![]() ![]() Do public transport improvements increase agglomeration economies? A review of literature and an agenda for research. How density and mixed uses at the workplace affect personal commercial travel and commute mode choice. Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay Area. Transportation Research Part A: Policy and Practice, 30(5), 361–377.Ĭervero, R., & Duncan, M. Mixed land-uses and commuting: Evidence from the American housing survey. Destinations that matter: Associations with walking for transport. Journal of Korea Planning Association, 56(1), 22–38.Ĭerin, E., Leslie, E., du Toit, L., Owen, N., & Frank, L. Analysis on the determinants of hourly-based mixed level of defacto population. The entertainment zone: Unplanned nightlife and the revitalization of the American downtown. A resurgence in urban living? Trends in residential location patterns of young and older adults since 2000. Testing the 'popsicle test': Realities of retail shopping in new 'traditional neighbourhood developments'. Applied Spatial Analysis and Policy, 8(3), 231–247.īartlett, R. Cyber cities: Social media as a tool for understanding cities. Journal of Marketing Research, 42(1), 109–115.Īrribas-Bel, D., Kourtit, K., Nijkamp, P., & Steenbruggen, J. A multipurpose shopping trip model to assess retail agglomeration effects. ![]() ![]() These results imply that a proper degree of mixed use attracts more people to commercial areas, even at night, thereby enhancing the vitality of the streets.Īrentze, T. Furthermore, the land use of the adjacent areas significantly influences the visiting patterns of commercial districts. Second, mixed land use and a high percentage of residential use in commercial areas have a positive impact on visitor concentration at night. ![]() A comparison of land use patterns between the two groups shows the following significant differences: First, the nocturnal districts tend to have distinctive features consisting of vibrant streets due to a higher degree of mixed use. We introduce a binomial logistic model where a binary indicator is used as the dependent variable for the two groups. Using k-means clustering and hourly location-based population data collected from smartphone signals in Seoul, we classify commercial districts into two groups: diurnal and nocturnal. The standardized values of the population in each census tally district form distinct patterns for every hour of the day. This paper aims to analyze the impact of mixed land use on population flow in the diurnal and nocturnal districts. This difference in peak time of population flow may be affected by land use, such as mixed use. Some commercial districts attract the majority of their visitors during the daytime, while others at night. ![]()
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