Morphological Transformation of Urban Open Spaces: History
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Subjects: Urban Studies

Ongoing urbanization has led to the continuous expansion of built-up areas; as a result, open space is under great threat. Despite the wealth of studies conducted on open spaces, there is still a further need to further investigate the morphology of open space, particularly in an effort to understand the trends and drivers of open space morphological transformation that remain under-researched.

  • urban open space
  • morphology
  • trends
  • methods

1. Introduction

Open space is a widely studied topic; as such, it is no surprise that a number of academics have put forward various definitions and classifications in relation to this subject. Tang and Wong adopt a broad definition that encompasses parks and recreational spaces, outdoor public gathering places, unroofed and undeveloped natural landscapes on urban land, adjacent spaces between villages and buildings and urban spaces, and areas open to the public and commerce (including cafes, retail markets, theme parks, sports, streets, and sidewalks) [1]. It should also be noted that open space is a form of space that continually changes in line with urban development. Mark Francis claims that in addition to the traditional public spaces of city parks, squares, neighborhood parks, and playgrounds, the significance of the value of public space gave rise to community open spaces and gardens near to nature, linear parks, waterfronts, and greenways accessible to pedestrians, so as to provide a diverse and resourceful environment for learning, manipulation, and enjoyment [2]. It should also be noted that, in urban studies research, open spaces and green spaces interact to a considerable degree.
As an aspect of urban planning, green spaces are an increasingly important factor influencing urban residents’ quality of life. Due to its significant environmental, social, and economic value, urban green space (UGS) is widely regarded as one of the most important indicators of sustainable development. Specifically, in addition to changing the physical dimensions of the city, enhancing its aesthetics, and boosting property values, UGS also provides space for recreational activities, promotes health and social well-being, fosters relations with nature, and creates beautiful landscapes [3]. Moreover, the health benefits derived from UGS are important for improving urban environments [4]. In the past two years, the perception and use of UGS have assumed a renewed importance against the backdrop of the COVID-19 pandemic, largely due to its capacity to improve mental health and physical activity levels [5]. On this basis, the importance of urban nature and open spaces as a means to prepare for pandemics cannot be understated. Although green spaces offer a multitude of benefits for urban residents, increasing UGS may trigger gentrification in neighboring areas, driving up housing prices and encouraging the development of more high-end facilities, which steadily pushes out existing residents so that more affluent individuals can move in [6].
Today, the majority of the global population live in urban areas. In 2045, there will be 6 billion urban residents, 1.5 times the 2020 urban population [7]. This shift will lead to an inevitable extension and/or increase in the density of urbanized areas and the transformation of green spaces, thereby opening up urban open spaces to dynamic change. In cities, the growth of residential and commercial areas and infrastructure has precipitated and expedited the decline of green spaces, as can be seen in the cases of Kuala Lumpur [8] and Hong Kong [9]. Elsewhere, other studies have observed that UGS are rapidly disappearing in cities located in Asian countries, such as Karachi, Pakistan [10] and Hanoi, Vietnam [11]. Anguluri and Narayanan claim that urbanization that does not also create green spaces has exerted many negative physical and social impacts on city residents. As such, there is value in studying the factors that influence the changes in urban open space [12].
However, the best way to research the formation and transformation process of open space in terms of size, shape, and function is based on urban morphology. This is because urban morphology relates to temporal conditions, and urban forms are affected by historical/periodical changes [13]. For this reason, urban morphology researchers attach importance to retrospective studies in order to understand the current situation and present conditions. In this way, the physical structure of the city can be analyzed and the evolution of the city explained by referring to both historical and contemporary research. The “morphology” of urban green space can be understood by referring to the basic levels of “form”, “distribution”, and “pattern”, as well as the correlations between the three. More importantly, there is insufficient literature available to study and understand the factors pertaining to urban green space morphology as integral research, and a focus on the interactions of individual characteristics and how they influence urban open space evolution.

2. Drivers Influencing Urban Open Space Morphology

The drivers or factors that are described here are variables or determinants of open space morphology as a whole. Previous research on this topic has tended to explore the impacts of factors, including the policy, management, intrapersonal, and structural factors affecting urban green space visitation [19]; environmental factors influencing adults’ participation in physical activity [20]; and physical and non-physical factors influencing perceived green space accessibility [21]. Notably, these are the outcomes relating to the influence of diverse factors. Additionally, although a variety of factors have been said to affect urban open space morphology, few studies have examined these factors and outcomes in great detail. This is a significant knowledge gap, as these factors influence the changes that occur in urban open spaces.
More specifically, based on the wide-ranging definitions of open space, it is important to understand changes in open spaces in terms of quantity and space. However, morphological theory reveals the historical process of development in the city form and its spatial consequences. The study of the drivers influencing urban green spaces from a morphological perspective has both theoretical and practical significance for the comprehensive understanding of city spaces, along with its application in urban planning and construction. Moreover, a comprehensive perspective helps to identify what is already known, the gaps in the existing knowledge, and directions for further research. Thus, useful guidelines can be provided for urban planners and landscape architects.
Based on the literature (Table 1), several factors were identified in the relevant research on urban open spaces based on morphology theory, including natural geographical factors, government policy factors and socioeconomic factors. (1) Natural geographical factors: the location and topographic gradients of elevation and slope; (2) Government policy factors: the policy of green spaces and urban development; (3) Socioeconomic factors: population increases and economic growth. The research framework was established by authors for exploring the relationship between the three factors, urban open space, and the morphology of public open space changes outcomes.
Table 1. Previous studies from 2000–2022.
Year Types of Settings Authors Scale Research Concerns and Objectives Identified Drivers of Space Morphology Methods (Data Collection and Data Analysis)
2022 Urban green spaces [22] City Investigating the distribution patterns and drivers of UGS. Wealth and land use Using a combination of remote sensing data and fieldwork.
Calculating the proportion of UGS in different urban functional units.
2022 Green spaces [23] District Exploring the local spatial evolution and analyzing the influence factors of its transformation. Social and economic development. The remote sensing image data was used as the basic data.
Extracting the green space area conversion
analysis information.
2022 Green spaces [24] City Investigating the changing pattern of green spaces and how the topographic gradients of elevation and slope influence changes. Topographic gradients of elevation and slope. Applied for land use/land cover classification using GIS.
Using overlay analysis.
2022 Parks and green spaces [25] City Analyzing the correlation between urban development and parks and green spaces policy. Green spaces policy. Literature review, including theses, academic journal papers, research reports, and newspaper materials.
2022 Green spaces [26] City Analyzing the green spaces of Dhaka over a 30-year period using GIS and remote sensing. High population density and accelerated infrastructure development. Using GIS and Remote Sensing to collect images.
Using normalized difference vegetation index (NDVI) to calculate the total changes.
2022 Urban green spaces [27] Downtown Comparing the changes of greening policies for UGS evolution in the two cities. Several urban greening policies. Using GIS and Remote Sensing to collect images. Statistical Yearbooks and document planning.
Using the area change index, spatial morphological dimension, and spatial aggregation dimension.
2021 Urban green spaces [28] City Exploring the spatial-temporal dynamics of UGS and its influences on urban eco-environments in developing cities. Rapid urbanization and population growth. Landsat images and MODIS products; maps; statistical data.
Landscape pattern analysis.
2021 Green spaces [29] Not available Identifying the relevant issues to address the challenges facing China’s Green spaces planning system. Policy regulations. Literature review and policy analysis.
2021 Green spaces [30] City Evaluating the impact of changes on the structure of green spaces, and exploring the impact of different types of urban expansion and planning policies on changes to green space structure. Rapid urban expansion. Land use land cover (LULC) maps of the cities were developed based on satellite images.
Landscape metrics and statistical analysis.
2021 Urban green spaces [31] City Assessing the magnitude, directions of urban expansion and UGS change, as well as spatial variations. The spatio-temporal pattern of urban expansion. RS and GIS and Landscape Expansion Index (LEI) were used to extract Land Use Land Cover (LULC) data.
Measuring urban expansion and UGS change and analyze urban growth patterns.
2021 Green spaces [32] Regional Revealing the spatial-temporal change and driving factors of green spaces. Anthropogenic activities and geographical
environmental factors.
Remote sensing imageries.
Landscape pattern index.
2021 Urban green spaces [33] Cities Exploring the effect of different levels of urbanization on changes in green spaces. Economic development. Using a time-series of remote sensing data.
Indexes analysis.
2021 Urban green spaces [34] City Employing integrated approaches to characterize the changing patterns and intensities of green spaces. Rapid urbanization and greening policies. Landsat images to interprete land use datasets.
Landscape metrics.
2020 Urban green spaces [35] City Assessing the present status of green cover and evaluating the spatio-temporal changes in the land use/land cover composition. Rapid and unplanned urbanization. Field visits, the Office of the Asansol Municipal Corporation.
Calculation of NDVI.
2020 Urban green spaces [36] District Analyzing the dynamic changes in landscape patterns, quantitatively evaluating the eco-service value of urban green spaces, and discussing how they mutually influence each other. The rapid development of urban-rural integration and human factors. Remote sensing image.
Landscape pattern index.
2020 Urban green spaces [37] City How political circumstances of municipal governance and the pursuit of development can precipitate losses. The political circumstances of an urban area. Using a time series of satellite images.
Using the area calculation function in ArcMap 10.5.
2020 Green spaces [38] City Analyzing and assessing the changing scale and spatial layout of the urban green spaces. The expansion of urban and built-up areas, and the influx of migrants. Using the landsat thematic mapper (TM) and OLI/TIRS remote sensing image data.
Assessment using various indices.
2019 Urban green spaces [39] Town Analyzing urban green space changes and their drivers. Physical expansion of the built-up area, population growth, high land value, and laxity in the enforcement of planning regulations. Using series Landsat images, land inventory, interview, focus group discussion, and field observation for data collection, and a combination of techniques, including pixel based image classification, qualitative descriptive and GIS-based processing for data analyses.
2019 Green spaces [40] City An analysis of fragmented green spaces has been conducted. Urbanization. High-resolution
satellite images.
Using ENVI software computing.
2019 Urban green spaces [41] City Analyzing temporal and spatial changes in urban green spaces and exploring the driving forces underlying the observed changes. Different districts’ geographical locations. The Earth System Science Data Sharing Platform. Remote sensing images.
Calculating landscape indices.
2019 Urban open-green spaces [42] City Investigating the changes that have occurred in urban open-green spaces in Nevsehir. Urbanization. Analyses consist of satellite image classification, plant index production, and GIS-based analyses methods.
2019 Green spaces [43] City Understanding the factors that determine an increase or decrease of urban green spaces in a post-socialist city. Different regimes. Historical maps and aerial images.
Temporal analysis, proximity analysis.
2019 Urban green spaces [44] City Focusing on urban GS at a neighborhood scale to analyze GS in more granular detail. Compact urbanization. Urban GS was extracted using the normalized difference vegetation index based on GF-1 remote sensing images. Overlay.
2019 Urban green spaces [45] City center Developing an understanding of how urbanization influences the fragmentation of urban green spaces, and offers insights into the planning of urban green spaces from the perspective of promoting sustainability. Rapid urbanization and planning policies. Landsat images.
Landscape metrics.
2018 Urban green spaces [46] Cities Determining the appropriate proportion of public greenery to built-up areas in cities. Urbanization. The Local Data Bank.
Surveys.
2018 Green spaces [47] Regional Investigating green space types of the Beijing–Tianjin–Hebei region based on the elevation data and land use/cover for those years. Urbanization and greening policies. Landsat images.
Using ENVI software computing.
2018 Urban public spaces [48] City Identifying the major environmental challenges associated with the continued destruction of public urban space. Rapid population increase. Literature review.
2017 Urban green spaces [49] Regional Developing a systematic approach to monitoring changes in the urban landscape and assessing the conditions of UGS in the Klang Valley. Urbanization. Remote sensing processing techniques were used to extract meaningful data from mid-resolution Landsat satellite images.
Analyse using landscape metrics.
2017 Urban green spaces [50] City Studying the distribution of various types of urban green space in Shanghai. Rapid urbanization. High satellite image data.
Landscape pattern index and gradient analysis.
2017 Public open spaces [51] City Discerning the influence of factors on open space planning and development in Hong Kong. Government planning and development strategies. Government’s latest planning and development strategies.
2017 Urban green spaces [52] Cities Identifying general patterns relating to the quantity and structure of urban green space, and the demographic and economic characteristics of the cities in the study. Population density and economic level. Using remote sensing analysis of Landsat 7 data.
Calculating landscape indices.
2016 Green spaces [53] City Exploring the change of green space in Suzhou and revealing the spatial characteristics, ecological benefits, and its impact mechanism. Different districts’ geographical locations. Landsat remote-sensing image data.
Analyse using landscape metrics.
2015 Green spaces [54] City Assessment of changes in green spaces of Nanjing in terms of scale and structure. Population density. Landsat Satellite Data.
Analyse using landscape metrics.
2015 Urban green-spaces [55] Cities Identifying problems, challenges and strategies of urban green space planning during the densification processes. Urban densification. Literature review.
2013 Urban green spaces [56] City Investigating land use/land cover changes in Dehradun city and associated changes in urban green cover between 2004 and 2009. Urbanization. Remote Sensing to obtain detailed.
Using Image Derived Parameters.
2013 Green spaces [57] Cities Investigating the temporal trend in green space coverage and its relationship with urbanization. Urbanization. The Statistics Yearbook,
green space coverage of cities were calculated through least square linear regressions.
2013 Urban green spaces [58] City Analyzing the environmental quality based on green spaces to provide appropriate recommendations to elevate the environmental quality to international standards. Population density. Green space areas were extracted from Thailand Earth Observation System (THEOS) satellite
imagery using Normalized Difference Vegetation Index (NDVI). Extracted green space areas were further analysed quantitatively with air quality indicators and population density utilizing deductive indexing method.
2011 Green spaces [59] City To develop a comprehensive plan of green spaces development both at the municipal and regional levels. Geography. Using GIS and FRAGSTATS 3.3.
Overlaying the two green space distribution
maps and calculating the changing area, and the variation values of each green space type were obtained from the data of the land use change survey.
2011 Green spaces [60] City Using landscape metrics to assess green spaces fragmentation. Different districts’ geographical locations. The original orthophoto maps and land use digital maps with 0.5 m resolution used in this study were purchased from the Hong Kong government. Green spaces and different land uses were
extracted from the maps and transferred to raster maps, assisted by “3S” techniques.
Calculating different landscape metrics.
2011 Urban green spaces [61] City Using landscape pattern metrics to characterize shifting green space patterns. Rapid urbanization and greening policies. Remote-sensing image data.
Landscape metrics analysis.
2009 Urban green spaces [62] City To detect changes in the extent and pattern of green areas of Mashad and analyze the results of landscape ecology principles and functioning of the green spaces. Open lands for housing development. Combination of remote sensing image classification, landscape metrics assessment and vegetation indices.
2008 Open spaces [1] City Evaluating the land-use zoning and development of open spaces. Different districts’ geographical locations. The land-use planning and statutory zoning for open space.
2007 Urban green spaces [11] Downtown Identifying green space changes and their drivers. Economic growth, population increases, urbanization, and weaknesses in the planning and management of urban development. Graph theory, landscape metrics, GIS and FRAGSTATS 3.3.
2007 Greenbelt [63] City Documenting the spatial and temporal changes of greenbelts over the past decade by analyzing satellite images. Urban containment policy. Remote Sensing, analysis of archived documents.
2006 Urban green spaces [64] City Presenting a new method for quantifying and capturing changes in green space patterns. Government policy. GIS and remote sensing.
Landscape metrics.
2006 Urban open spaces [65] City Exploring the revitalization of existing traditional open spaces. City Planning Act. Case study.
2003 Green spaces [66] City Examining the issues, obstacles, and processes involved in remediating potentially contaminated urban brownfield sites. Urban planning policy. Case studies and personal interviews.

This entry is adapted from the peer-reviewed paper 10.3390/su141710856

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