Socio-physical liveability through socio-spatiality in low-income resettlement archetypes - A case of slum rehabilitation housing in Mumbai, India

Spatial analysis of food and water-borne diseases in Ahmedabad, India: Implications for urban public health planning

Spatial analysis of infectious diseases can play an important role in mapping the spread of diseases and can support policy making at local level. Moreover, identification of disease clusters based on local geography and landscape forms the basis for disease control and prevention. Therefore, this study aimed to examine the spatial-temporal variations, hotspot areas, and potential risk factors of infectious diseases (including Viral Hepatitis, Typhoid and Diarrhea) in Ahmedabad city of India. We used Moran's I and Local Indicators of Spatial Association (LISA) mapping to detect spatial clustering of diseases. Spatial and temporal regression analysis was used to identify the association between disease incidence and spatial risk factors. The Moran's I statistics identified presence of positive spatial autocorrelation within the considered diseases, with Moran's I from 0.09 for typhoid to 0.21 for diarrhea (p < 0.001). This indicates a clustering of affected wards for each disease, suggesting that cases were not randomly distributed across the city. LISA mapping demonstrated the clustering of hotspots in central regions of the city, especially towards the east of the river Sabarmati, highlighting key geographical areas with elevated disease risk. The spatial clusters of infectious diseases were consistently associated with slum population density and illiteracy. Furthermore, temporal analysis suggested illiteracy rates could increase risk of viral hepatitis by 13 % (95 % Confidence Interval (CI): 1.01-1.26) and of diarrhea by 18 % (95 % CI: 1.07-1.31). Significant inverse association was also seen between viral hepatitis incidence and the distance of wards from rivers. Conclusively, the study highlight the impact of socio-economic gradients, such as slum population density (indicative of poverty) and illiteracy, on the localized transmission of water and foodborne infections. The evident social stratification between impoverished and affluent households emerges as a notable contributing factor and a potential source of differences in the dynamics of infectious diseases in Ahmedabad.

Evaluation of health impacts of the improved housing conditions on under-five children in the socioeconomically underprivileged families in central India: A 1-year follow-up study protocol

Unacceptable housing conditions prevalent in Indian urban slums adversely affect the health of residents. The Government of India initiated the Basic Services to the Urban Poor (BSUP) as a sub-mission under the Jawaharlal Nehru National Urban Renewal Mission (JNNURM), to provide basic services to the urban poor. As per the available scientific literature, the health effects of such improved housing schemes for the poor have not been studied so far in India, especially in under-five children (0-5 years old) who spend most of their time indoors. The present paper describes the protocol for a follow-up research study proposed to fill this gap. This study, funded by the Indian Council of Medical Research (Sanction No. 5/8-4/9/Env/2020-NCD-II dated 21.09.2021), will be conducted in Bhopal in the central Indian province of Madhya Pradesh for over 2 years. We will recruit 320 under-five children each from Group 1 (Beneficiary families residing in the houses constructed under BSUP) and Group 2 (Slum dwelling families eligible for improved housing but who did not avail of benefit). Eligible children will be recruited in the first household visit. During the same visit, we will record clinical history, examination findings and take anthropometric measurements of participants. We will also collect data regarding socio-economic-environmental parameters of the house. During subsequent monthly follow-up visits, we will collect primary data on morbidity profile, anthropometric details and medical history over 1 year. Approval for the study was obtained from the Institutional Ethics Committee of the National Institute for Research in Environmental Health (No: NIREH/BPL/IEC/2020-21/198, dated 22/06/2020). This study will evaluate the impact of different housing conditions on the health of under-five children. Finding of this research will be beneficial in guiding future housing-related policy decisions in low- and middle-income countries.

Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality

Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively.