Crowdsourced online food images, when combined with food image recognition technologies, have the potential to offer a cost-effective and scalable solution for the assessment of the restaurant nutrition environment. While previous research has explored this approach and validated the accuracy of food image recognition technologies, much remains unknown about the validity of crowdsourced food images as the primary data source for large-scale assessments. In this paper, we collect data from multiple sources and comprehensively examine the validity of using crowdsourced food images for assessing the restaurant nutrition environment in the Greater Hartford region. Our results indicate that while crowdsourced food images are useful in terms of the initial assessment of restaurant nutrition quality and the identification of popular food items, they are subject to selection bias on multiple levels and do not fully represent the restaurant nutrition quality or customers\' dietary behaviors. If employed, the food image data must be supplemented with alternative data sources, such as field surveys, store audits, and commercial data, to offer a more representative assessment of the restaurant nutrition environment.

Deep learning models can recognize the food item in an image and derive their nutrition information, including calories, macronutrients (carbohydrates, fats, and proteins), and micronutrients (vitamins and minerals). This technology has yet to be implemented for the nutrition assessment of restaurant food. In this paper, we crowdsource 15,908 food images of 470 restaurants in the Greater Hartford region on Tripadvisor and Google Place. These food images are loaded into a proprietary deep learning model (Calorie Mama) for nutrition assessment. We employ manual coding to validate the model accuracy based on the Food and Nutrient Database for Dietary Studies. The derived nutrition information is visualized at both the restaurant level and the census tract level. The deep learning model achieves 75.1% accuracy when compared with manual coding. It has more accurate labels for ethnic foods but cannot identify portion sizes, certain food items (e.g., specialty burgers and salads), and multiple food items in an image. The restaurant nutrition (RN) index is further proposed based on the derived nutrition information. By identifying the nutrition information of restaurant food through crowdsourced food images and a deep learning model, the study provides a pilot approach for large-scale nutrition assessment of the community food environment.

Substance abuse is a complex and challenging public health problem. In order to better address substance abuse, it is vital to understand the perspectives of people whose communities are disproportionately impacted by it. This photovoice study aimed to understand how community members perceive the relationship between place, health, and substance abuse in Hartford, Connecticut, one city grappling with substance abuse and its related challenges. Findings revealed three themes: perceived place-based environmental risk factors for substance abuse; coping strategies to maintain sobriety in this challenging environmental context; and participants\' recommendations for addressing substance abuse. Implications are discussed.

OBJECTIVE: People retain culinary customs when they migrate. We tested this commitment via the study of Puerto Rican fresh produce markets in the continental United States over time, 18 yr, and space, by comparisons with source markets in Puerto Rico (PR).
METHODS: A survey of Puerto Rican markets in Hartford (HT), Connecticut in 1993-1994 was repeated in 2009-2010. A comparative study was made at open-air markets in PR in 2009. Surveys recorded fresh crops, and interviews with vendors and Hartford Puerto Rican residents provided context.
RESULTS: We recorded 84 plant crops (64 species; 32 families) for seven categories. The largest category was viandas (fresh, starchy \"root\" crops and immature fruits), followed by saborizantes (flavorings). In the second HT survey, 80% of the crops were still present. And ∼90% of the HT 1993-1994 crops and ∼75% of the HT 2009-2010 crops were shared with markets in PR.
CONCLUSIONS: On the basis of our results, we suggest two new concepts. The persistence of these largely tropical foods in a temperate market far removed from tropical PR shows the importance of basic foods as an element of cultural identification. We recognize this stability as an example of \"culinary cultural conservation\". Second, analysis of these fresh produce markets leads to the conclusion that viandas are the most prominent in diversity, persistence over time and distance, volume, and in terms of consumers\' \"willingness to pay\". Accordingly, we consider the viandas a good example of a \"cultural keystone food group\", a food group that is emblematic of a community\'s culinary conservation.