Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning
Abstract
1. Introduction
- (1)
- How can we combine the latest practices of visual perception methods and the nature of spontaneous streetscape elements?
- (2)
- What are the spatial distribution characteristics of the spontaneous streetscape elements at an intraurban scale?
- (3)
- What are the impacts of the features on diversified social sensing?
- (1)
- Overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity.
- (2)
- Specifically, the spontaneous streetscape elements can be divided into three types, given the differentiated roles of significantly positive, negative, and polarizing impacts on both types of social sensing.
2. Literature Review
2.1. Nature of the Spontaneous Streetscape and the Elements
2.2. Image Identification of Micro-Scale Elements in Street Studies
2.3. Data-Driven Social Sensing and Its Methods
3. Materials and Methods
3.1. Study Area
3.2. Data Acquisition and Methods
3.2.1. Mobile Collection of Street View Images
3.2.2. Classification of Spontaneous Streetscape Elements
3.2.3. Machine Learning Model Training
3.2.4. Social Sensing Data Processing
3.2.5. Multiple Regression Models
4. Results
4.1. Features of Spontaneous Streetscape Element Distribution
4.2. Spatial Distribution of Social Sensing
4.3. Impact of the Spontaneous Streetscape Features on Social Sensing
4.3.1. Performance Comparison of Multiple Regression Models
4.3.2. Impact of Spontaneous Streetscape Features on Economic Vitality
Dianping Merchant Quantity as the Proxy of Economic Vitality
Dianping Consumer Comments as the Proxy of Economic Vitality
4.3.3. Impact of Spontaneous Streetscape Features on Social Media Perception
Weibo Check-In Quantity as a Proxy of Social Media Perception
Weibo Sentiment Index as the Proxy of Social Media Perception
5. Discussion and Conclusions
- (1)
- Overall, the spontaneous streetscape features have a certain similarity in terms of their impact on both dimensions of social sensing in Quanzhou, indicating significant clustering and transitional trends and strong spatial heterogeneity.
- (2)
- Specifically, the spontaneous streetscape elements can be divided into three categories, given their differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Study Area | Image Data | Selected Streetscape Element | Algorithm | Source |
---|---|---|---|---|
56 global cities | Google Street View | Wall, building, road, sidewalk, fence, signboard, path, stairs, door, bench, awning, streetlight, pole, fountain, sculpture, traffic light, stairway, tree, grass, plant, water, waterfall, lake, mountain, rock | PSPNet | [16] |
Shenzhen, China | Tencent Street View | Greenspace vegetation (tree, forest, greenbelt, lawn), building, sky, wall, road, pavement | SegNet | [21] |
Guangzhou, China | Tencent Street View | Street furniture (fences, streetlights, traffic lights, cameras, windows), pedestrian, bicycle, booth, trade name, signboard, pavement, roadway, motor vehicles | FCN | [35] |
Shanghai, China | Baidu Street View | Sky, building, tree, road, pavement, fence, sign, vehicle, pedestrian, bike | SegNet | [36] |
Xuzhou, China | Baidu Street View | Signboard, frame door, glass interface, open store, closed store | YOLO | [11] |
Atlanta, GA, USA | Google Street View | Building, house, sidewalk, road, car, tree, plant, grass, walk signal, crosswalk, sidewalk, buffer, streetlight | PSPNet | [37] |
Seoul, Korea | Naver Street View | Building, tree, plant, sky, road, sidewalk, street furniture | High-resolution networks; Canny Edge | [38] |
Seoul, Korea | Google Street View | Vegetation, building, road, sidewalk | Deeplab V3+ | [39] |
ZG city, China | Baidu Street View | Wall, building, tree, road, window, grass, plant, sidewalk, fence | Faster R-CNN; PSPNet | [40] |
Type | Subtype | Label Code | Definition |
---|---|---|---|
Basic | Framed door/window | Framedoor | (1) There are clear horizontal and vertical frames inside the outline of doors and windows; (2) an interface mainly used by the storefronts operating indoors, with relatively poor display quality. |
Glass gateway/window | Glassinterface | (1) Glass occupying the main part of the gateways or windows, with no frame inside; (2) an interface mainly used by the storefronts needing to be displayed externally. | |
Open entrance | Openentry | (1) Only the outlines of entrances can be seen, with shadows or various goods in them; (2) an interface of the storefronts that require direct and constant outdoor activities. | |
Closed entrance | Closedstore | (1) Storefronts that are shut down or not in business; (2) a special component of the streetscape that can reflect urban economies and the experience of street vitality. | |
Commercial | Store signboard | Signboard | (1) A reflection of street economic vitality and the number of storefronts, whether in business or not; (2) usually fixed above entrance interfaces. |
Banner/ad | Banner | (1) Larger quantity and smaller scale compared to store signboards; (2) generally used at the height of the visual level or lower. | |
Movable | Sunshade/awning | Sunshade | (1) It mainly serves to shield against wind, rain, and sunlight, and also indicates a certain outdoor usage range; (2) usually located at a higher position than doors and windows and close to the vertical interfaces of storefronts. |
Outdoor umbrella | Umbrella | (1) Commonly used by street vendors, or storefronts to set up stalls and outdoor businesses along the street. | |
Potted plant | Pottedplant | (1) A kind of private greenery for store decoration and embellishment of natural elements; (2) usually placed at entrances. | |
Cultural | Wall decoration painting/sculpture | Decoration | (1) Generally, hard materials, such as bricks and stones, are used with clear outlines; (2) a cultural element usually placed at vertical interfaces or independently near entrances. |
Variables | Abbr. | Description | Calculation Method |
---|---|---|---|
Dependent variables | |||
Online economic vitality index | DMQ | The quantity of the Dianping merchants in a unit | Buffer creation and the spatial connection tool in overlay analysis in ArcGIS |
DCC | The sum of the Dianping consumer comments in a unit | Buffer creation and the spatial connection tool in overlay analysis in ArcGIS | |
Social media perception index | WCQ | The quantity of the Weibo check-in records in a unit | Buffer creation and the spatial connection tool in overlay analysis in ArcGIS |
WSI | The average sentiment index of Weibo texts in a unit | where confiw and prob_piw are obtained from the Baidu Natural Language Processing (NLP) API, and stand for the confidence level and positive sentiment probability of the w-th Weibo text in the i-th unit, respectively. | |
Independent variables | |||
Basic interface usage | SFD | The sum of the framed doors and windows in a unit | SFDi = FDli + FDri where FDli and FDri are the sums of the framed doors and windows of the i-th unit from the L- and R-side, respectively. |
SGI | The sum of the glass gateways and windows in a unit | SGIi = GIli + GIri where GIli and GIri are the sums of the glass gateways and windows of the i-th unit from the L- and R-side, respectively. | |
SOE | The sum of the open entrances in a unit | SOEi = OEli + OEri where OEli and OEri are the sums of the open entrances of the i-th unit from the L- and R-side, respectively. | |
SCS | The sum of the closed entrances in a unit | SCSi = CSli + CSri where CSli and CSri are the sums of the closed entrances of the i-th unit from the L- and R-side, respectively. | |
Interface diversity index | SDI | The interface diversity degree of the storefronts in business calculated by Shannon entropy | where Pj is the proportion of the j-th type of the interfaces from operating storefronts within the i-th unit. |
Commercial element usage | SSB | The sum of the store signboards in a unit | SSBi = SBli + SBri where SBli and SBri are the sums of the store signboards of the i-th unit from the L- and R-side, respectively. |
SBA | The sum of the banners and ads in a unit | SBAi = BAli + BAri where BAli and BAri are the sums of the banners and ads of the i-th unit from the L- and R-side, respectively. | |
Movable element usage | SSS | The sum of the sunshades and awnings in a unit | SSSi = SSli + SSri where SSli and SSri are the sums of the sunshades and awnings of the i-th unit from the L- and R-side, respectively. |
SPP | The sum of the potted plants in a unit | SPPi = PPli + PPri where PPli and PPri are the sums of the potted plants of the i-th unit from the L- and R-side, respectively. | |
Cultural element usage | SDE | The sum of the wall decoration paintings and sculptures in a unit | SDEi = DEli + DEri where DEli and DEri are the sums of the cultural decoration paintings and sculptures of the i-th unit from the L- and R-side, respectively. |
Social Sensing Index | R2 | Adjusted R2 | Durbin-Watson Value |
---|---|---|---|
DMQ | 0.439 | 0.406 | 1.976 |
DCC | 0.463 | 0.431 | 2.119 |
WCQ | 0.255 | 0.201 | 2.076 |
WSI | 0.183 | 0.124 | 1.949 |
Social Sensing Index | GWR | MGWR | ||||
---|---|---|---|---|---|---|
R2 | Adjusted R2 | AIC | R2 | Adjusted R2 | AIC | |
DMQ | 0.786 | 0.709 | 300.633 | 0.800 | 0.744 | 274.549 |
DCC | 0.819 | 0.746 | 281.044 | 0.812 | 0.755 | 268.980 |
WCQ | 0.377 | 0.271 | 357.567 | 0.502 | 0.374 | 343.209 |
WSI | 0.245 | 0.134 | 378.724 | 0.364 | 0.242 | 364.564 |
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Li, K.; Lin, Y. Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning. Buildings 2025, 15, 1522. https://doi.org/10.3390/buildings15091522
Li K, Lin Y. Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning. Buildings. 2025; 15(9):1522. https://doi.org/10.3390/buildings15091522
Chicago/Turabian StyleLi, Keran, and Yan Lin. 2025. "Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning" Buildings 15, no. 9: 1522. https://doi.org/10.3390/buildings15091522
APA StyleLi, K., & Lin, Y. (2025). Mapping the Impact of Spontaneous Streetscape Features on Social Sensing in the Old City of Quanzhou, China: Based on Multisource Data and Machine Learning. Buildings, 15(9), 1522. https://doi.org/10.3390/buildings15091522