Table 6 Questionnaire data for model fitting.

From: Comprehensive walkability assessment of urban pedestrian environments using big data and deep learning techniques

Variable

Estimate

Standard error

P>|z|

Green vision rate

   

Low

-0.6655

0.0324

\(\:<\)0.001

Middle

-0.4637

0.0310

\(\:<\)0.001

High^

0

-

-

Enclosure ratio

   

Open

0.0673

0.0283

0.017

Semi-open

-0.2712

0.0323

\(\:<\)0.001

Closed^

0

-

-

Relative width of sideway

   

Low

-1.0482

0.0340

\(\:<\)0.001

Narrow

-0.4485

0.0274

\(\:<\)0.001

Wide^

0

-

-

Fence

   

Yes

0.3345

0.0251

\(\:<\)0.001

No^

0

-

-

Traffic volume

   

Low

-0.0468

0.0309

0.023

Middle

-0.0386

0.0302

\(\:<\)0.001

High^

0

-

-

Foot traffic

   

Low

0.2950

0.0290

\(\:<\)0.001

Middle

0.1419

0.0322

\(\:<\)0.001

High^

0

-

-

Street signage

   

Low

0.0126

0.0317

0.186

Middle

0.0971

0.0311

0.002

High^

0

-

-

Number of observations

23,724

  

Log likelihood

-7774.0209

  

LR chi2

1827.77

  

Prob > chi2

\(\:<\)0.001

  

Pseudo R2

0.1452

  
  1. ^ as reference level.