Spatial-temporal Big Data Verification of Spatial Interaction between Urban Commercial Centers and Consumption Travels
Taking four commercial centers in Shanghai as examples,this paper uses spatial-temporal big data to verify the spatial interaction characteristics between commercial centers and consumption travels. It compares the choices of attractiveness variables and distance parameters in the planning of commercial centers using the Huff model. Firstly,the numbers of resident's consumption travels to four commercial centers are measured by using spatial-temporal big data and consumption travel time is calculated using the Internet map API,treating them as the measured values. Secondly,the distance parameter of Huff model is calibrated using the OLS method. Finally,the measured values are compared with the predicted values obtained from Huff model calculated with 4 attractiveness variables separately. It is concluded that probabilities of consumption travel are still the combination results of travel time and commercial center attractiveness in the current era,conforming to the spatial decay of the power function. However,the four commercial centers have different distance parameters. When the POI mixture of commercial center is used as attractiveness variables,the division of the sphere of influence calculated by Huff model is closest to the actual consumption travel choice. Currently,when applying the Huff model in commercial center planning,it is not appropriate to adopt a unified value for multiple commercial centers in the choice of distance parameters. Furthermore,more emphasis should be placed on the role of POI mixture representing the diversity of business formats in the construction of attractiveness variables.
commercial centersspatial interactionHuff Modelspatial-temporal big dataconsumption travelShanghai
NETL
NSTL
万方数据
