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Table 4 Regression of influential factors for crime rates: Spatial regression analysis

From: Domestic migration, home rentals, and crime rates in China

  Spatial lag model Spatial error model Spatial-autoregressive model with spatial-autoregressive disturbances
Variables (11) (12) (13) (14) (15) (16)
lmigr .154** .219*** .152** .218*** .151** .217***
lrent .245*** .120** .247*** .126** .248*** .126**
edu −.090*** −.032 −.091*** −.031 −.089*** −.030
lunwr .349*** .252*** .358*** .267*** .352*** .263***
lhc −.348*** −.200** −.355*** −.222*** .352*** −.219***
lyouth .225* −.180** .221* −.212* .224* −.210*
lsex .695*** .717*** .713** .749*** .697*** .738***
urban .076 −.134 .073 −.141 .075 −.139
lpgdp .137*** .196*** .135** .191*** .135*** .191***
lpgex −.108*** −.006** −.109*** −.007 −.108** −.006
Constant 6.525*** 5.541*** 6.820*** 5.544*** 6.753*** 5.498***
lambda .019 .012    .196 .013
rho    −.115 −.347 −.121 −.349
  1. Standardized spatial contiguity weights matrices and maximum likelihood estimators are employed in these models
  2. Note: ***p < .01, **p < .05, *p < .1