利用python进行空间自相关的检验并构建地理加权回归(GWR)模型 说到地理加权回归,相信大家肯定不会陌生。作为一种先进的空间数据分析技术,地理加权回归能够充分捕捉空间关系的非平稳性。举个简单的不恰当的例子,我们要对中国各个城市的奢侈品消费量与人均收入进行建模。正常的的理解是人均收入越高,奢侈品消费量就越大,在...
'NOx', 'PM25', 'VOC'] result_params = results.params for i, factor in enumerate(factors): ...
model = smf.ols(formula ='tip ~ total_bill',data=tips) results = model.fit() #fit方法用数据拟合模型 print(results.summary()) #summary方法查看结果 print(results.params) #params属性只查看系数 print(results.conf_int()) #conf_int()方法提取置信区间,确定估计值,误差范围 ''' OLS Regression R...
print(gwr_bw) gwr_results = GWR(g_coords, g_y, g_X, gwr_bw).fit gwr_results.params[0:5] array([[-0.23204579, 0.22820815, 0.05697445, -0.42649461], [-0.2792238 , 0.16511734, 0.09516542, -0.41226348], [-0.248944 , 0.20466991, 0.07121197, -0.42573638], [-0.23036768, 0.1527493 , 0.0510379...
params,columns=var_names) # 回归参数显著性 gwr_flter_t=pd.DataFrame(gwr_results.filter_tvals()) #构建回归系数gdf coef_gpd=gpd.GeoDataFrame(gwr_coefficent,geometry=gpd.points_from_xy(Vio_gdf.geometry.x,Vio_gdf.geometry.y),crs=CRS('EPSG:32651')) #绘图 fig,ax = plt.subplots(nrows=2,...