A geo-big data approach to intra-urban food deserts: Transit-varying accessibility, social inequalities, and implications for urban planning

Su, Shiliang; Li, Zekun; Xu, Mengya; Cai, Zhongliang; Wang, Min

doi:10.1016/j.habitatint.2017.04.007

Cited by 78 publications

(37 citation statements)

References 80 publications

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“…We first included the percentage of the population aged under 19 (Children) and the percentage of the senior population aged 65 and over (Senior) as the control variables since these households are more vulnerable to unhealthy food sources (Larsen and Gilliland 2008;Wang et al 2016;Su et al 2017;Li and Ashuri 2018). Many studies have considered the effect of the education level on food accessibility (Larsen and Gilliland 2008;Laxy et al 2015;Su et al 2017;Li and Ashuri 2018). We chose the percentage of residents who have a higher education such as post-secondary certificate, diploma, or degree (High Education) as the education level indicator.…”

Section: Regression Modelsmentioning

confidence: 99%

“…Then, the mixed probability distribution of the count variable Y i can be written as: Following the previous studies, we chose eight socio-demographics as the key variables to observe the associations between food availability and neighbourhood socio-economic characteristics. We first included the percentage of the population aged under 19 (Children) and the percentage of the senior population aged 65 and over (Senior) as the control variables since these households are more vulnerable to unhealthy food sources (Larsen and Gilliland 2008;Wang et al 2016;Su et al 2017;Li and Ashuri 2018). Many studies have considered the effect of the education level on food accessibility (Larsen and Gilliland 2008;Laxy et al 2015;Su et al 2017;Li and Ashuri 2018).…”

Section: Regression Modelsmentioning

confidence: 99%

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Neighbourhood food environments revisited: When food deserts meet food swamps

Yang

Wang

Qiu

2019

Canadian Geographies / Géographies canadiennes

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This study uses service area–based coverage and various count regression methods to assess neighbourhood‐level healthy and unhealthy food environments, and food access associated with different socio‐economic groups in Edmonton, Canada. We identify three types of vulnerable neighbourhoods according to different food environments: food deserts (i.e., neighbourhoods lack sufficient access to healthy foods); food swamps (i.e., neighbourhoods have excess access to unhealthy foods); and those with overlaps of food swamps and food deserts. We also identify neighbourhoods with superior access to healthy foods (i.e., food oases). Additionally, our results from regression analyses indicate: (1) child population is negatively associated with both healthy and unhealthy food resources; (2) good access to public transportation is associated with good coverage of all healthy food outlets and convenience stores; and (3) deprived neighbourhoods with higher percentages of minority populations have better coverage of both healthy and unhealthy foods in general. The results from this study can help the City of Edmonton identify the key neighbourhoods with high potential for local business and the hotspot neighbourhoods that require particular support. Tailored strategies are proposed to effectively and efficiently improve food environments with limited resources.

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Section: Regression Modelsmentioning

confidence: 99%

Section: Regression Modelsmentioning

confidence: 99%

Neighbourhood food environments revisited: When food deserts meet food swamps

Yang

Wang

Qiu

2019

Canadian Geographies / Géographies canadiennes

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“…But in fact, geographic space is heterogeneous space, and the flow of people and things in space is mostly along the road network rather than the straight line distance. Although this is a simpler method, it completely ignores the actual traffic situation, which may lead to conflicting conclusions [42,43]. The distance on network path is based on the road network model and complex parameter setting to fit the traveling path on an actual road.…”

Section: Introductionmentioning

confidence: 99%

“…In the real-time navigation module, the two dimensions of time and space are integrated to evaluate accessibility, thus incorporating a comprehensive consideration of the layout and management of the urban spatial structure and the results of demand. Currently, web mapping API is being applied primarily to hospital accessibility analysis [24], accessibility of business centers [49], measurement of parks accessibility [50,51], approach to intra-urban food deserts [43], and so forth. This method is simple and easy to operate.…”

Section: Introductionmentioning

confidence: 99%

Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China

et al. 2019

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The accessibility of hospital facilities is of great importance not only for maintaining social stability, but also for protecting the basic human right to health care. Traditional accessibility research often lacks consideration of the dynamic changes in transport costs and does not reflect the actual travel time of urban residents, which is critical to time-sensitive hospital services. To avoid these defects, this study considered the city of Kaifeng, China, as an empirical case, and directly acquired travel time data for two travel modes to the hospital in different time periods through web mapping API (Application Program Interface). Further, based on travel time calculations, we compared five baseline indicators. For the last indicator, we used the optimal weighted accessibility model to measure hospital accessibility for each residential area. The study discovered significant differences in the frequency and spatial distribution of hospital accessibility using public transit and self-driving modes of transportation. In addition, there is an imbalance between accessibility travel times in the study area and the number of arrivals at hospitals. In particular, different modes of transportation and different travel periods also have a certain impact on accessibility of medical treatment. The research results shed new light on the accessibility of urban public facilities and provide a scientific basis with which local governments can optimize the spatial structure of hospital resources.

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“…But CDRs are not the only location‐related data that has been used. For example, data from a web mapping service application (Baidu.com) were used to calculate average travel distance to healthy food outlets in order to identify urban areas with limited food accessibility in China (Su, Li, Xu, Cai, & Weng, ). Car GPS data have been used to map the spatio‐temporal distribution of pollution emissions from traffic (Huang, Cao, Jin, Yu, & Huang, ; Luo et al, ).…”

Section: Big Data For Developmentmentioning

confidence: 99%

Positive deviance, big data, and development: A systematic literature review

Albanna

Heeks

2018

E J Info Sys Dev Countries

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Positive deviance is a growing approach in international development that identifies those within a population who are outperforming their peers in some way, eg, children in low-income families who are well nourished when those around them are not. Analysing and then disseminating the behaviours and other factors underpinning positive deviance are demonstrably effective in delivering development results. However, positive deviance faces a number of challenges that are restricting its diffusion.In this paper, using a systematic literature review, we analyse the current state of positive deviance and the potential for big data to address the challenges facing positive deviance. From this, we evaluate the promise of "big data-based positive deviance": This would analyse typical sources of big data in developing countries-mobile phone records, social media, remote sensing data, etc-to identify both positive deviants and the factors underpinning their superior performance. While big data cannot solve all the challenges facing positive deviance as a development tool, they could reduce time, cost, and effort; identify positive deviants in new or better ways; and enable positive deviance to break out of its current preoccupation with public health into domains such as agriculture, education, and urban planning. In turn, positive deviance could provide a new and systematic basis for extracting real-world development impacts from big data. KEYWORDS big data, developing countries, machine learning, mobile data, positive deviance, systematic literature review 1 | INTRODUCTIONMany development practitioners continue to use a traditional "needs-based" approach to development, involving top-down identification of needs and problems, and the external imposition of solutions that meet those needs. This type of approach can work well in addressing specific technical challenges. But it works much less well where development requires learning and behavioural change by beneficiary groups, something which necessitates much greater knowledge of and engagement with beneficiary communities (Nel, 2018;Pascale, Sternin, & Sternin, 2010;Saïd Business School, 2010;Singhal, 2011). As a result, more bottom-up "asset-based" approaches have come into existence, which capitalize on a community's inherent assets and capabilities-including knowledge-in solving development problems. Positive deviance (PD) is one such assetbased approach. It is based on the observation that in every group or community, a few individuals use uncommon practices and behaviours to achieve better solutions to problems than their peers who face the same challenges and barriers (Pascale et al., 2010). Those individuals are referred to as "positive deviants" (PDs), and adopting their solutions on a wider basis is referred to as the PD approach.The term "positive deviance" was first used in 1976 to describe a practical strategy for the design of food supplementation programmes in Central America, a strategy that was derived endogenously rather than exogenously through identify...

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A geo-big data approach to intra-urban food deserts: Transit-varying accessibility, social inequalities, and implications for urban planning

Cited by 78 publications

References 80 publications

Neighbourhood food environments revisited: When food deserts meet food swamps

Neighbourhood food environments revisited: When food deserts meet food swamps

Spatial Accessibility to Hospitals Based on Web Mapping API: An Empirical Study in Kaifeng, China

Positive deviance, big data, and development: A systematic literature review

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