2022
DOI: 10.3390/su14052839
|View full text |Cite
|
Sign up to set email alerts
|

Measurement of Street Network Structure in Strip Cities: A Case Study of Lanzhou, China

Abstract: As the foundation and skeleton of urban space, the street network is significant to the urban travel environment and socio-economic activities. To reveal the structural characteristics of the street network, this paper proposes a measurement index system to study the street network structure and urban travel characteristics. To illustrate the relationship between spatial accessibility of streets in strip cities and residents’ travel and service demands, we take Lanzhou, a typical strip city, as an example for … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 53 publications
0
2
0
Order By: Relevance
“…This result is consistent with previous reports that CL spikes contributed 85% to seed yield. (Li, 2015).…”
Section: Discussionmentioning
confidence: 99%
“…This result is consistent with previous reports that CL spikes contributed 85% to seed yield. (Li, 2015).…”
Section: Discussionmentioning
confidence: 99%
“…In addition, when this study transforms from an urban scale to a provincial scale, it is found that the development stage of the provincial adaptive cycle is related to the average level of all cities in the region. Li [65] also conducted a similar study for the fve provinces in northwest China. He found in the analysis of multifactor urban network structure that the development levels of cities in the region were signifcantly diferent and that the provinces with multiple high-level cities within their jurisdiction had a higher development level of comprehensive quality than other provinces, and vice versa.…”
Section: Scale Heterogeneity Of Urban and Regional Adaptivementioning
confidence: 99%