Revealing the Spatio-Temporal Heterogeneity of the Association between the Built Environment and Urban Vitality in Shenzhen
<p>Study area.</p> "> Figure 2
<p>The dynamic of urban vitality in Shenzhen on weekday.</p> "> Figure 2 Cont.
<p>The dynamic of urban vitality in Shenzhen on weekday.</p> "> Figure 3
<p>The dynamic of urban vitality in Shenzhen on weekend.</p> "> Figure 3 Cont.
<p>The dynamic of urban vitality in Shenzhen on weekend.</p> "> Figure 4
<p>Standard deviation distribution of vitality on weekdays and weekends.</p> "> Figure 5
<p>Regression results of OLS model for different moments on weekdays and weekends.</p> "> Figure 6
<p>Regression coefficients for different moments on weekdays and weekends.</p> "> Figure 7
<p>Coefficient map of population density.</p> "> Figure 8
<p>Coefficient map of building density.</p> "> Figure 9
<p>Coefficient map of road density.</p> "> Figure 10
<p>Coefficient map of functional mixture.</p> "> Figure 11
<p>Coefficient map of bus stop density.</p> "> Figure 12
<p>Coefficient map of distance to metro.</p> "> Figure 13
<p>Coefficient map of distance to CBD.</p> ">
Abstract
:1. Introduction
2. Literature Review
2.1. Current Status of Research on Urban Vitality Measurements
2.2. Current Status of Research on Influencing Factors
3. Materials and Methods
3.1. Study Area
3.2. Data Sources
3.3. Traffic Analysis Zone
3.4. Urban Vitality Measurement Method
3.5. The Urban Built Environmental Indicator System
3.6. The OLS Model
3.7. The Moran’s I Index
3.8. The GTWR Model
4. Results
4.1. Spatio-Temporal Variation of Urban Vitality
4.2. Global Regression Results of the Association between Built Environment and Urban Vitality
4.3. Spatio-Temporal Geographical Regression Results of the Association between Built Environment and Urban Vitality
4.3.1. Spatial Autocorrelation Test
4.3.2. Results of GTWR Regression Analysis
4.3.3. Spatial–Temporal Analysis of GTWR Regression Coefficients
- Temporal analysis of GTWR regression coefficients
- Spatial analysis of GTWR regression coefficients
- (1)
- Density
- (2)
- Design
- (3)
- Diversity
- (4)
- Distance to transit
- (5)
- Destination accessibility
5. Discussion
5.1. Research Novelty and Contribution
5.2. Urban Planning Implications
5.3. Limitations and Prospection
6. Conclusions
- (1)
- On both weekdays and weekends, the urban vitality in Shenzhen showed a pattern of being high in the west and south and low in the east and north, and the high-vitality areas showed a clustered, polycentric character. The distribution of high-vitality areas on weekdays did not change significantly within a day. The distribution of urban vitality on weekends was more dispersed and did not have obvious temporal variation. The temporal change in urban vitality was more dramatic during the weekdays than during the weekends.
- (2)
- The effects of built environment factors on urban vitality exhibited significant temporal differences. Population density, building density, bus stop density, and distance to subway station showed positive effects on weekdays and weekends. Distance to the CBD exhibited a negative effect on weekdays and weekends and showed a significant diurnal difference. Road network density and functional mix, on the other hand, exhibited positive and negative effects throughout the day.
- (3)
- In terms of spatial distribution, the effect of population density and building density on urban vitality was always positive, but the intensity of the effect exhibited differences with the TAZ units. The coefficients of road network density, functional mix, bus stop density, and distance from metro stations had both positive and negative values in the study area. Distance from the CBD mainly showed a negative impact, except for areas farther away from the CBD.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Source | Literatures | Advantages | Disadvantages |
---|---|---|---|
Questionnaires | Sung et al., 2015 [11] | Simple and easy to use | High cost, small coverage, and low accuracy |
Chen et al., 2013 [12] | |||
Zarin et al., 2015 [13] | |||
Wu et al., 2018 [35] | |||
Nighttime light images | Jia et al., 2020 [15], | Wide coverage and easy access | Low spatial resolution and only reflects nighttime vitality |
Zhang et al., 2022 [17] | |||
Xia et al., 2020 [19] | |||
POI | Zeng et al., 2018 [20] | Easy access and high accuracy | Lack of time information |
Ye et al., 2018 [21] | |||
Mobile phone data | Wu et al., 2019 [22] | Broad coverage and user groups | Low precision and difficult to obtain |
Kang et al., 2020 [23] | |||
Xia et al., 2022 [24] | |||
Social media data | Wang et al., 2022 [25] | High precision and easy access | Biased user groups exist |
Yue et al., 2019 [26] | |||
Li et al., 2022 [27] | |||
GPS trajectory data | Zeng et al., 2020 [31] | High precision and wide coverage | Difficult to access |
Li et al., 2022 [32] | |||
Positioning density data | Zhuo et al., 2021 [29] | Higher spatio-temporal resolution and more comprehensive user groups | Difficult to access |
Lin et al., 2023 [30] |
Literatures | Models | Key Factors |
---|---|---|
Sung et al., 2015 [11] | OLS | Land use mix, density, block size, building age, accessibility, and boundaries |
Liu et al., 2022 [42] | OLS | Occupancy balance, floor area ratio, open space ratio, and road network density |
Sulis et al., 2018 [36] | OLS | Population density and population mobility |
Pan et al., 2021 [41] | SAR | Land use density, buildings, and public transportation |
Wu et al., 2019 [22] | SLR | Mixed use and diversity, scale, older buildings, density, and boundary vacuum |
Xia et al., 2022 [24] | SEM | Recreation POI density and transportation accessibility |
Liu et al., 2020 [50] | GWR | Road accessibility, POI density, and POI diversity |
Zhang et al., 2021 [56] | GTWR | POI density, POI diversity, road network density, and intersection density |
Data | Source | Resolution (Format) | Time |
---|---|---|---|
Baidu heatmap | Baidu Map (https://huiyan.baidu.com/products/platform, accessed on 11 November 2020) | CSV | 2020 |
Population density | WorldPop (https://www.worldpop.org/, accessed on 8 October 2022) | 1KM, Tiff | 2020 |
POI | Amap (https://lbs.amap.com/, accessed on 12 September 2020) | CSV | 2020 |
Roads | OpenStreetMap (https://www.openstreetmap.org/, accessed on 10 July 2020) | SHP | 2020 |
Building boundaries | OpenStreetMap (https://www.openstreetmap.org/, accessed on 10 July 2020) | SHP | 2020 |
Administrative district | Amap (https://lbs.amap.com/, accessed on 8 June 2020) | SHP | 2020 |
Indicator | Variables | Description |
---|---|---|
Density | Population density (PD) | Mean population density within each TAZ unit |
Building density (BD) | Mean building density within each TAZ unit | |
Design | Road density (RD) | Road network density within each TAZ unit |
Diversity | Urban functional mix (UM) | The level of diversity of POI types within each TAZ unit |
Distance to transit | Bus stop density (SD) | Bus stop density within each TAZ unit |
Distance to metro (DM) | Distance of each TAZ unit from the nearest metro station | |
Destination accessibility | Distance to CBD (DC) | Distance of each TAZ unit from the nearest CBD |
Time | H0 | H1 | H2 | H3 | H4 | H5 | H6 | H7 | H8 | H9 | H10 | H11 |
Moran’s I | 0.36 | 0.36 | 0.35 | 0.35 | 0.34 | 0.33 | 0.34 | 0.40 | 0.45 | 0.54 | 0.52 | 0.51 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Time | H12 | H13 | H14 | H15 | H16 | H17 | H18 | H19 | H20 | H21 | H22 | H23 |
Moran’s I | 0.46 | 0.49 | 0.52 | 0.53 | 0.52 | 0.50 | 0.48 | 0.48 | 0.45 | 0.42 | 0.39 | 0.38 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Time | H0 | H1 | H2 | H3 | H4 | H5 | H6 | H7 | H8 | H9 | H10 | H11 |
Moran’s I | 0.38 | 0.38 | 0.37 | 0.36 | 0.35 | 0.34 | 0.35 | 0.36 | 0.36 | 0.43 | 0.45 | 0.47 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Time | H12 | H13 | H14 | H15 | H16 | H17 | H18 | H19 | H20 | H21 | H22 | H23 |
Moran’s I | 0.44 | 0.45 | 0.46 | 0.49 | 0.49 | 0.47 | 0.44 | 0.43 | 0.40 | 0.39 | 0.38 | 0.36 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Indicators | Weekdays | Weekends | ||||||
---|---|---|---|---|---|---|---|---|
Min | Max | Average | Std. | Min | Max | Average | Std. | |
PD | 0.133 | 0.967 | 0.283 | 0.137 | 0.133 | 0.967 | 0.283 | 0.137 |
BD | 0.053 | 0.698 | 0.385 | 0.142 | 0.053 | 0.698 | 0.385 | 0.142 |
RD | −0.367 | 0.306 | 0.09 | 0.131 | −0.367 | 0.306 | 0.09 | 0.131 |
MIX | −0.217 | 0.262 | 0.029 | 0.094 | −0.217 | 0.262 | 0.029 | 0.094 |
SD | −0.186 | −0.432 | 0.169 | 0.082 | −0.186 | −0.432 | 0.169 | 0.082 |
DM | −0.159 | 0.513 | 0.066 | 0.125 | −0.159 | 0.513 | 0.066 | 0.125 |
DC | −0.481 | 0.249 | −0.221 | 0.121 | −0.481 | 0.249 | −0.221 | 0.121 |
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Li, Z.; Zhao, G. Revealing the Spatio-Temporal Heterogeneity of the Association between the Built Environment and Urban Vitality in Shenzhen. ISPRS Int. J. Geo-Inf. 2023, 12, 433. https://doi.org/10.3390/ijgi12100433
Li Z, Zhao G. Revealing the Spatio-Temporal Heterogeneity of the Association between the Built Environment and Urban Vitality in Shenzhen. ISPRS International Journal of Geo-Information. 2023; 12(10):433. https://doi.org/10.3390/ijgi12100433
Chicago/Turabian StyleLi, Zhitao, and Guanwei Zhao. 2023. "Revealing the Spatio-Temporal Heterogeneity of the Association between the Built Environment and Urban Vitality in Shenzhen" ISPRS International Journal of Geo-Information 12, no. 10: 433. https://doi.org/10.3390/ijgi12100433