Street vendors problem is a challenge to city management in China. Vendors on the street provide convenience for citizens on one hand

and to some extent disturb the daily order of cities on the other hand. Most studies on street vendors focus on the mechanics of vendors aggregation with certain management methodology

and focus on vendors of particular streets. A quantitative modeling on vendor distribution is rare. A number of factors with complex relationship to one another decide the aggregation of street vendors

therefore

the algorithm of random forest is suitable for its application to building classifiers with complex features. With this algorithm not relying on particular critical factor

problems occurred frequently in other algorithms

such as outliers

noises and overfitting

can be well avoided. Further analysis can be made since the algorithm provides magnitude for each street vendors concerned factors. According to literature and field investigation

average house price per square

length of street segments

folks number

bus line number are chosen for modeling. Haizhu district of Guangzhou has been investigated. Taking kappa coefficient and overall accuracy as standard

the optimum predict model of street vendors is generated with training and parameters adjustment

setting no vendor as grade 1

1~10 vendors as grade 2

11~20 vendors as grade 3

21 or more vendors as grade 4. The proposed model of street vendors distribution which predicts the appearance of street vendors

not only facilitates city planning and management

but also throws new light on relevant research.