Forestry, Faculty of
Non UBC
Forest Resources Management, Department of
DSpace
ISPRS International Journal of Geo-Information 13 (12): 452 (2024)
Zhang, Shawei
Chen, Jiawen
Cai, Yuxuan
Wen, Yuhan
Niu, Jiaqi
Chen, Mingze
This study explores the intricate relationship between urban amenities and the transformation of urban green spaces (UGS) in Guangzhou, China, over the decade from 2013 to 2022. Amid rapid urbanization, maintaining and expanding green spaces has become increasingly challenging, especially in densely populated urban centers. This research utilizes remote sensing data and Point of Interest (POI) data to assess how different types of urban amenities influence UGS dynamics based on geospatial analytics. The study focuses on the central districts of Guangzhou, a city facing significant urban development pressures, to provide a nuanced understanding of these interactions. Employing both Ordinary Least Squares (OLS) regression and Random Forest (RF) models, the analysis examines the impact of 23 categories of POIs on the spatial and temporal changes in UGS. Key findings reveal that amenities such as auto repair shops, shopping services, and transit facilities are negatively correlated with UGS, indicating that their presence may contribute to the reduction in green space. Conversely, amenities like scenic spots and life services show a positive correlation, suggesting they might support the preservation or expansion of green spaces. The results underscore the dual role of urban amenities in both supporting and constraining green space development, highlighting the need for carefully balanced urban planning strategies. This study provides valuable insights for policymakers and urban planners aiming to promote sustainable urban growth while preserving essential green spaces, ensuring that urban environments remain livable and ecologically resilient.
Citation: Zhang, S.; Chen, J.; Cai, Y.;Wen, Y.; Niu, J.; Chen, M. Assessingthe Association Between UrbanAmenities and Urban Green SpaceTransformation in Guangzhou. ISPRSInt. J. Geo-Inf. 2024, 13, 452. https://doi.org/10.3390/ijgi13120452Academic Editor: Wolfgang KainzReceived: 8 October 2024Revised: 23 November 2024Accepted: 12 December 2024Published: 15 December 2024Copyright: © 2024 by the authors.Published by MDPI on behalfof the International Society forPhotogrammetry and Remote Sensing.Licensee MDPI, Basel, Switzerland.This article is an open accessarticle distributed under the termsand conditions of the CreativeCommons Attribution (CC BY) license(https://creativecommons.org/licenses/by/4.0/).ArticleAssessing the Association Between Urban Amenities and UrbanGreen Space Transformation in GuangzhouShawei Zhang 1, Jiawen Chen 2, Yuxuan Cai 3 , Yuhan Wen 4, Jiaqi Niu 5 and Mingze Chen 6,*1 School of Architecture & Applied Arts, Guangzhou Academy of Fine Arts, Guangzhou 510006, China;zhang_shawei@gzarts.edu.cn2 College of Engineering + College of Environmental Design, University of California Berkeley,Berkeley, CA 94720, USA; jiawen_chen@berkeley.edu3 Division of the Social Sciences, University of Chicago, Chicago, IL 60637, USA; yuxuanc@uchicago.edu4 School of Planning, Design & Construction, Michigan State University, 552 W Circle Dr.,East Lansing, MI 48824, USA; wenyuhan@msu.edu5 The Bartlett School of Architecture, University College London, Gower Street, London WC1E 6BT, UK;ucbqniu@ucl.ac.uk6 Department of Forest Resources Management, University of British Columbia,Vancouver, BC V6T 1Z4, Canada* Correspondence: mingze.chen@ubc.ca; Tel.: +1-235-965-2346Abstract: This study explores the intricate relationship between urban amenities and the transforma-tion of urban green spaces (UGS) in Guangzhou, China, over the decade from 2013 to 2022. Amidrapid urbanization, maintaining and expanding green spaces has become increasingly challenging,especially in densely populated urban centers. This research utilizes remote sensing data and Point ofInterest (POI) data to assess how different types of urban amenities influence UGS dynamics based ongeospatial analytics. The study focuses on the central districts of Guangzhou, a city facing significanturban development pressures, to provide a nuanced understanding of these interactions. Employingboth Ordinary Least Squares (OLS) regression and Random Forest (RF) models, the analysis examinesthe impact of 23 categories of POIs on the spatial and temporal changes in UGS. Key findings revealthat amenities such as auto repair shops, shopping services, and transit facilities are negativelycorrelated with UGS, indicating that their presence may contribute to the reduction in green space.Conversely, amenities like scenic spots and life services show a positive correlation, suggesting theymight support the preservation or expansion of green spaces. The results underscore the dual roleof urban amenities in both supporting and constraining green space development, highlighting theneed for carefully balanced urban planning strategies. This study provides valuable insights forpolicymakers and urban planners aiming to promote sustainable urban growth while preservingessential green spaces, ensuring that urban environments remain livable and ecologically resilient.Keywords: urban green spaces; urban amenities; remote sensing; spatial analysis1. IntroductionIn recent years, the acceleration of urbanization has led to a series of environmentalissues, making the maintenance and construction of urban green spaces (UGS) a crucialmeans for urban managers to govern and improve residents’ living environments. UGS,such as parks, gardens, and nature reserves, are integral parts of the urban ecosystem andplay a vital role in enhancing urban livability and sustainability [1]. They provide numer-ous ecological, environmental, and economic benefits, including improving air quality,regulating temperature, creating wildlife habitats, and increasing property values [2–6].Additionally, green spaces contribute to the physical and mental health of urban residentsand enhance their well-being by offering recreational opportunities, aesthetic enjoyment,and social interaction spaces [7–11].ISPRS Int. J. Geo-Inf. 2024, 13, 452. https://doi.org/10.3390/ijgi13120452 https://www.mdpi.com/journal/ijgiISPRS Int. J. Geo-Inf. 2024, 13, 452 2 of 18However, many densely populated cities face significant challenges in maintaining andexpanding green spaces, which are often threatened by ever-expanding urbanization [12,13].To address these challenges, effective strategies for the protection and preservation of UGSare essential. Implementing sustainable urban planning policies that prioritize green spaceconservation can mitigate the negative impacts of urbanization. For instance, integratingUGS into urban development plans and adopting nature-based solutions can enhanceurban resilience [10]. Urban amenities, including schools, hospitals, shopping centers,public transportation facilities, and entertainment venues, play a critical role in urbandevelopment [14]. The distribution and accessibility of these amenities can influence landuse patterns, thereby affecting green space transformation. On one hand, the presenceof urban amenities can enhance the accessibility and utilization of green spaces [15,16].Amenities around green spaces can make it easier for residents to visit and use these areasfor leisure and social activities. The strategic layout of amenities can increase the valueand functionality of green spaces, promoting their preservation and integration into theurban structure, driving economic development and regional prosperity [17–19]. Li et al.(2022) emphasize the importance of multifunctional green infrastructure in promotingecological sustainability and social well-being [14]. On the other hand, due to the limitedland resources in cities, there may be competition between amenities and green spaces,especially in high-density urban centers [20]. The construction of amenities might exertpressure on green spaces, reducing the area available for greening and recreation, and im-pacting the scale and function of green spaces [21]. Some amenities (such as transportationhubs and commercial areas) might cause environmental pollution and ecological damage,like soil compaction, water source pollution, and loss of wildlife habitats, affecting theenvironmental quality and ecological function of green spaces [22–24]. Therefore, to achievesustainable and equitable urban development, it is essential to comprehensively considerthe impact of urban amenities on green space transformation, monitor and understandtheir spatiotemporal dynamics, and formulate reasonable urban planning and policies toensure the coordinated development of green spaces and amenities.Remote sensing technology has become a powerful tool for monitoring land use andland cover changes, including green space transformation. Over time, remote sensing,through satellite and aerial imagery, can capture large-scale, high-resolution spatial data,providing valuable insights into the extent, distribution, and changes in urban greenspaces [25–28]. Guo (2019) used remote sensing, LBS, and GIS data to find that spatialstructure, building density, road density, and green space coverage significantly affectPM2.5 pollution exposure [29]. Techniques like Normalized Difference Vegetation Index(NDVI), land use/land cover (LULC) classification, and temporal analysis are widely usedto quantify and assess the health and dynamics of UGS. For example, NDVI uses data frominfrared and visible light bands to effectively assess vegetation health and coverage [24].The LULC classification method categorizes remote sensing images into different landuse types, such as green spaces, built-up areas, and water bodies, through classificationalgorithms [30]. This method visually displays the spatial distribution and changes ofurban green spaces. Temporal analysis, by examining remote sensing data over a period oftime, can reveal the dynamic process of green space changes, identifying trends of greenspace expansion or reduction [31,32].According to the above, the distribution of urban amenities is considered a key com-ponent of urban development patterns, encompassing the locations of various urbaninfrastructures that support residents’ activities. Notably, the establishment of urban ameni-ties has a dual effect on the development of UGS. Studying the relationship between urbanamenities and LULC is crucial for understanding the spatial patterns and environmentalchanges during urbanization, as the distribution and density of amenities affect land useefficiency and the urban ecological environment. There are multiple ways to obtain urbanamenities data. Through comprehensive evaluation, the most appropriate data sourcesand methods can be chosen based on the research questions, enhancing the accuracy andISPRS Int. J. Geo-Inf. 2024, 13, 452 3 of 18credibility of the research results. Table 1 lists the currently widely used literature forexploring urban amenities and land use transformation.Table 1. Studies exploring urban amenities and land use transformation.Study Topic Types of Data Source Data Method PublicationResidential livingservice amenitiesCatering and shops, entertainment andleisure, education and healthcare, publicservice and personal carePOI data fromsocial mediaGIS, spatialanalysis model [33]Land usetransformation Commercial areaPOI data from opendata map GIS [34]Residential livingservice amenitiesLiving service amenities in a certainresidential areaPOI data from opendata map GIS [35]Distribution of urbanamenitiesService and consumer goods, publicservice, transportation service, andtourist attractionPOI data from opendata mapNDVI, NDBI,multivariateanalytical models[14]Distribution of urbanamenitiesCommercial, services, offices, openspaces, public facilitiesPOI data from opendata map GIS [36]Public transport Urban street POI data frommap data GIS, spatial analysis [37]Public transportSustenance, education, transportation,healthcare, entertainment, finance,commerce and othersPOI data from OSMopen data mapThree multivariateanalytical models [38]The spatial distributionof an urban amenityStreet trees, public street andwater polygonsRemote sensing,cadastral dataRemote sensingtechnique [39]Urban livabilityUrban transit stations, urban transitlines, commercial facilities, medicalfacilities, schools, parks and squares,markets, road intersections, chemicalfacilities or gas stationsRemote sensingAdvanced LandObservationSatellite (ALOS)imagesRemote sensingtechnique, GIS [40]Urban ResidentialLand SuitabilityShopping centers, parks, andmedical servicesRemotely-SensedImages, SocialSensing data(Tencent userdensity (TUD) andPoint of Interest(POI) data),OpenStreetMapRoad NetworkImage processing andspatial analysis [41]Club-based green space Golf coursesWebsite database,Baidu map,questionnairesGIS, survey [42]Transportation physicalactivity amongolder personsStores, streets, places for walking andcycling, neighborhood surroundings,transit stop, commercial, industrial,recreation and parkQuestionnaires, GIS GIS, survey [43]Urban life satisfactionLight, science, education, culture,medical care, government institutions,living services, andtransportation infrastructureNighttime lightdata, POI data, andSina Weibo dataGIS [44]Land usetransformation Reservoirs, roads, railways, airports,Government data,Landsat images,Google EarthGIS, NDVI [45]ISPRS Int. J. Geo-Inf. 2024, 13, 452 4 of 18POI data offer significant advantages over other data sources, including real-timeavailability, comprehensive coverage, high spatial resolution, ease of access, and richsupplementary information. Derived from regularly updated online map services andnavigation applications, POI data provide precise geographic coordinates of various urbanfacilities—such as restaurants, schools, hospitals, shops, and parks—enabling detailed spa-tial analyses at a micro level. Many POI data sources (e.g., Google Maps, OpenStreetMap)offer API interfaces or open data, making it more convenient for researchers to access andprocess this information compared to some government datasets or remote sensing data.However, despite the widespread use of remote sensing data and POI data in urbanland use mapping and distribution studies, there is a significant research gap in effec-tively integrating these two data sources to explore internal urban changes and the spatialcorrelation between the distribution of urban amenities and urban green spaces. Mostexisting studies focus on either remote sensing data or POI data independently, whichlimits the understanding of the complex interactions between urban development andenvironmental factors. Furthermore, the lack of integrated methodologies hinders theability to comprehensively analyze urban dynamics and reveal nuanced patterns that couldinform sustainable urban planning and management.To address this gap, this study aims to integrate remote sensing technology with POIdata to analyze internal urban changes and investigate the spatial relationships betweenurban amenities distribution and urban green spaces in Guangzhou. This study will focuson the following three questions:(1) How can remote sensing technology be used to monitor changes in green space areasin Guangzhou, and how did the UGS change in the past decade?(2) How can the spatial correlation between urban amenities distribution and green spacetransformation in Guangzhou be analyzed using POI data?(3) How do the different types of urban amenities in Guangzhou affect the spatiotemporalcharacteristics of green space transformation in specific areas?2. Methods2.1. Study AreaThe study focuses on Guangzhou, China, a significant port city for foreign trade.As of the end of 2023, Guangzhou had a resident population of 18.827 million (2023Guangzhou National Economic and Social Development Statistical Bulletin, sourced fromthe Guangzhou Statistics Bureau website. (http://tjj.gz.gov.cn/stats_newtjyw/tjsj/tjgb/qstjgb/content/mpost_9567759.html/, accessed on 15 September 2024). Located near theestuary of the Pearl River Basin, the southern part of the city features an extensive coastalalluvial plain (Overview of Guangzhou’s Physical Geography, Guangzhou MunicipalGovernment official website. (https://www.gz.gov.cn/zlgz/gzgk/zrdl/, accessed on 20September 2024), providing ample prerequisites for the development of urban green spaces.This research primarily targets the central urban area of Guangzhou, including YuexiuDistrict, Tianhe District, and Haizhu District (Figure 1). This area also forms a core partof the Guangdong−Hong Kong−Macao Greater Bay Area and has faced challenges ofimbalanced development and rapid urbanization over the past two decades, leading toan uneven distribution of green spaces and urban infrastructure. In recent years, theGuangzhou government, through the Guangzhou Forestry and Landscape Bureau, hasformulated green space system plans and specialized forestry and landscape plans (Publicannouncement of the “Guangzhou Green Space System Plan (2020–2035)”. (http://lyylj.gz.gov.cn/, accessed on 22 September 2024). These efforts aim to improve the environment,enhance public health, and upgrade urban infrastructure. According to 2022 data from theGuangzhou Forestry and Landscape Bureau, the greenery coverage rate in built-up areasreached 44.2%, and the service radius coverage rate of park green spaces achieved 81.2%.Guangzhou continues to advance the construction of a park city, including the restoration of107,600 square meters of green space and the greening renovation of 170,000 square meters(Official website of the Guangzhou Forestry and Landscape Bureau, Government AffairsISPRS Int. J. Geo-Inf. 2024, 13, 452 5 of 18Disclosure. (http://lyylj.gz.gov.cn/, accessed on 20 September 2024). These characteristicsprovide an opportunity to assess the association between urban amenities and urban greenspace transformation.ISPRS Int. J. Geo-Inf. 2024, 13, x FOR PEER REVIEW 5 of 19 advance the construction of a park city, including the restoration of 107,600 square meters of green space and the greening renovation of 170,000 square meters (Official website of the Guangzhou Forestry and Landscape Bureau, Government Affairs Disclosure. (http://lyylj.gz.gov.cn/, accessed on 20 September 2024). Thes characteristics provide an opportunity to assess the association between urban amenities and urban green space transformation. Figure 1. Study area. As shown in Figure 2, the workflow is divided into several steps. First, we obtained remote sensing data on land use in Guangdong Province from 2013 to 2022. Based on the ArcGIS Pro platform, we identified different land use types and extracted the green space boundaries relevant to this study. Second, we generated a hexagonal grid covering the specified area, with each hexagonal cell having an area of 210,000 square meters. By calculating the green space area and proportion within each hexagonal grid, we were able to analyze annual changes for each grid cell. Third, we downloaded 2024 Point of Interest (POI) data for Guangzhou from the Gaode Map Open Platform. Using the same hexagonal grid as the boundary, we calculated the number and types of POIs within each grid cell for each year from 2013 to 2022. Fourth, we conducted an Ordinary Least Squares (OLS) correlation analysis between the annual UGS area in each grid and 23 types of POI data from 2013 to 2022. Furthermore, we employed the Random Forest method to compare the results, providing a more accurate understanding of the relationship between urban amenities and urban green space transformation in Guangzhou. This analysis includes using R² to compare the explanatory power of models across different years, as well as changes in model feature importance, which helps to more accurately identify and track the key factors influencing model performance and their dynamic changes. R2 serves as a unified standard for evaluating model performance at various time points. As a widely used metric in regression analysis, it facilitates the tracking and analysis of trends in model explanatory power, thus providing richer information on the evolving characteristics of the research subject over time. Figure 1. Study area.As shown in Figure 2, the workflow is divided into everal steps. First, w obtainedremote sensing data on land use in Guangdong Province from 2013 to 2022. Based onthe ArcGIS Pro platform, we identified different land use types and extracted the greenspace boundaries relevant to this study. Second, we generated a hexagonal grid coveringthe specified area, with each hexagonal cell having an area of 210,000 square meters. Bycalculating the green space area and proportion within each hexagonal grid, we were ableto analyze annual changes for each grid cell. Third, we downloaded 2024 Point of Interest(POI) data for Gu gzhou from the Gaode Map Open Platform. Using th same hexagonalgrid as the boundary, we calculated the number and types of POIs within each grid cellfor each year from 2013 to 2022. Fourth, we conducted an Ordinary Least Squares (OLS)correlation analysis between the annual UGS area in each grid and 23 types of POI datafrom 2013 to 2022. Furthermore, we employed the Random Forest method to comparethe results, providing a more accurate understanding of the relationship between urbanam nities and urb n green space transformation in Guangzhou. This analysis includesusing R² to compare the explanatory power of models across different years, as well aschanges in model feature importance, which helps to more accurately identify and trackthe key factors influencing model performance and their dynamic changes. R2 serves as aunified standard for evaluating model performance at various time points. As a widelyused metric in regression analysis, it facilitates the tracking and analysis of trends in modelexplanatory power, thus providing richer information on the evolving characteristics of theresearch subject over time.ISPRS Int. J. Geo-Inf. 2024, 13, 452 6 of 18ISPRS Int. J. Geo-Inf. 2024, 13, x FOR PEER REVIEW 6 of 19 Figure 2. Research workflow. 2.2. Urban Green Space Transformation 2013–2022 Using Remote Sensing Data Based on previous research referenced, the method for obtaining remote sensing data on land use in Guangdong Province was based on the 30 m annual land cover dataset of China, produced using 335,709 Landsat scenes available on the Google Earth Engine. This dataset primarily uses a Random Forest classifier for land classification, with an accuracy of approximately 80% (30 m annual land cover and its dynamics in China from 1990 to 2019). The dataset includes annual data from 2013 to 2022 and distinguishes six land types: cropland, forest, grassland, water bodies, built-up land, and unused land. Using the ras-ter-to-polygon tool, we merged the grassland and forest layers to form the green space component. Subsequently, we organized the annual remote sensing data from 2013 to 2022. These steps facilitate a deeper investigation of the characteristics of urban green space transformation within stable spatial relationships. Figure 3 presents the annual dis-tribution of increases and decreases in urban green space from 2014 to 2021. The box plots clearly depict the variation in green space changes within grid cells each year. Green box plots represent grid cells where green space increased, while red box plots show those where green space decreased. The figure illustrates significant fluctuations in green space changes during the period from 2014 to 2021, with varying levels of increase and decrease across different years. These trends reflect the dynamic nature of urban green space, in-fluenced by factors such as urban expansion and infrastructure development. Although 2022 was included in the analysis, the data show no significant change between 2021 and 2022. This lack of variation is likely attributable to the impact of the COVID-19 pandemic, which may have caused a slowdown or halt in urban planning and construction activities, resulting in stable green space for that year. Figure 2. Research workflow.2.2. Urban Green Space Transformation 2013–2022 Using Remote Sensing DataBased on previous research referenced, the method for obtaining remote sensing dataon land use in Guangdong Provinc was based on the 30 m annual land cover dataset ofChina, produced using 335,709 Landsat scenes available o the Google Earth Engine. Thisdataset primarily uses a Random Forest classifier for land classification, with an accuracyof approximately 80% (30 m annual land cover and its dynamics in China from 1990 to2019). The dataset includes annual data from 2013 to 2022 and distinguishes six landtypes: cropland, forest, grassland, water bodies, built-up land, and unused land. Usingthe raster-to-polygon tool, we merged the grassland and forest layers to form the greenspace component. Subsequently, we organized the annual remote sensing data from 2013 to2022. These steps facilit te a deeper investigation of the characteristics of urb n green spacetransformation withi stable spatial relationships. Figure 3 presents the annual distributionof incre ses and decreases in urban green space from 2014 to 2021. The box plots clearlydepict the variation in green space changes within grid cells each year. Green box plotsrepresent grid cells where green space increased, while red box plots show those wheregreen space decreased. The figure illustrates significant fluctuations in green space changesduring the period from 2014 to 2021, with varying levels of increase and decrease acrossdifferent years. These trends reflect the dynamic nature of urban green space, influencedby factors such as urban expansion and infrastructure development. Although 2022 wasincluded in the analysis, the data s ow no significant change between 2021 and 2022. Thisl ck of variation is likely at ributable to the impact of the COVID-19 pandem c, which mayhave aused a slowdown or halt in urban planning and construction activities, resulting instable green space for that year.ISPRS Int. J. Geo-Inf. 2024, 13, 452 7 of 18ISPRS Int. J. Geo-Inf. 2024, 13, x FOR PEER REVIEW 7 of 19 Figure 3. The annual changes in green space from 2013 to 2022 (Appendix A: Maps by year). 2.3. POIs According to existing research, there is a certain relationship between urban green spaces and the density, diversity, and accessibility of urban Points of Interest (POIs). In previous studies, the density of specific POIs per unit area is often used to measure rele-vant special indicators, such as exploring the number of parks as an indicator of urban livability. Therefore, we used function-based data (i.e., POIs) to calculate variables for ur-ban green space transformation, including functional density, functional diversity, and accessibility. We downloaded 2024 POI data (a total of 1,015,533 records) from the Gaode Map Open Platform. Each POI included four types of information: name, category, coor-dinates, and classification. After data preprocessing, including deduplication and re-Figure 3. The annual changes in green space from 2013 to 2022 (Appendix A: Maps by year).2.3. POIsAccording to existing research, there is a certain relationship between urban greenspaces and the density, diversity, and accessibility of urban Points of Interest (POIs). Inprevious studies, the density of specific POIs per unit area is often used to measure relevantspecial indicators, such as exploring the number of parks as an indicator of urban livability.Therefore, we used function-based data (i.e., POIs) to calculate variables for urban greenspace transformation, including functional density, functional diversity, and accessibility.We downloaded 2024 POI data (a total of 1,015,533 records) from the Gaode Map OpenPlatform. Each POI included four types of information: name, category, coordinates, andclassification. After data preprocessing, including deduplication and re-aggregating statis-tics based on the study area boundaries, we retained 274,308 valid records within thedefined study range. All data were grouped into 23 clusters, including shopping services,living services, dining services, corporate enterprises, traffic facilities, indoor facilities, andISPRS Int. J. Geo-Inf. 2024, 13, 452 8 of 18transportation services. The Gaode Maps POI classification system consists of three hier-archical levels: primary categories, secondary categories, and tertiary categories. Amongthese, there are 23 primary categories, 267 secondary categories, and 869 tertiary categories.All data are grouped into 23 clusters, including shopping services, living services, diningservices, corporate enterprises, traffic facilities, indoor facilities, and transportation services.In this study, we used the types, total count, and density of POIs within each hexagonalgrid as indicators. A density index was employed to evaluate the 23 categories of urbanPOIs within hexagonal grids of radius r = 300. First, we measured the spatial distributionand spatial quantity of all POIs based on the tessellated hexagonal grids, allowing us toinfer the relationship between green space area and POI data from the variations in thedata. Subsequently, kernel density analysis was conducted to further assess the relationshipbetween urban amenities and urban green space.2.4. Regression (OLS, RF)2.4.1. OLSOrdinary Least Squares (OLS) is a fundamental method used in statistics to estimatethe parameters of a linear regression model. It is a powerful and widely used technique forfitting linear models to data by minimizing the sum of squared errors between observed andpredicted values. Previous studies have employed OLS to evaluate and visualize the corre-lation between one dependent variable and one or more variables with a prediction [32,46].The OLS common algorithm is based on Equation (1).Υ = Xβ+ ε (1)where Y is the vector of observed dependent variable values. X is the matrix of independentvariables (including a column of ones if there’s an intercept). β is the vector of unknownregression coefficients to be evaluated. ϵ is the vector of errors or residuals.The OLS estimates for β can be computed using Formula (2).βˆ =(XTX)−1XTy (2)2.4.2. Random ForestAdmittedly, OLS tends to ignore spatial characteristics of data, while previous studieshave indicated that a Random Forest (RF) regression model serves as a productive methodto test geospatial data with a high prediction performance, and it is easier to estimatefeature importance [47,48]. RF is a supervised machine learning method that combinesmultiple decision trees to improve predictive performance. RF can handle quantities ofindependent decision trees during the training [49], and each decision is made by collectinga subset of training samples from the original large dataset [50]. It is less prone to overfittingcompared to individual decision trees. Overall, compared to traditional models, it providesa robust classification process and validates the suitability of the data for exploring featureimportance [51,52].Generally, OLS is a simple and interpretable model suited for linear relationships,while Random Forest is powerful, flexible, and capable of handling complex, non-lineardata with interactions. In this study, we applied OLS and RF to measure the relationshipbetween UGS and urban amenities in a long time series. The process was run in the ArcGIS,after which the final value of R2 was used to measure the model performance.3. Results3.1. Verify the Relationship Between Urban Green Space and InfrastructureTo reveal the relationship between changes in urban green space (UGS) and urbanamenities development, and to uncover the underlying spatial distribution logic, weconducted an Ordinary Least Squares (OLS) correlation analysis between annual UGS areaand 23 types of POI data from 2013 to 2022 (Table 2). The figure illustrates the correlationISPRS Int. J. Geo-Inf. 2024, 13, 452 9 of 18data between UGS and POIs over ten consecutive years, showing that from 2013 to 2023,the R2 value decreases exponentially year by year, indicating a reduction in explanatorypower. Significant statistical effects were observed for POI types such as auto repair, livingservices, shopping services, traffic facilities, and scenic spots, where p-values for /t/ wereless than 0.05 at a significant level, demonstrating stronger explanatory power.Table 2. Descriptive summary of variables.Variable Description Mean Std. Dev. Max SourceUrban Green Spaces (UGS)Direct Variable2014_UGS_area 25,958.63718 54,525.45988 0 216,605.312Scraped fromGoogle EarthEngine2015_UGS_area 25,712.73968 54,324.91852 0 216,605.3122016_UGS_area 24,720.57214 53,769.22216 0 216,605.3122017_UGS_area 24,101.39029 53,383.80103 0 216,605.3122018_UGS_area 23,629.00769 53,294.3073 0 216,605.3122019_UGS_area 0.023062173 0.053141277 0 0.2166053122020_UGS_area 23,083.0739 53,196.02921 0 216,605.3122021_UGS_area 22,676.82557 52,820.02104 0 216,605.3122022_UGS_area 22,676.82557 52,820.02104 0 216,605.312Points of Interest (POIs)Indirect Variableevent_activities 0.002277904 0.047691115 0 1Scraped from theGaode Map OpenPlatformtransport_facility_services 11.60971906 15.18045945 0 79accommodation_services 3.737281701 7.763462641 0 121sports_leisure_services 3.688686409 5.516648017 0 50public_facilities 1.987091875 4.301584899 0 64companies_enterprises 18.64616553 29.41172348 0 311medical_health_services 4.307517084 6.79947564 0 49business_residential 8.461655277 10.89021317 0 54place_name_address_info 13.12908125 17.9222836 0 207indoor_facilities 10.53986333 63.02276303 0 759motorcycle_services 0.041002278 0.233556286 0 3government_organizations_social_groups 7.255884586 12.25813937 0 104auto_services 3.203492787 11.89274075 0 337auto_repair 0.596810934 1.319973578 0 12auto_sales 0.45254366 1.850056267 0 32lifestyle_services 30.29005315 46.46455982 0 402science_education_cultural_services 8.377372817 12.43595892 0 145shopping_services 41.50645406 65.224182 0 554transit_facilities 10.62034928 12.80263362 0 68roadside_facilities 0.050873197 0.343846797 0 4finance_insurance_services 2.496583144 5.474952264 0 68scenic_spots 1.146545178 2.693106812 0 35dining_services 26.13515566 42.27781132 0 377Auto repair, traffic facilities, and shopping services exhibited negative correlations,while scenic spots and living services showed positive correlations. From the POI attributetable, the scenic spots category represents parks, plazas, and related attractions, whichlargely determine UGS changes. Therefore, the more scenic spots within a hexagonal grid,the more significant the changes in UGS. The R2 value decreased from 0.166 in 2013 to 0.140in 2022, showing a reduction in explanatory power. The R2 value declined more rapidlybetween 2013 and 2018, with the sharpest drop occurring between 2016 and 2017, wherethe R2 value decreased by 0.007. In the four years following 2018, the R2 value changedrelatively little, reflecting the fluctuation of UGS over the 10-year period (Figure 4)ISPRS Int. J. Geo-Inf. 2024, 13, 452 10 of 18ISPRS Int. J. Geo-Inf. 2024, 13, x FOR PEER REVIEW 10 of 19 table, the scenic spots category represents parks, plazas, and related attractions, which largely determine UGS changes. Therefore, the more scenic spots within a hexagonal grid, the more significant the changes in UGS. The R2 value decreased from 0.166 in 2013 to 0.140 in 2022, showing a reduction in explanatory power. The R2 value declined more rap-idly between 2013 and 2018, with the sharpest drop occurring between 2016 and 2017, where the R2 value decreased by 0.007. In the four years following 2018, the R2 value changed relatively little, reflecting the fluctuation of UGS over the 10-year period (Figure 4) Figure 4. OLS regression coefficients across years. 3.2. The Change in Urban Green Space Is Related to Social Background The Random Forest algorithm, an ensemble method composed of decision trees, combines multiple weak classifiers to achieve higher accuracy and generalization perfor-mance for the overall model. Using Random Forest enables a better output of feature im-portance, identifying the most explanatory features. From 2013 to 2022, the R2 value con-tinued to show a year-by-year decreasing trend (Table 3). As a significant part of the urban space, UGS exhibited more noticeable changes in R2 values in Table 3 compared to the OLS correlation analysis, making the Random Forest analysis more suitable for this study. Spe-cifically, the R2 value decreased from 0.2929 in 2013 to 0.2475 in 2022, with a difference of 0.0454. During the period from 2013 to 2018, the R2 value gradually decreased, but from 2018 to 2019, it increased against the trend, rising from 0.2547 to 0.2611. From 2020 to 2021, the change in the R2 value was minimal, and in 2022, it remained unchanged from 2021. Table 3. Comparison of R2 values between least square method and random forest. 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 OLS R2 0.166 0.162 0.160 0.153 0.150 0.146 0.143 0.143 0.140 0.140 RF R2 0.2929 0.2826 0.2777 0.2751 0.2658 0.2547 0.2611 0.2468 0.2475 0.2475 The fluctuation in the data reflects the relationship between urban green space and urban amenities in the context of societal developments. Between 2013 and 2018, more active urban development activities led to a negative correlation between green space and infrastructure development, with some erosion of urban green spaces. However, by 2018, the total area of urban green spaces increased relative to the total infrastructure, leading to green space development over this period. Beginning in early 2020, the global COVID-Figure 4. OLS regr ssion coeffici nts across years.3.2. The Change in Urban Green Space Is Related to Social BackgroundThe Random Forest algorithm, an ensemble method composed of decision trees, com-bines multiple weak classifiers to achieve higher accuracy and generalization performancefor the overall model. Using Random Forest enables a better output of feature importance,identifying th mo t expl at ry features. From 2013 to 2022, the R2 value continued toshow a year-by-year decreasing trend (Table 3). As a significan part of the urban space,UGS exhibited more noticeable changes in R2 values in Table 3 compared to the OLS correla-tion analysis, making the Random Forest analysis more suitable for this study. Specifically,the R2 value decreased from 0.2929 in 2013 to 0.2475 in 2022, with a difference of 0.0454.During the period from 2013 to 2018, the R2 value gradually decreased, but from 2018 to2019, it increased against the trend, rising from 0.2547 to 0.2611. From 2020 to 2021, thechange in the R2 value was minimal, and in 2022, it remained unchanged from 2021.Table 3. Comparison of R2 v lues between least square method and ra dom orest.2013 2014 2015 2016 2017 2018 2019 2020 2021 2022OLS R2 0.166 0.162 0.160 0.153 0.150 0.146 0.143 0.143 0.140 0.140RF R2 0.2929 0.2826 0.2777 0.2751 0.2658 0.2547 0.2611 0.2468 0.2475 0.2475The fluctuation in the data reflects the relationship between urban green space andurban amenities in the context of societal developments. Between 2013 and 2018, moreactive urban development activities led to a negative correlatio between gre n space andinfrastructure d vel pment, with some erosion of urban green sp ces. However, by 2018,the t tal area of urban green sp ces increased relative to the total infrastructure, leading togreen space development over this period. Beginning in early 2020, the global COVID-19pandemic inevitably restricted urban development activities, which is also reflected in theR2 values. This aligns with our expectations, and from 2021 to 2022, the R2 value remainedunchanged (Figure 5).ISPRS Int. J. Geo-Inf. 2024, 13, 452 11 of 18ISPRS Int. J. Geo-Inf. 2024, 13, x FOR PEER REVIEW 11 of 19 19 pandemic inevitably restricted urban development activities, which is also reflected in the R2 values. This aligns with our expectations, and from 2021 to 2022, the R2 value re-mained unchanged (Figure 5). Figure 5. Changes in Random Forest characteristics from 2013 to 2022. 4. Discussion This study examines the intricate relationship between urban amenities and the transformation of UGS in Guangzhou from 2013 to 2022, with the goal of understanding how different types of amenities impact green space dynamics. Through the use of remote sensing data, Point of Interest (POI) data, and both Ordinary Least Squares (OLS) regres-sion and Random Forest (RF) models, the analysis has identified significant correlations between various urban amenities and changes in UGS. The findings provide several key insights into the complex interactions between urban development and green space i re 5. s i a rest characteristics fro 2013 to 2022.4. DiscussionThis study examines the intricate relationship between urban amenities and the trans-formation of UGS in Guangzhou from 2013 to 2022, with the goal of understanding howdifferent types of amenities impact green space dynamics. Through the use of remote sens-ing data, Point of Interest (POI) data, and both Ordinary Least Squares (OLS) regressionand Random Forest (RF) models, the analysis has identified significant correlations betweenvarious urban amenities and changes in UGS. The findings provide several key insightsinto the complex interactions between urban development and green space preservation,highlighting both challenges and opportunities for sustainable urban planning.4.1. Impact of Urban Amenities on UGS TransformationOne of the main findings of this study is the dual role of urban amenities in eithersupporting or limiting UGS. Amenities such as auto repair shops, shopping services, andISPRS Int. J. Geo-Inf. 2024, 13, 452 12 of 18transit facilities were found to have a negative correlation with UGS, indicating that theirpresence may contribute to the reduction in green spaces. This result is consistent withprevious research suggesting that urban infrastructure development, particularly in denselypopulated areas, competes with land designated for green spaces [17,53]. The expansionof transportation facilities, in particular, often requires substantial land use, which canlead to the encroachment upon or fragmentation of UGS, further reducing their ecologicalfunctionality [54].Conversely, amenities such as scenic spots and life services showed a positive corre-lation with UGS. Scenic spots, which often include parks, plazas, and other recreationalareas, seem to play a vital role in promoting the preservation or even expansion of greenspaces. This aligns with findings from other studies, where the presence of aestheticallyand recreationally valuable amenities has been shown to increase public demand for greenspaces [55], prompting local governments and developers to prioritize their conservation.This dynamic suggests that strategic urban planning that incorporates recreational andaesthetic amenities could be a pathway toward maintaining and enhancing UGS in urbancenters [17].In contrast, certain amenities analyzed in this study, such as medical health services,dining services, and public facilities, showed no significant correlation with UGS changes.This lack of impact may stem from their development patterns, which are generally lessland-intensive and tend to adapt to existing urban infrastructure without directly encroach-ing on green spaces [56]. For example, medical health services and dining services oftenoccupy pre-established urban plots rather than requiring the conversion of green space intodeveloped land [10]. Similarly, public facilities such as libraries and post offices are typicallyplanned in areas with limited competition for green space. Moreover, these amenities mayalso have localized impacts, such as the creation of small green spaces like courtyards, thatare not captured at the broader scale of this analysis. The neutral relationship observedfor these amenities highlights the need for more granular studies to uncover any subtle orcontext-specific dynamics that might exist between UGS and urban development.4.2. Temporal Dynamics of UGS ChangesThe temporal analysis reveals important trends in UGS transformation over the decadestudied. From 2013 to 2018, there was a marked decline in UGS, which can be attributed tothe rapid pace of urbanization in Guangzhou during this period. The city’s developmentboom led to increased demand for land for infrastructure and commercial purposes, furtherexacerbated by population growth and rising housing demands [57]. These factors likelyintensified the competition for land, with UGS being sacrificed in favor of more immediateurban development needs.However, starting in 2018, a notable shift occurred, with the rate of UGS declineslowing down, and in some areas, even reversing. This trend can be linked to the in-creasing awareness among both the public and policymakers of the importance of greenspaces for urban livability and environmental sustainability. Several local governmentinitiatives aimed at enhancing urban greenery, such as the “park city” concept introducedin Guangzhou, have likely contributed to the stabilization and recovery of UGS duringthis period [58]. These efforts reflect a growing recognition that green spaces are notmerely aesthetic features but essential components of urban ecosystems that provide criti-cal services, including air quality improvement, temperature regulation, and recreationalopportunities [56,59].The COVID-19 pandemic also played a role in shaping UGS dynamics, particularlyfrom 2020 onwards. Lockdown measures and restrictions on construction activities mayhave temporarily slowed down the loss of green spaces, while simultaneously underscoringthe value of accessible green areas for urban residents during times of crisis. Numerousstudies have documented the increased use of parks and green spaces during the pandemicas people sought outdoor areas for physical activity and mental well-being [60–63]. ThisISPRS Int. J. Geo-Inf. 2024, 13, 452 13 of 18shift in public behavior has reinforced the need to protect and expand UGS as an integralelement of urban resilience.4.3. Model Comparisons and InsightsThe use of both OLS and RF models in this study provided a robust methodologicalframework for assessing the relationship between urban amenities and UGS. While OLSregression is a widely used technique for evaluating linear relationships, it has limitationswhen dealing with spatially complex and non-linear data, which is where the RF modelproved advantageous. The RF model’s ability to handle non-linear relationships andinteractions between variables allowed for a more nuanced understanding of the factorsdriving UGS transformation [64].For instance, the RF model revealed that certain amenities, such as traffic facilities,had a more pronounced negative effect on UGS than initially suggested by the OLS model.This finding highlights the importance of using more sophisticated models that can capturethe multifaceted nature of urban development processes. Furthermore, the RF model’shigher predictive accuracy reinforces its suitability for urban ecological studies, whereinteractions between built environments and natural systems are often non-linear andcontext-dependent.4.4. Implications for Urban Planning and PolicyThe results of this study have significant implications for urban planning and policyin Guangzhou and other rapidly urbanizing cities. First, the identification of specificurban amenities that negatively impact UGS suggests the need for targeted interventionsto mitigate their effects. For example, policies could be implemented to incentivize theintegration of green infrastructure into the design of new transportation and commercialdevelopments [65]. Green roofs, vertical gardens, and other nature-based solutions couldhelp offset the loss of UGS while contributing to the ecological sustainability of urbanenvironments [65–68].Second, the positive correlation between recreational amenities and UGS underscoresthe potential for leveraging public spaces, such as parks and plazas, to promote green spacepreservation. Urban planners should prioritize the creation of multi-functional spaces thatnot only meet recreational and social needs but also enhance ecological connectivity andbiodiversity [69]. Integrating green spaces into the urban fabric in ways that are accessibleand beneficial to the public can help ensure their long-term protection and relevance [1].Finally, the study’s findings highlight the importance of adopting a spatial-temporalapproach to urban green space management. Through the spatial-temporal dynamic anal-ysis of urban UGS evolution, landscape pattern and driving force, it is concluded thatecological restoration has slowed down the trend of green space reduction, and it is sug-gested that decision makers should guide sustainable green space management through thisspatial-temporal approach [32]. Insights into how UGS changes in response to urbanizationand greening policies are essential for guiding sustainable urban development. By studyingthe green landscape pattern index of Shanghai over the past 25 years, combined with therelevant planning and policies of urban green space, it is concluded that rapid urbanizationand green policy jointly affect the spatial-temporal dynamics of UGS. On this basis, wecan put forward the inspiration for Shanghai’s new urban planning and policy [26,32]. Byunderstanding how UGS and urban amenities evolve over time and in relation to eachother, urban planners can develop more effective strategies for balancing urban growthwith environmental sustainability [70]. This approach is particularly important in citieslike Guangzhou, where rapid development pressures can quickly erode natural spaces ifnot carefully managed [71].4.5. Limitations and Future ResearchWhile this study provides valuable insights into the relationship between urbanamenities and UGS, it has several limitations. The reliance on POI data, for example, meansISPRS Int. J. Geo-Inf. 2024, 13, 452 14 of 18that the analysis may not fully capture informal or unregistered amenities that also play arole in shaping UGS dynamics. Future research could incorporate additional data sources,such as crowd-sourced or field survey data, to provide a more comprehensive picture ofurban amenities. On the other hand, the R2 value in this study is relatively low. Althoughurban green space and infrastructure are important components of urban development, therelationship between the two may not be significant enough. Future studies can exploremore explanatory variables to improve the interpretability of the model.Moreover, while the RF model performed well in predicting UGS changes, thereremains a need for further refinement of spatial analysis techniques that can account forthe heterogeneity of urban environments. Integrating more detailed land use data, such asbuilding heights or impervious surface cover, could enhance the accuracy of future modelsand lead to more precise policy recommendations.5. ConclusionsThis study investigates the complex interplay between urban amenities and the trans-formation of UGS in Guangzhou, China, from 2013 to 2022. Utilizing remote sensing andPoint of Interest (POI) data, along with Ordinary Least Squares (OLS) and Random Forest(RF) models, the research identifies significant correlations between various amenitiesand changes in UGS. Key findings reveal that amenities like auto repair shops and tran-sit facilities negatively impact UGS, while scenic spots and life services tend to supportgreen space preservation. The study highlights a temporal shift in UGS dynamics, witha decline from 2013 to 2018 due to rapid urbanization, followed by a stabilization andrecovery post-2018, driven by heightened public awareness and government initiatives.The use of RF modeling offers deeper insights into the non-linear relationships affectingUGS, underscoring its relevance in urban ecological research.There is a decreasing trend in the explanatory power of both OLS and Random Forestanalyses over the 10-year period, with R2 values declining from 0.166 to 0.140 and 0.2929to 0.2475, respectively. Notably, the sharpest decrease in R2 for OLS occurred between2016 and 2017 (0.007), while Random Forest showed a temporary increase from 0.2547 to0.2611 between 2018 and 2019, highlighting the dynamic relationship between urban greenspaces and urban amenities. However, due to limitations in data coverage and accuracy,incorporating high-precision remote sensing imagery and temporal changes in POI datacould significantly enhance future research. This research provides valuable implicationsfor urban planners and policymakers, emphasizing the need for strategic interventions thatbalance urban growth with green space preservation, ultimately promoting sustainableurban environments.Author Contributions: Conceptualization, Mingze Chen and Shawei Zhang; methodology, YuxuanCai and Shawei Zhang; software, Yuxuan Cai; validation, Jiawen Chen, Yuhan Wen, and Jiaqi Niu;formal analysis, Yuxuan Cai; investigation, Shawei Zhang; resources, Mingze Chen; data curation,Yuxuan Cai; writing—original draft preparation, Shawei Zhang, Jiawen Chen, Yuxuan Cai, andJiaqi Niu; writing—review and editing, Mingze Chen; visualization, Yuxuan Cai and Mingze Chen;supervision, Shawei Zhang; project administration, Mingze Chen; funding acquisition, Shawei Zhang.All authors have read and agreed to the published version of the manuscript.Funding: Guangzhou Philosophy and Social Science Planning 2022 Annual Project (No.: 2022GZYB67).Data Availability Statement: The original contributions presented in the study are included in thearticle; further inquiries can be directed to the corresponding author.Conflicts of Interest: The authors declare no conflicts of interest.ISPRS Int. 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Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
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urban green spaces
urban amenities
remote sensing
spatial analysis
Assessing the Association Between Urban Amenities and Urban Green Space Transformation in Guangzhou
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