Research in the United Kingdom and more recently in the United States has found geographic differences in access to affordable, nutritious food. In some cases more limited access has been associated with a higher proportion of residents in ethnic minority groups. Using Geographic Information Systems (GIS), we explored the potential existence of “food deserts” and their relationship with ethnicity in Santa Cruz, Monterey and San Benito Counties. Relative to the region as a whole, there were few clusters of census blocks with less access to retail food outlets with fresh produce (grocery stores, supermarkets and fruiterias) after adjusting for population density. In addition, access to these retail food outlets was not associated with the percentage of the population that was Latino. However, we identified some areas that would benefit from further investigation, and that may be suitable locations for locating new fruit and vegetable markets. Such markets may benefit local residents, as well as new, limited-resource, and minority farmers who often have inadequate access to distribution networks for their produce.

In this work, we simplify and enhance the visualization currently supported by the UC Atlas Website for mapping global inequality by (i) creating a simple user interface, (ii) supporting time series animation of global maps, and (iii) simplifying and integrating theline graphs, bar graphs, and ranked bar graphs. The visualization system is accessible at http://atlas-dev.ucsc.edu/ian. Our vision is to enhance the visualization system by adding additional types of charts including scatter plots, star plots, parallel coordinates, and small multiples visualization while keeping the user interface simple and integrated.

In this work, our objective is to visualize the relationship between the variables that impact health in a global context. Recently, Cornia et al. [1] have proposed five main determinants of global health – material deprivation, progress in health technology, acute psychosocial stress, unhealthy lifestyle, and income inequality etc. Results of regression analysis worldwide indicate that almost 90% of the variation in health can be attributed to twelve variables representing these five determinants. We compute correlations between the health variables and its determinants and apply a visualization tool [2] to display these correlations globally and at country level in order to gain a better understanding. We observe that the country-level results obtained through easy-to-understand graphs and simple correlation analysis pose an anomaly to the worldwide regression results and require further analysis to close the gap between correlation and regression analysis and the gap between the country-level and global-level analysis.