Visualization of residential house prices

Visualization of house prices in second-hand residential areas in Wuhan

1. Data collection and preprocessing

The data of second-hand housing in Wuhan were collected from Wuhan public service platform 54000 strip , Double check the data 、 Address standard processing 、 After removing the null value, we get 2960 Cell data . However, the lack of longitude and latitude information can not be visualized , So with Baidu API Geocoding .

2. Call Baidu API Geocoding

Applied for Baidu API Of AK after , Call its geocoding function , Obtain the longitude and latitude according to the cell address , Finally, it is sorted out as the price of second-hand housing community in Wuhan shp data .

3. Use QGIS Coordinate correction

Use the coordinates directly , And discovery and existing reality poi The position does not match , It is found that the coordinate system used by Baidu map needs secondary conversion , So I decided to use it QGIS Geohey Plug in processing . Reference resources QGIS Coordinate transformation

4. Nuclear density analysis

Arcgis The kernel density analysis function obtains the final layer , It turns out that h It's the river bank 、 Jianghan 、 Houses in Wuchang district are more expensive .