Much of modern urban planning in the United States is concerned with making cities more walkable. However, this is occurring as the urban landscape of the U.S. is altered radically by changes in crime patterns after the summer of 2020. This paper seeks to find out what the relationship is between walkability and crime in major U.S. cities after 2020. Using multiple linear regressions at the city and neighborhood scale, walkability is found to be a significant, positive predictor of 2019 violent crime rate, 2020 violent crime rate, 2020 property crime rate, and 2020 total crime rate at the city level. It was found to be a positive, but not significant predictor at the neighborhood level. Walkability has no protective influence against crime/rising crime, and it appears that as crime gets worse it tends to get worse in the cities that are more walkable, but other variables such as African American population are better determinants of crime. Urban planners should seek to increase walkability while also finding a way to mitigate potential exposure to crime.

Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.

Universities host a large, young and diverse population that commutes to the same location every day, which makes them ideally suited for public transportation ridership. However, at many universities in the US, this potential for high levels of transit ridership is not being maximized. This research aims to identify the areas where Valley Metro’s public transit service to ASU’s Tempe campus is over- and under-performing in comparison with the overall public transportation service to the entire Phoenix metro area. The hypothesis states that proximity to campus and the convenience of using public transportation would be the two main factors in determining the success of an area’s public transportation service. ASU’s Parking & Transit Services provided confidential data with the addresses of all the students and employees who purchased a parking pass, transit pass and bike registration. With these data, the public transportation mode share for commuters to ASU in each census block group was calculated and compared to the mode share for the general public, which was based on US Census data. The difference between the public transit mode shares of ASU pass holders vs. commuting by the general public was then computed and analyzed to identify areas as hot and cold spots. These heat maps are then compared to the hypothesized factors of proximity to campus and the convenience of public transportation in terms of the light rail line, park-and-ride lots, and number of transfers needed to connect to campus. The transfers were estimated using origin and destination survey data provided by Valley Metro. Results show that the convenience of public transportation was a driving factor in explaining where the transit mode share to ASU is higher than that of the general public, whereas the proximity to campus had little impact on the areas with high ASU-specific transit mode shares. There is an absence of hot spots directly around the campus which is explained by the combination of both high transit share for the non-ASU population and the large share of ASU students and employees using active transportation and free circulator buses this close to campus. These findings are significant specifically to ASU because the university can learn where the transit service is performing well and where it is underperforming. Using these findings, ASU PTS can adjust its pricing, policies, services and infrastructure and work with Valley Metro and the City of Tempe to improve the ridership for both students and employees. Future research can compare more factors to further interpret what leads to success for transit service to university campuses.