Strategic Planning for Smart City Development: Assessing Spatial Inequalities in the Basic Service Provision of Metropolitan Cities

ABSTRACT

Spatial inequality in basic service provision is a key challenge for smart city development strategies. With a focus on walkable accessibility, this paper proposes a new methodological approach to analyzing spatial inequalities in metropolitan cities. In addition to proving the effectiveness of the proposed methodology by using New York City as an empirical case, the insights offered by the analysis contribute to an improvement in the current understanding of the negative implications that spatial inequalities can have on public health. Policies concerning local public service rearrangement should consider spatial inequalities to promote more effective forms of public participation both in the United States and globally.

KEYWORDS:

• Smart city development
• urban sustainability
• spatial inequality
• assessment
• neighborhood walkability

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1 Spatial inequality is only one of the multiple dimensions of inequality, but it is of particular significance, especially when spatial and regional divisions are consistent with political and ethnic tensions that undermine social and political stability (Kanbur and Venables, 2005). Spatial inequality dimensions have spanned the social sciences. Some scholars investigate this issue based on the choice of jobs and educational institutions (Tsou et al., 2005). Others have examined the distribution income of spatial inequality (Lessmann, 2014). With considerations of equity issues explored by Hay (1995), spatial equity can be understood by equity access to public service, measured by accessibility (Talen and Anselin, 1998; Yin et al., 2018). This paper evaluates spatial inequality by linking the concept of equity with spatial analysis of accessibility toward daily urban amenities.

2 American FactFinder/American Community Surveys: 2010–2014 (5-year Estimates)

3 β_i has empirical values in this study: β(Gr) = 0.3, β(Bu) = 0.15, β(Su) = 0.3, β(Gs) = 0.25).

Additional information

Funding

This work was supported by grants from National Natural Science Foundation of China [grant numbers 71834005, 71673232]; the Research Grant Council of Hong Kong [grant numbers CityU 11271716; CityU 11612620]; the Strategic Priority Research Program of Chinese Academy of Sciences, Pan-Third Pole Environment Study for a Green Silk Road (Pan-TPE) [grant number XDA20040400]; the CityU Internal Funds [grant numbers 9680195, 9610386]; and the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) [grant number: 311020003‬].

Notes on contributors

Mengbing Du

Mengbing Du, Mengbing Du is a PhD Student at City University of Hong Kong.

Xiaoling Zhang

Xiaoling Zhang, is a professor at City University of Hong Kong.

Luca Mora

Luca Mora, is an associate professor of urban innovation at the Business School of Edinburgh Napier University.