been a shift towards integrated polycentricity in the unfolding spatial organization of this ‘workshop of the world’ through functional polycentricity and a typology of the geographies of these links. The results suggest complex interaction processes in the PRD in which network interactions significantly increased in 2001–08 and slightly declined in 2008–13 (with the exception of manufacturing links, which are increasingly managed from headquarters in Guannei, Shenzhen). We argue that the PRD is increasingly characterized by a functional polycentric and cross-regional interaction pattern for market-oriented sectors; although more regionalized, networks continue to dominate sectors that have higher proportions of state-owned enterprises.

An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China

In order to investigate the effect of meteorological factors on submicron particle (ultrafine particle (UFP) and particulate matter 1.0 (PM1.0)) concentrations under busy traffic conditions, a model study was conducted in Hangzhou, a city with a rapid increase of on-road vehicle fleet in China. A statistical model, Autoregressive Integrated Moving Average (ARIMA), was used for this purpose. ARIMA results indicated that barometric pressure and wind velocity were anti-correlated and temperature and relative humidity were positively correlated with UFP number concentrations and PM1.0 mass concentrations (p<0.05). These data suggest that meteorological factors are significant predictors in forecasting roadside atmospheric concentrations of submicron particles. The findings provide baseline information on the potential effect of meteorological factors on UFP and PM1.0 levels on a busy viaduct with heavy traffic most of the day. This study also provides a framework that may be applied in future studies, with large scale time series data, to predict the impact of meteorological factors on submicron particle concentrations in fast-developing cities, in China.

Follow-up study of school-age children's sleep quality in Shanghai

China has undergone massive socioeconomic change during the past several years, and its impact on children's sleep is still unrecognized. Shanghai, as one of typical economically fast-developing cities, was chosen as observational city in this study, which was designed to explore trends in sleep quality in Shanghai school-aged children and related high risk factors on sleep quality. Totally 884 fifth grade school-aged students were sampled by stratified cluster random sampling method from 10 primary schools of Shanghai in 2005, then four years later in 2009, 2161 same grade students were sampled from the same schools. Chinese version of Children's Sleep Habits Questionnaire (CSHQ) was used to evaluate 8 sleep quality parameters among those children, and high risk factors on school-aged children's sleep quality were investigated as well. The prevalence of poor sleep quality decreased from 29.2% in 2005 to 27.9% in 2009, and among 8 sleep quality parameters, bedtime resistance decreased from 33.1% to 28.7%, sleep anxiety from 50.6% to 39.8%, night waking from 25.2% to 21.5%, and parasomnia from 51.2% to 45.8%. The factors, such as heavier homework burden, longer daily computer use, bright light during sleep, cosleeping, existence of chronic disease and irregular sleep habits of parents, were associated with poor sleep quality of school-aged children after adjusting for children's age, gender, and family social-economic status. Part of sleep quality parameters improved during the past 4 years, but current situation is still tough with more than 1/4 poor sleep quality children. Helping children to develop good sleep hygiene as well as educating parents how to shape children's regular sleep habits might be effective methods to improve children's sleep quality.

Weekly peak load forecasting for fast-developing cities

The fast pace of the development activities in the Kingdom of Saudi Arabia (KSA) has resulted in the pressing need for more dependable forecasts for expanding industrial and residential electrical demands. Rapid-developing countries like the KSA experience a large, varying load growth rate, which has dynamic rather than static characteristics. The authors present a method for developing adequate time series models for the dynamic characteristics of the electrical power demands of a typical rapid-developing electric utility from actual five-year load demand time series. Two models have been developed. The first uses a long-trend component modelled by a polynomial function and a seasonal-cyclic component modelled by Fourier expansion. The second uses an autoregressive time series model for the seasonal-cyclic component. These models have been used to generate weekly peak-load forecasts for one year ahead.