Google-truthing to assess hot spots of food retail change: A repeat cross-sectional Street View of food environments in the Bronx, New York

Retrospective analysis of regional and metropolitan school food environments using Google Street View: A case study in New South Wales, Australia with youth consultation

ISSUE ADDRESSED

Food environments surrounding schools have a strong influence on the adolescent's food choices. Moreover, the prevalence of diet-related chronic diseases is higher in regional than metropolitan areas in Australia. Understanding school food environments in these different settings is crucial for informing future strategies to improve adolescent health.

METHODS

Google Street View was used to identify food outlets within 1.6 km around all secondary schools in Wagga Wagga and Blacktown in New South Wales which were selected as regional and metropolitan case study areas. Based on food outlet type, healthfulness categories were assigned, and Chi-squared tests were performed. The Health Advisory Panel for Youth at the University of Sydney (HAPYUS) were engaged to obtain their perspectives on findings.

RESULTS

Unhealthful food outlets were consistently most prevalent around schools in Wagga Wagga and Blacktown over 17 years. In 2023, these were predominantly restaurants (19.4% vs. 21.1%), cafés (16.8% vs. 11.1%), fast-food franchise outlets (15.1% vs. 17.4%) and independent takeaway stores (14.1% vs. 9.6%). No significant difference in healthfulness between regional and metropolitan areas was found. Youth advisors recognised price and social reasons as major contributors to food choices.

CONCLUSIONS

Google Street View was used as a novel resource to examine school food environments in regional and metropolitan areas which have remained consistently unhealthful for nearly two decades. SO WHAT?: Unhealthful school food environments may encourage poor diets and exacerbate rates of adolescent overweight and obesity. Critical government action is needed to improve school food environments.

Developing the DIGIFOOD Dashboard to Monitor the Digitalization of Local Food Environments: Interdisciplinary Approach

BACKGROUND

Online food delivery services (OFDS) enable individuals to conveniently access foods from any deliverable location. The increased accessibility to foods may have implications on the consumption of healthful or unhealthful foods. Concerningly, previous research suggests that OFDS offer an abundance of energy-dense and nutrient-poor foods, which are heavily promoted through deals or discounts.

OBJECTIVE

In this paper, we describe the development of the DIGIFOOD dashboard to monitor the digitalization of local food environments in New South Wales, Australia, resulting from the proliferation of OFDS.

METHODS

Together with a team of data scientists, we designed a purpose-built dashboard using Microsoft Power BI. The development process involved three main stages: (1) data acquisition of food outlets via web scraping, (2) data cleaning and processing, and (3) visualization of food outlets on the dashboard. We also describe the categorization process of food outlets to characterize the healthfulness of local, online, and hybrid food environments. These categories included takeaway franchises, independent takeaways, independent restaurants and cafes, supermarkets or groceries, bakeries, alcohol retailers, convenience stores, and sandwich or salad shops.

RESULTS

To date, the DIGIFOOD dashboard has mapped 36,967 unique local food outlets (locally accessible and scraped from Google Maps) and 16,158 unique online food outlets (accessible online and scraped from Uber Eats) across New South Wales, Australia. In 2023, the market-leading OFDS operated in 1061 unique suburbs or localities in New South Wales. The Sydney-Parramatta region, a major urban area in New South Wales accounting for 28 postcodes, recorded the highest number of online food outlets (n=4221). In contrast, the Far West and Orana region, a rural area in New South Wales with only 2 postcodes, recorded the lowest number of food outlets accessible online (n=7). Urban areas appeared to have the greatest increase in total food outlets accessible via online food delivery. In both local and online food environments, it was evident that independent restaurants and cafes comprised the largest proportion of food outlets at 47.2% (17,437/36,967) and 51.8% (8369/16,158), respectively. However, compared to local food environments, the online food environment has relatively more takeaway franchises (2734/16,158, 16.9% compared to 3273/36,967, 8.9%) and independent takeaway outlets (2416/16,158, 14.9% compared to 4026/36,967, 10.9%).

CONCLUSIONS

The DIGIFOOD dashboard leverages the current rich data landscape to display and contrast the availability and healthfulness of food outlets that are locally accessible versus accessible online. The DIGIFOOD dashboard can be a useful monitoring tool for the evolving digital food environment at a regional scale and has the potential to be scaled up at a national level. Future iterations of the dashboard, including data from additional prominent OFDS, can be used by policy makers to identify high-priority areas with limited access to healthful foods both online and locally.

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

A longitudinal analysis of the fluctuation of food stores in Belo Horizonte, Minas Gerais, Brazil

BACKGROUND

Changes in food environments have the potential to affect consumption, nutritional status, and health, and understanding these changes is of utmost importance. This study, therefore, aimed to examine the fluctuation of food stores that sell fruits and vegetables over five years in the health promotion service area of Primary Health Care (PHC) in Belo Horizonte, Minas Gerais, Brazil.

METHODS

This was an ecological study that used data from a food environment audit conducted in the realm of Brazilian PHC. Buffers of 1 mile (equivalent to 1600 m) were created around health promotion services to define food environments. All food stores and open-air food markets that sold fruits and vegetables (FV) within this buffer area were considered eligible. The data collection was performed during two periods: the baseline, in 2013, and after five years, in 2018. This study compares the fluctuation by the type of stores and according to the health vulnerability index (HVI).

RESULTS

After 5 years, 35.2% of the stores were stable; 154 stores were closed, and 155 were opened. The stability was greater in low-vulnerability areas, and the fluctuation differed by type of store only for areas with high vulnerability. The number of supermarket decreased in high HVI territories; and local stores, showed greater stability when compared to specialized FV markets.

CONCLUSIONS

The differences in store fluctuations according to the vulnerability of areas demonstrate the importance of food supply policies considering the local characteristics to reduce inequities of access to healthy foods.

Validation of retail food outlet data from a Danish government inspection database

BACKGROUND

Globally, unhealthy diet is one of the leading global risks to health, thus it is central to consider aspects of the food environment that are modifiable and may enable healthy eating. Food retail data can be used to present and facilitate analyses of food environments that in turn may direct strategies towards improving dietary patterns among populations. Though food retail data are available in many countries, their completeness and accuracy differ.

METHODS

We applied a systematically name-based procedure combined with a manual procedure on Danish administrative food retailer data (i.e. the Smiley register) to identify, locate and classify food outlets. Food outlets were classified into the most commonly used classifications (i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores and miscellaneous) each divided into three commonly used definitions; narrow, moderate and broad. Classifications were based on branch code, name, and/or information on the internal and external appearance of the food outlet. From ground-truthing we validated the information in the register for its sensitivity and positive predictive value.

RESULTS

In 361 randomly selected areas of the Capital region of Denmark we identified a total of 1887 food outlets compared with 1861 identified in the register. We obtained a sensitivity of 0.75 and a positive predictive value of 0.76. Across classifications, the positive predictive values varied with highest values for the moderate and broad definitions of fast food, convenience stores and supermarkets (ranging from 0.89 to 0.97).

CONCLUSION

Information from the Smiley Register is considered to be representative to the Danish food environment and may be used for future research.

Children's Community Nutrition Environment, Food and Drink Purchases and Consumption on Journeys between Home and School: A Wearable Camera Study

Children's community nutrition environments are an important contributor to childhood obesity rates worldwide. This study aimed to measure the type of food outlets on children's journeys to or from school, children's food purchasing and consumption, and to determine differences by ethnicity and socioeconomic status. In this New Zealand study, we analysed photographic images of the journey to or from school from a sample of 147 children aged 11-13 years who wore an Autographer camera which recorded images every 7 s. A total of 444 journeys to or from school were included in the analysis. Camera images captured food outlets in 48% of journeys that had a component of active travel and 20% of journeys by vehicle. Children who used active travel modes had greater odds of exposure to unhealthy food outlets than children who used motorised modes; odds ratio 4.2 (95% CI 1.2-14.4). There were 82 instances of food purchases recorded, 84.1% of which were for discretionary foods. Of the 73 food and drink consumption occasions, 94.5% were for discretionary food or drink. Children on their journeys to or from school are frequently exposed to unhealthy food outlets. Policy interventions are recommended to limit the availability of unhealthy food outlets on school routes.

Assessing changes in the food retail environment during the COVID-19 pandemic: opportunities, challenges, and lessons learned

BACKGROUND

COVID-19 mitigation strategies have had an untold effect on food retail stores and restaurants. Early evidence from New York City (NYC) indicated that these strategies, among decreased travel from China and increased fears of viral transmission and xenophobia, were leading to mass closures of businesses in Manhattan's Chinatown. The constantly evolving COVID -19 crisis has caused research design and methodology to fundamentally shift, requiring adaptable strategies to address emerging and existing public health problems such as food security that may result from closures of food outlets.

OBJECTIVE

We describe innovative approaches used to evaluate changes to the food retail environment amidst the constraints of the pandemic in an urban center heavily burdened by COVID-19. Included are challenges faced, lessons learned and future opportunities.

METHODS

First, we identified six diverse neighborhoods in NYC: two lower-resourced, two higher-resourced, and two Chinese ethnic enclaves. We then developed a census of food outlets in these six neighborhoods using state and local licensing databases. To ascertain the status (open vs. closed) of outlets pre-pandemic, we employed a manual web-scraping technique. We used a similar method to determine the status of outlets during the pandemic. Two independent online sources were required to confirm the status of outlets. If two sources could not confirm the status, we conducted phone call checks and/or in-person visits.

RESULTS

The final baseline database included 2585 food outlets across six neighborhoods. Ascertaining the status of food outlets was more difficult in lower-resourced neighborhoods and Chinese ethnic enclaves compared to higher-resourced areas. Higher-resourced neighborhoods required fewer phone call and in-person checks for both restaurants and food retailers than other neighborhoods.

CONCLUSIONS

Our multi-step data collection approach maximized safety and efficiency while minimizing cost and resources. Challenges in remote data collection varied by neighborhood and may reflect the different resources or social capital of the communities; understanding neighborhood-specific constraints prior to data collection may streamline the process.

Beyond food swamps and food deserts: exploring urban Australian food retail environment typologies

OBJECTIVE

'Food deserts' and 'food swamps' are food retail environment typologies associated with unhealthy diet and obesity. The current study aimed to identify more complex food retail environment typologies and examine temporal trends.

DESIGN

Measures of food retail environment accessibility and relative healthy food availability were defined for small areas (SA2s) of Melbourne, Australia, from a census of food outlets operating in 2008, 2012, 2014 and 2016. SA2s were classified into typologies using a two-stage approach: (1) SA2s were sorted into twenty clusters according to accessibility and availability and (2) clusters were grouped using evidence-based thresholds.

SETTING

The current study was set in Melbourne, the capital city of the state of Victoria, Australia.

SUBJECTS

Food retail environments in 301 small areas (Statistical Area 2) located in Melbourne in 2008, 2012, 2014 and 2016.

RESULTS

Six typologies were identified based on access (low, moderate and high) and healthy food availability including one where zero food outlets were present. Over the study period, SA2s experienced an overall increase in accessibility and healthiness. Distribution of typologies varied by geographic location and area-level socio-economic position.

CONCLUSION

Multiple typologies with contrasting access and healthiness measures exist within Melbourne and these continue to change over time, and the majority of SA2s were dominated by the presence of unhealthy relative to healthy outlets, with SA2s experiencing growth and disadvantage having the lowest access and to a greater proportion of unhealthy outlets.

Improving spatial data in health geographics: a practical approach for testing data to measure children's physical activity and food environments using Google Street View

Background

Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand.

Methods

We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets.

Results

A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated.

Conclusions

Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.

Panoramic Street-Level Imagery in Data-Driven Urban Research: A Comprehensive Global Review of Applications, Techniques, and Practical Considerations

The release of Google Street View in 2007 inspired several new panoramic street-level imagery platforms including Apple Look Around, Bing StreetSide, Baidu Total View, Tencent Street View, Naver Street View, and Yandex Panorama. The ever-increasing global capture of cities in 360° provides considerable new opportunities for data-driven urban research. This paper provides the first comprehensive, state-of-the-art review on the use of street-level imagery for urban analysis in five research areas: built environment and land use; health and wellbeing; natural environment; urban modelling and demographic surveillance; and area quality and reputation. Panoramic street-level imagery provides advantages in comparison to remotely sensed imagery and conventional urban data sources, whether manual, automated, or machine learning data extraction techniques are applied. Key advantages include low-cost, rapid, high-resolution, and wide-scale data capture, enhanced safety through remote presence, and a unique pedestrian/vehicle point of view for analyzing cities at the scale and perspective in which they are experienced. However, several limitations are evident, including limited ability to capture attribute information, unreliability for temporal analyses, limited use for depth and distance analyses, and the role of corporations as image-data gatekeepers. Findings provide detailed insight for those interested in using panoramic street-level imagery for urban research.

Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results Using Omnidirectional Street View Imagery

BACKGROUND

The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial infrastructure to withstand supply and demand changes induced by such crises.

OBJECTIVE

This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City (NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, and in-person checks.

METHODS

In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included two socioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods (East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers) that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance (when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that research assistants then physically visited. Using the "notes" feature of Google Maps as a data collection tool, notes were made on which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of the vendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent.

RESULTS

Of the 135 open vendors identified in 2019 imagery data, 35% (n=47) were absent/closed and 10% (n=13) were open with more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35% (28/80) of vendors in Manhattan were absent/closed, as were 35% (19/55) of vendors in Brooklyn. Although Google Street View was able to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data for some areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been missed in street view imagery. The Google Maps "notes" feature was used by multiple research assistants simultaneously to rapidly collect observational data on mobile devices.

CONCLUSIONS

The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environment and can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies.