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|Title:||CHILDREN’S INDEPENDENT MOBILITY IN URBAN NEIGHBOURHOODS OF INDIA: A CASE OF KOLKATA|
|Keywords:||CIM;Urban Mobility;Child Development;City Traffic;ArcGIS 10.7;Cognitive Skills|
|Abstract:||Children’s independent mobility (CIM) has a direct impact on improving a child’s cognitive skills, environmental resilience and connection with his/her neighbourhood. It also offers opportunities for an increased physical activity level among children. Despite these benefits, several international studies have indicated a widespread decline in the degree of CIM over the last three decades. At the same time, there is a sharp rise in overweight and obesity issues among children, especially in developing countries. Multidisciplinary researchers have argued that the built environment and socio-cultural transformations due to the growing pressure of urbanisation have resulted in children’s restrictions on freedom to move. However, owing to the heterogeneous character of these environmental factors, the current state of research remains inconsistent in terms of their effect on CIM. It is attributed to the lack of similar methodologies used and the non-existence of standardised measurement for CIM. Additionally, it was found that a majority of these studies were carried out in developed countries, especially Europe, presenting a semi-picture of a global problem at hand. Narratives on CIM from developing countries remain limited and under-researched, especially in India. To address the gap, this study, for the first time, aims to generate empirical evidence on the current levels of CIM and the influencing environmental factors within an Indian urban neighbourhood context. It approaches the topic adopting the previously used socio-ecological framework for examining the multi-level influences of socio-demographic, built and social environmental factors on children’s mobility. The study follows a comparative case study approach as a methodology by taking the case of five neighbourhoods across three distinct typologies in the eastern metropolitan city of Kolkata. Each child’s neighbourhood was operationalised using the buffer method. The definition of ‘neighbourhood’ was adopted as an area within an 800 m pedestrian network buffer around each child’s home. Further, the study intended to include school as one of the destinations within a child’s local surroundings; therefore, only children and their families residing within 800m of school were considered. The purposive sampling technique was employed to collect the cross-sectional data from 673 children aged 7-12 years and their parents belonging to middle-income households. A total of seven schools agreed to participate in the study across five distinct neighbourhoods. These five neighbourhoods are broadly categorised under three iv typologies of low-rise (Khidirpur and Behala), mid-rise (Salt Lake Sector 1 and 3) and high-rise (Action Area I, New Town) neighbourhoods. To collect data on CIM and neighbourhood environmental variables, tools were first finalised and validated by conducting expert reviews and pilot testing in a neighbourhood context of Delhi. This process assisted in contextualising the existing scales for measuring CIM licence and destination within Indian context. At the same time, a total of seven neighbourhood built and social environment variables were finalised. The built environment (BE) variables included land-use mix, street connectivity, traffic exposure and residential density. In order to obtain data for calculating these variables, a GIS dataset containing street networks and land-use were developed for each neighbourhood separately using ArcGIS 10.7. On the other hand, social environment (SE) variables which included neighbourhood cohesion, connection and safety, were obtained using an online survey method with parents. Further, semi-structured interviews were also conducted with consenting and available parents to obtain insights on their overall understanding of CIM as a concept. For analysis, both CIM licence (obtained from parents) and CIM destination (obtained from children) were analysed separately. Chi-square tests were conducted to check the association between both the measures of CIM and socio-demographic variables (child’s age and gender, parent’s gender, qualification and employment status, family type and vehicular ownership). Multi-nominal logistic regression in case of CIM licence and binary logistic regression in case of CIM destination was employed for assessing their relationship with neighbourhood BE and SE variables. Lastly, data obtained from parental interviews were analysed using content analysis. The majority of high CIM levels (CIM licence scores and CIM destination ratios) were obtained from low-rise neighbourhoods. In contrast, the majority of low CIM levels were obtained from high-rise neighbourhoods. Overall, the results reflected a declining trend in CIM across all neighbourhood typologies. Even among neighbourhoods with higher CIM levels, the overall share was less than 50%, raising alarming concern for children’s mobility experience in Kolkata. Among socio-demographic factors, except child’s age, other variables (child’s gender, parent’s gender, qualification, employment status and family type) were not found to be significantly impacting CIM. The most liberal independent mobility licence across all neighbourhoods was ‘going to places within walking distances other than school’. This was v most prevalent in low-rise neighbourhoods, with more than half of children granted this permission. The least liberal licence across all neighbourhoods was ‘using public transport alone’. In terms of destination, the highest percentage of independent mobility trips was for parks or playgrounds (40%), followed by the local street (36%). Interestingly, only 17% of children enjoyed independent commuting to school, a figure relatively low compared to various developed countries. In terms of the relationship between CIM and neighbourhood BE and SE, notable findings emerged. ‘Land-use mix’ was revealed as an influential variable impacting CIM positively. It was further supported by parental interviews, which revealed a positive relationship between walkable child-specific destinations and higher CIM. On the other hand, ‘traffic exposure’ was found to be negatively impacting CIM, especially within low-rise neighbourhoods. ‘Social cohesion’ and ‘neighbourhood safety’ were the only SE variables having a significant effect on CIM within all neighbourhoods, respectively. Overall, parental CIM licence tends to be more affected by the neighbourhood BE while children’s actual independent mobility tends to be affected by neighbourhood SE. The study's most novel findings were the emergence of neighbourhood ‘verticality’ and ‘spatial growth’ as important predictors of CIM. The findings suggested that low-rise or mid-rise residential neighbourhoods offer a higher degree of safety perception among parents or caregivers than high-rise neighbourhoods. Similarly, organic spatial growth with a high land-use mix than grid or geometrically planned growth was found to be more beneficial for a higher CIM. Further, findings also reveal that a sustainable neighbourhood that offers comfort to families to stay for generations is an indicator of a healthy and positive social environment that adds to CIM's supportive elements. Overall, this thesis weaves a narration unfolding different macro and microsystems layers affecting children’s independent explorations across distinct neighbourhood typologies. Concludingly, this study, by providing a case of one megacity, initiates an intense discourse on children’s mobility in a country with enormous diversities within social, cultural and urban environment structures. Simultaneously, it calls for large-scale data on CIM identifying patterns for creating an effective child-friendly environment for independent mobility at local levels.|
|Research Supervisor/ Guide:||Raheja, Gaurav|
|Appears in Collections:||DOCTORAL THESES (A&P)|
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