Access or Accessibility? A Critique of the Urban Transport SDG Indicator

Abstract

Progress towards the UN Sustainable Development Goals (SDGs) is being evaluated through the use of indicators. Despite the importance of these indicators, the academic community has done little in terms of a critical reflection on their choice, relevance, framing and operationalization. This holds for many SDG domains, also for the urban sector domain of target 11. To partially address this void, we aim to critically review the UN methodology for the urban access indicator, SDG indicator 11.2. In discussing its conceptual framing against the background of paradigm shifts in transportation planning, we argue that this indicator has a number of shortcomings. The most important one is that it is supply oriented and measures access to transportation infrastructure, rather than accessibility to activity locations. As an alternative, we develop two accessibility indicators that show substantial variation in accessibility across geographical areas. We implement all indicators for the city of Bogotá in Colombia, using a geo-information based approach. Our results show that SDG indicator 11.2 fails to represent the transport reality well. Its supply oriented focus neglects transport demand, oversimplifies the transport system and hides existing inequalities. Moreover, it does not provide useful evidence for targeting new interventions. The proposed accessibility indicators provide a more diverse, complete and realistic picture of the performance of the transport system. These indicators also capture the large spatial and socio-economic inequalities and can help to target improvements in urban transportation.

Global Ambitions, Local Contexts: Alternative Ways of Knowing the World

“…Similar to Brussel et al's [26] critique on the lack of capacity of the access indicator to capture transport inequalities, Ulbrich et al [30] Table A1 in Appendix A), point to the absence of intra-urban inequalities in their framing, and redress it by distinguishing between vertical (income-based), horizontal (social differences) and spatial inequality. For example, the perception of convenient access of the transport indicator (11.2.1), measured by a distance of 500 metres, will differ across specified groups as they have different demands and capabilities.…”

“…The conceptualization of the 232 SDG indicators is the result of an iterative and open negotiation process between multiple stakeholders and shaped by dominant development agendas [4]. Several contributions to this SI [25][26][27][28][29][30] critically examine the framing of specific indicators or the content of goals within their domain. They propose alternative framings or suggestions for inclusions, which they feel provide a more meaningful, scientifically sound and context-specific reference for monitoring and addressing the policy problem.…”

“…'Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities'. However, Brussel et al [26] claim that the access problem is not only a transport supply problem. Supply needs to be connected to people's activities and land use demand, namely the locations offering jobs, facilities (e.g., hospitals, schools, public institutions) and amenities (e.g., shops, attractions).…”

“…In their contribution, they demonstrate the shortcomings of the supply-driven framing of UN indicator 11.2.1, which neglects activity locations and context-specific issues. Drawing on contemporary knowledge in transport studies, Brussel et al [26] propose two alternative access indicators, i.e., the potential accessibility indicator and actual travel indicator, to complement the UN indicator. Both are showcased for the city of Bogotá in Colombia, a highly unequal city in terms of transport supply, spatial pattern of different socio-economic groups and activity locations [33].…”

“…Equal accessibility for all:identifying inequalities in transport accessibility for targeted policy interventions [26] Revealing an effect (inequality) [26,30] Spatial analysis to inform land-use efficient urban development strategies [42] Mapping to reduce complexities [24] Easy interpretation [42] Warning of potential pitfalls due to subjective choices made [24] Statistical methodologies can adequately identify spatial differences and clustering Identifying underrepresented groups to leave no one behind [29] Identifying underreported groups which may be at risk [29,30] Equal access for all: identifying inequalities in education [28] Identifying inequality [28] Participatory geo-spatial methodologies can produce reliable information from marginalized groups Certain inequalities are not sufficiently captured by the SDGs: alternative approaches to leave no one behind [30] Revealing effects, i.e., inequality [30] Combining multiple data streams will adequately measure the real world (data ecosystem)…”