Traffic congestion, car dependence, and environment pollution have become the key factors of sustainable urban development in China. Smart phone app was used to collect oneweek commuting data for 286 residents at the trip level in Xi′an city, and environment audit was made to collect the building environment data near their neighborhood and work locations. Using multinomial Logit regression, the impact of the traditional built environment “5D” factors and other built environment factors on the six commute modes was analyzed.The results show that the following factors have significant impact on active commute mode and public transit commute mode, that is, population and building density of the workplace, distance between workplace and city center, population and road network density of the residence, number of bus lines, and whether there are colleges or stores nearby.The built environment of the workplace has greater impact on commute mode choice than that of resident locations.In addition to the traditional "5D" factors, whether there are colleges or stores nearby has significant impact on some commute modes. Besides, personal health status has a significant impact on the commute mode choice, and obese people are more inclined to commute by car. Based on all of the above findings, suggestions of planning strategies are put forward.

The spatial distribution of producer services in metro station areas in Xi′an city and corresponding acting mechanisms are studied through the effective description function of point of interest (POI) data to economic activities at small spatial scale. The scope determining method of a metro station area is put forward and the producer services sectors thriving within inner city are integrated into 6 categories of businesses. Then the general characteristics of spatial distribution of each category are analyzed and compared by location quotient calculation, as well as the local ones at the same time. Moreover, secondorder partial correlation analysis is employed to explain the influences on spatial distribution concentrations of 6 categories in metro station areas due to their material space environments, while some mechanisms can be concluded later with the spatial distribution outcomes of Xi′an city. The results indicate that the general distribution characteristics in the whole city is more concentrations of producer services happened in metro station areas than other places and there are still 3 patterns of local distribution characteristics for 6 categories of businesses, including centripetal ones, centrifugal ones and sandwich ones. The diversity increasing of space for residence and employment in metro station areas surely contributes to the spatial concentrating of most categories of producer services, but more open space makes obstacles to their concentrating in the same situation. Furthermore, the increasing of urban road density of traffic space in metro station areas only influences the distributing of a few categories and the influential effects are also weak.Three influential mechanisms of spatial distribution for producer services in metro station areas of Xi′an city can be put forward, including complementary mechanism owing to intensive entityrelationship among correlative industries, competitive mechanism owing to resources contention of material space, selective supporting mechanism owing to atypical demands from specific industrial sectors.

Shared bicycle brings practical travel convenience for urban residents, and brings new ideas to actively deal with and solve urban diseases. Taking the builtup area of Xi′an as the research area and constructing a conceptual model of the use intention of the shared bicycle, the effects of service convenience and perceived risk on the use intention of shared bicycle are examined. The study concluded service convenience and it′s three sub dimensions(obtaintion convenience, transaction convenience, and followup convenience) have significant positive effects on the use intention (γ=0.603, P<0.001; γ1=0.320, P1<0.001; γ2=0.622, P2<0.001; γ3=0.478, P3<0.001). The perceived risk and it′s three sub dimensions(economic risk, privacy risk, body risk) have significant negative effects on the use intention (γ=-0.537, P<0.001; γ1=-0.448, P1<0.001; γ2=-0.326, P2<0.001; γ3=-0.535, P3<0.001). The partial mediator role of perceived risk in the relationship of service convenience and use intention is significant, and the partial mediator role of service convenience in the relationship of perceived risk and use intention is also significant.

The communityoriented new retail is a special space of interaction between urban living circles and business circles, its spatial distribution and location are worthy of further exploration. Based on the AOI(area of interest) data of the official service area of 12 Freshippo stores, a communityoriented new retail representative company in Chengdu city, and POI(point of interest) data of surrounding communities, universities, etc., we use text analysis, mathematical statistics, and spatial analysis to explore the spatial pattern, the choice of location and influencing factors of Freshippo stores. The findings are as follows: Freshippo stores show a diagonal Alike distribution with Tianfu Square as the center between the second ring road and the fourth ring road of the city. The store number shows a “highlowlow” pattern with the increase of the distance. The distribution of Freshippo stores combines the “point” store with the “polygon” service scope. The service area shape is restricted by the community distribution, road network and geomorphologic landscape. As for the choice of location, Freshippo stores tend to be located on the underground layer of the shopping mall at the edge or periphery of the business district with a large number of communities in the service area, which have a certain basic flow of customers, lower rent, and convenient road transportation. The service objects of Freshippo stores are mainly communityoriented, also including universities, enterprises, and parks.The main competitors are supermarkets, other new retail stores and catering stores. Their size, product types, and spatial distribution will impact Freshippo stores′ distribution. Finally, from the perspective of multiscale integration, the influencing mechanism of the distribution pattern and location of communityoriented new retail spatial is explored, and targeted optimization suggestions and future research directions are proposed.