Method and device for mining urban community structure based on navigation data
A technology of navigation data and mining devices, which is applied in the field of data processing and can solve problems such as inaccurate and unreasonable communities
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Embodiment 1
[0022] The urban community structure mining method of this embodiment is based on the navigation data generated by taxis all over the city around the clock. Firstly, the hot spots of the city are mined through spatial clustering, and the city is grid-divided according to the clustering features. On this basis, the The spectral clustering algorithm excavates closely related regions, which constitute the community structure in the city. Such as figure 1 As shown, the specific steps are as follows:
[0023] Step 1: According to the passenger status in the taxi navigation data, extract the pick-up and drop-off points from the taxi navigation data, and use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm to classify the pick-up and drop-off points Carry out spatial clustering to mine the hotspots frequently visited by residents when traveling. The DBSCAN clustering algorithm can better deal with uneven and irregular taxi pick-up and dro...
Embodiment 2
[0045] In the first embodiment of the above method, hotspot areas (city center areas) are first found from the entire urban area, and communities are divided only for the hotspot areas. A city generally includes the downtown area and the suburbs, and the data in the suburbs are relatively scattered, and there is no need to divide them into communities. Therefore, the embodiments only focus on the hotspot areas, that is, the downtown area.
[0046] Different from Method Embodiment 1, the urban community structure mining method in this embodiment does not need to divide hotspot areas, but divides the communities in the city where there are pick-up and drop-off point data, and realizes the division of the entire urban community. Include the following steps:
[0047] 1) Clustering the city pick-up and drop-off point data provided by the taxi navigation data, and determining the center point of each cluster;
[0048] 2) Taking the center point as the reference point, divide the ur...
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