2025 年 13 巻 3 号 p. 138-159
Rapid urbanization challenges the provision and management of Urban Green Spaces (UGS), essential to both urban ecological health and human well-being. The recent decline in UGS availability and accessibility calls for nuanced evaluation tools. In response, this research introduces the Integrated Neighbourhood Green Index (INGI) - a multifaceted index assessing urban green spaces, considering their distribution, accessibility, environmental conditions, and population density. The application of INGI in the context of Bhopal, India, reveals a city-wide score of 0.627, underscoring the rich but unevenly distributed urban green infrastructure. Careful data management strategies mitigated the impact of ward area outliers, preserving the robustness of our results. Bhopal's urban environment, with 1180.541 Ha of open green space and an average Land Surface Temperature of 32.8167°C, illustrates the complexities of managing UGS in rapidly growing cities. The INGI, as demonstrated in this study, serves as a vital tool for shaping sustainable urban development policies and strategies. Its utility transcends the geographical boundaries of this study, offering potential applications in diverse urban contexts worldwide.
The escalating pace of urbanization, projected to house 68% of the global population by 2050, poses myriad environmental and societal challenges, including heightened pollution, aggravated climate change, intensified urban heat island effects, and burgeoning public health issues. Amidst these multifaceted urban quandaries, urban green spaces (UGS) - comprising parks, gardens, playgrounds, forests, and other natural or semi-natural vegetated areas within urban environments - have attracted increasing scholarly and policy attention for their wide-ranging benefits (Astell-Burt, Hartig et al., 2022; Rahimi-Ardabili, Astell-Burt et al., 2021; Song, Ikei et al., 2016; Zhao, Jiang et al., 2024).
Urban green spaces are instrumental in serving critical ecological functions. They play a key role in air purification (Dadvand, Nieuwenhuijsen et al., 2015), local climate regulation (Houlden, Jani et al., 2021), surface runoff reduction (Zhang, B., Xie et al., 2015), and biodiversity conservation (Lai, Canavan et al., 2019). Notably, these spaces act as substantial carbon sinks, contributing significantly to the global fight against climate change (Nieuwenhuijsen, 2020),. In addition to ecological benefits, UGS also impart substantial health benefits to urban inhabitants. The presence of UGS promotes physical activity (An, Shen et al., 2019; De la Fuente, Pachón-Basallo et al., 2021; Kärmeniemi, Lankila et al., 2018), thereby helping to reduce the risk of a plethora of chronic conditions, including depression, osteoporosis, cardiovascular diseases, and fall-related injuries (Ezejimofor, Chen et al., 2016; Hamanaka and Mutlu, 2018; Moore, Lee et al., 2016). They also foster improved mental health and general well-being, and provide cultural, educational, and aesthetic value to the community (Burls, 2007; Jennings and Bamkole, 2019).
Nevertheless, despite the recognition of these multiple benefits, the allocation and quality of UGS often vary substantially across different urban landscapes. The unequal access to green spaces frequently reflects and intensifies other types of societal imbalances, giving rise to worries about environmental fairness (Pindo, Nurul et al., 2025; Wolch, Byrne et al., 2014). It is crucial to distribute UGS fairly to ensure that their quality and usefulness are not affected by varying environmental conditions, such as air quality and temperature, which can differ greatly between different urban areas. (Baumgart and Rüdiger, 2018; Choi, Berry et al., 2021; Wang, Bakker et al., 2014).
Thanks to advanced digital technologies like urban informatics and remote sensing, we now have greater opportunities to conduct precise and detailed spatial analysis and measurement of urban green spaces (Cugurullo, 2020; Yang, Zhao et al., 2023). The use of Geographic Information Systems (GIS) and satellite imagery has become essential in evaluating the availability, accessibility, and environmental state of UGS (Kato-Huerta and Geneletti, 2022; Stessens, Canters et al., 2020). It is becoming increasingly clear that current methods and metrics do not adequately encompass all aspects of UGS. Therefore, a more comprehensive approach is necessary to accurately measure UGS (Lin, Meyers et al., 2015; Liu, Kwan et al., 2021).
This study proposes the Integrated Neighbourhood Green Index (INGI) in response to this need. The INGI go beyond the assessment of green cover by incorporating four key elements: the distribution of green spaces, their accessibility, the per capita availability of green spaces in the surrounding areas, and important environmental conditions such as temperature. This multivariate approach is intended to provide a more complete and nuanced understanding of the urban green space landscape, thereby facilitating more informed urban planning and policy decisions.
The development of the INGI is consistent with the paradigm shift toward green urbanization, which promotes the incorporation and integration of nature into urban environments as a key strategy for promoting urban sustainability (Beatley, 2012; Newman, Hargroves et al., 2012). The INGI is also consistent with the goals of urban resilience because it provides a potent instrument for monitoring progress toward these goals and guiding interventions targeted at enhancing urban livability and environmental health (Buck, Tkaczick et al., 2015).
In this study, we apply the INGI to the context of Indian cities. The aim of this research is to develop an index that quantitatively measures the distribution and accessibility of green spaces, population density, and environmental conditions such as temperature and air quality across various geographical locations. The findings from this study will guide and help to inform future city planning and policy guideline, not only in India but in other rapidly urbanizing regions across worldwide (Giles-Corti, Vernez-Moudon et al., 2016).
In conclusion, the advent of urban informatics and the availability of high-resolution remote sensing data offer unprecedented opportunities for understanding and assessing urban green spaces (Pouya and Aghlmand, 2022).The INGI is a major advancement in this area. It combines various elements of urban green spaces into a comprehensive measure, making it a valuable tool for urban planners, policymakers, and researchers dedicated to creating fair, sustainable, and resilient urban areas.
In recent years, there has been a lot of research on the benefits of green spaces for both urban residents and the environment (Aram, García et al., 2019; Gascon, Triguero-Mas et al., 2015; Rupprecht and Byrne, 2014). Although there is plenty of evidence supporting these benefits, it can be difficult to measure and assess their impact due to the diverse nature and effects of these spaces in different urban contexts. Existing literature provides diverse methodologies to measure and assess UGS. One of the conventional methods is the calculation of the percentage of green cover (Rahimi-Ardabili, Astell-Burt et al., 2021; Zhang, L., Tan et al., 2022). This measure is typically derived from remote sensing data and can provide a straightforward assessment of the quantity of green space within a city (Shahtahmassebi, Li et al., 2021). However, this method does not consider other essential aspects, such as the accessibility and quality of these green spaces, which are crucial for realizing their potential benefits (Aydın and Sıramkaya, 2014).
Another commonly used method in UGS research is the park accessibility measure, which determines the proportion of the population living within a specified distance of green space (Perry, Devan et al., 2018).Cetin's research (Cetin, 2015) implemented GIS analysis to assess the accessibility of green spaces in Kutahya, Turkey, highlighting the critical role of accessibility in UGS studies. Although accessibility is an essential aspect of UGS, relying solely on this measure can oversimplify the complex relationship between people and green spaces (Ladle, Galpern et al., 2018).
The Urban Neighbourhood Green Index (UNGI), proposed by Gupta and other (Gupta, Kumar et al., 2012), is a more comprehensive measure of green spaces. The UNGI incorporates multiple factors, including the area, type, proximity, and usability of green spaces, providing a more nuanced understanding of neighbourhoods in urban areas. Despite its comprehensiveness, the UNGI remains challenging to apply across different urban contexts due to data availability and scalability issues (Mears and Brindley, 2019).
Green urbanism, a concept that promotes the integration of nature within urban settings, forms a central principle in sustainable urban planning and development (Subadyo, Tutuko et al., 2019). Despite the increasing recognition of this concept, many cities still struggle to achieve it due to various challenges, including rapid urbanization, resource limitations, and socio-economic disparities. The concept of green urbanism underscores the importance of creating comprehensive measures like INGI, enabling a better understanding of neighbourhood green and their impacts (Kim, Rupprecht et al., 2020).
The selection of INGI components is based on their ability to holistically measure urban greenery. Each factor addresses a critical dimension of urban sustainability. Green Space Distribution (DSG): The presence of green spaces throughout an urban area is fundamental to ensuring equal access to the benefits of greenery. Unequal distribution of urban greenery can contribute to environmental and social disparities (Rigolon, Browning et al., 2021). Accessibility to Green Spaces (AGS): Research underscores the importance of having easily accessible green spaces to encourage physical activity and reduce heat-related morbidity (Cetin, 2015). Green Space Availability Per Capita (GSA): The amount of green space available to residents plays a significant role in their quality of life. Studies indicate that a higher per capita green space correlates with lower stress levels, improved mental health, and reduced heat-related mortality, particularly among vulnerable populations (Kato-Huerta and Geneletti, 2022). The presence of water bodies within green spaces further enhances cooling effects (Kingsley, Eliot et al., 2016). Land Surface Temperature (LST): Green spaces play a crucial role in mitigating the urban heat island effect by reducing land surface temperatures. Research has found that urban greenery, including parks and green roofs, can significantly cool urban environments, thereby reducing heat-related health risks. Studies have demonstrated that urban parks and street trees can reduce surrounding air temperature by up to 1°C, providing critical relief during heat waves (Völker, Baumeister et al., 2013).
One of the primary goals of urban sustainability is to create and maintain green spaces that are accessible and beneficial to all residents, contributing to improved health, well-being, and quality of life (Sharma, Saini et al., 2023; Verma, M. C. and Tiwari, 2021). The literature underscores that the lack of comprehensive indices that consider different dimensions of UGS hampers urban sustainability . According to Albro (2019), achieving sustainable cities requires a thorough understanding of UGS in terms of their distribution, accessibility, and quality, aspects that most existing indices fail to capture.
The development of an INGI thus represents a significant step forward in this direction. This index aims to provide a more holistic understanding of UGS, incorporating key elements like distribution, accessibility, population density, and environmental conditions.
This study uses an observational approach to assess the urban green spaces in Bhopal, India, through the INGI. The methodology utilizes various data sources to measure four crucial factors: the distribution of green spaces, their accessibility, availability per person, and environmental conditions. These aspects are calculated separately, standardized, and then combined to produce the INGI score. By considering Per Capita Open Green Space, the research acknowledges the significance of having accessible public green areas, offering a complete evaluation that can aid in future urban planning and policymaking
Study AreaThis study is centred around Bhopal, the capital of the Indian state of Madhya Pradesh. Bhopal is renowned for its stunning lakes and diverse flora and fauna. With a population of 1,798,218, the city has a population density of 6,290 people per square kilometre ( Directorate of Census Operations, 2014; Google Search, n.d.). The Bhopal Municipal Corporation, established in 1907, is responsible for governing the city, which covers a municipal area of 463 square kilometres divided into 85 wards, each considered a neighbourhood in this study. For the purposes of this study, open green spaces include parks and playgrounds, but vacant, barren, and agricultural land is excluded. Institutional green areas are also excluded as they are restricted for public use. Please refer Table 1 for detailed population statistic of Bhopal.
| Dara | Message |
|---|---|
| Demographic Profile | City Bhopal |
| Population | 17,98,218 |
| Municipal Area (sq. Km) | 463 |
| Governing Body | Bhopal Municipal Corporation (1907) |
| Population Density (person per sq. km) | 6290 |
| Literacy Rate (%) | 83.47 |
| Youth, 15 - 24 years (%) | 21.3 |
| Slum Population (%) | 26.68 |
| Working Age Group, 15-59 years (%) | 65.22 |
Source: Census of India, 2011 (Directorate of Census Operations , 2014)
Study DesignThe study employs an observational and cross-sectional design. The observational approach is chosen because the study does not manipulate any variables but rather records the existing distribution of urban green spaces. The cross-sectional design is justified because data is collected at a single point in time across multiple wards in Bhopal rather than tracking changes over time. This approach is particularly suited for urban green space studies, where spatial distribution and accessibility can be analyzed in a snapshot format (Weeks, Hill et al., 2013).
Furthermore, treating each ward as an individual unit of analysis allows for neighborhood-level insights, which provide a more granular understanding of variations in urban green spaces. This methodology aligns with prior research that assesses green space distribution at neighborhood scales to identify disparities in accessibility and environmental benefits (Baltagi and Hashem Pesaran, 2007).The current research employs an observational, cross-sectional design, with each ward of Bhopal treated as an individual neighbourhood for analysis (Weeks, Hill et al., 2013). This strategy is chosen to capture the heterogeneity in urban green space distribution and access within the city's diverse wards (Baltagi and Hashem Pesaran, 2007). By approaching each ward as a distinct neighbourhood; the research can capture the nuances of green space distribution within each area. This perspective provides a more accurate representation of the availability, access, and environmental impact of green spaces, as it allows for a granular analysis that accounts for the unique characteristics of each neighbourhood.
Additionally, the observational nature of the design allows for the collection of real-time, on-the-ground data. This method, as opposed to a hypothetical or experimental design, ensures that the findings reflect the actual state of green spaces within the city (Thiese, 2014).
In accordance with this design, data collection and analysis have been planned to focus on the four key components of the Integrated Neighbourhood Greenery Index (INGI): Distribution of Green Spaces (DGS), Accessibility to Open Green Spaces (AOGS), Green Space Availability (GSA), and Environmental Conditions based on Land Surface Temperature (LST). These components are derived for each ward independently and then combined to create the comprehensive INGI as show in Figure 1.
Data essential for the research is acquired from multiple sources. Satellite remote sensing data is employed to calculate the Normalized Difference Vegetation Index (NDVI) and provide the necessary information to assess greenery. Additionally, official city data is sourced to collect information on population demographics and green space areas for each neighbourhood, which are integral to determining the PCGS. The Accessibility to Green Spaces is evaluated through GIS data using network analysis method. Lastly, land surface temperature data derived from remote sensing is utilized to evaluate the environmental conditions, specifically temperature, within each neighbourhood.
Data AnalysisThe data analysis procedure encompasses four significant steps that correspond to the components of the INGI. Each of these components will be individually calculated using GIS and remote sensing data. Normalisation techniques will be applied to each component to ensure all values fall within a range of 0 to 1 (Lafortune, Fuller et al., 2018). Subsequently, assigned weights based on the importance of each component for the study will be used to integrate the individual components into a comprehensive INGI.
Data Preparation and TransformationIn data analysis, ensuring the accuracy and reliability of data is paramount, often necessitating the preparation and transformation of the collected information (Jin, Wang et al., 2017).
Outlier Detection: The first step is to identify potential outliers, which are values significantly differing from other observations and capable of heavily influencing statistical results (Seo, 2006). To detect these anomalies, the Interquartile Range (IQR) method is utilized. This statistical tool measures the dispersion of data and determines the range within which the majority of values lie, thus highlighting any unexpectedly extreme data points (Vinutha, Poornima et al., 2018; Wan, Wang et al., 2014).
After identifying outliers, it's important to decide how to handle them. Simply removing them could lead to loss of important information. Instead, a technique called winsorizing can be used (Lusk, Halperin et al., 2011). This involves replacing extreme values with certain percentile values, which limits their impact on the analysis while still preserving key data (Ghosh and Vogt, 2012).
Assigning WeightageThe weightage assignment in the Integrated Neighbourhood Green Index (INGI) was determined based on the relative significance of each factor in assessing urban greenery and its broader environmental and social impacts. The approach follows a structured framework that considers both empirical evidence and urban planning principles.
Distribution of Green Spaces (DGS) - 0.30: DGS holds the highest weight as the equitable spatial allocation of green areas is critical to maximizing urban sustainability. Areas with dense green distribution contribute to improved ecological balance, fostering biodiversity and minimizing localized temperature fluctuations (Sathyakumar, Ramsankaran et al., 2019). The weight reflects the foundational role of green space distribution in shaping urban microclimates and ensuring accessibility for all socio-economic groups. Accessibility to Open Green Spaces (AGS) - 0.25: While distribution is essential, the ease with which residents can reach these spaces significantly affects their utilization. Assigning AGS a high weight ensures that urban greenery is functionally accessible to residents, particularly in compact, high-density urban zones where green space is often limited. The weight accounts for factors such as walking distances, physical barriers, and pedestrian connectivity, which directly impact urban liveability. Green Space Availability per Capita (GSA) - 0.25: The allocation of this weight underscores the need for a balance between green space provision and population density. Higher per capita green space is associated with improved physical and mental well-being. This weight also accounts for urban expansion and the pressures of rapid urbanization, ensuring that green space allocation aligns with population growth dynamics. Land Surface Temperature (LST) - 0.20: While LST is a crucial environmental indicator, it has been assigned a slightly lower weight due to its indirect nature in assessing green space quality. Although green spaces mitigate urban heat islands, LST is influenced by multiple urban factors such as land cover, materials used in built environments, and seasonal variations. Thus, while LST remains an essential component in INGI, its relative influence is lower compared to direct accessibility and distribution measures.
The weightage allocation in INGI ensures a balanced and comprehensive evaluation of urban greenery, integrating spatial equity, accessibility, population needs, and environmental resilience. This approach provides an adaptable framework for policymakers to prioritize green space interventions effectively for urban planning and policymaking in Bhopal.
Distribution of Green Spaces (DGS)This component assesses the amount and spread of greenery in each neighbourhood. The DGS offers an insight into the general greenness of the neighbourhood and is important because having ample green spaces distributed evenly throughout an area can significantly contribute to the well-being of its residents (Rigolon, Browning et al., 2021). It has been shown to reduce stress, increase physical activity, and improve overall mental health. Moreover, green spaces can help mitigate some environmental challenges, like air pollution and the urban heat island effect. The Normalized Difference Vegetation Index (NDVI) component captures the degree of vegetation cover in each wards (Rhew, Vander Stoep et al., 2011). This data was derived from LANDSAT 8 satellite imagery, corresponding to June 2023, providing a representative snapshot of mid-year climatic conditions. To achieve a desirable granularity of data, the study region was delineated into a grid format, with each cell measuring 250m x 250m. From the centroid of these grid cells, the NDVI value was extracted and aggregated, and the median value was calculated for each ward using the Statistical Package for the Social Sciences (SPSS). Relative scores were then assigned to these categories on a scale of 0 to 1, with intervals of 0.2, thereby yielding scores of 0.2, 0.4, 0.6, 0.8, and 1 for each respective category (refer Table 2 no. 2).
| NDVI Range | DGS Score | Label |
|---|---|---|
| NDVI <= 0.128882 | 0.2 | Highly Built-Up Areas |
| 0.128882 < NDVI <= 0.324986 | 0.4 | Moderately Built-Up Areas |
| 0.324986 < NDVI <= 0.523961 | 0.6 | Low Green Coverage |
| 0.523961 < NDVI <= 0.722486 | 0.8 | Moderate Green Coverage |
| NDVI > 0.722486 | 1.0 | High Green Coverage |
To represent this in formulaic terms:
DSG i Score = assign_score (median NDVI i)…………(1)
Where assign_score (median NDVI i) is a function that assigns a score based on the median NDVI of ward i as per the defined NDVI categories and their corresponding scores. Table 2 outlines the Normalized Difference Vegetation Index (NDVI) ranges and their corresponding Distribution of Green Spaces (DGS) scores, which are used to assess the green coverage in Bhopal. NDVI is a key indicator of vegetation health and density, and it helps in categorizing the urban areas based on their greenness (Verma, M. and Kamat, 2022). This categorization ensures that the DGS scores accurately reflect the degree of vegetation cover in each ward, contributing to a holistic measurement of urban greenness as captured by the Integrated Neighbourhood Green Index (INGI).
Accessibility to Green Spaces (AGS)It's not enough to have ample green spaces; these spaces must be accessible to the residents. The AGS assesses the degree to which residents can easily reach green spaces (Cetin, 2015). This is crucial because accessible green spaces encourage outdoor activities and social interactions, enhancing community well-being and individual physical and mental health.
Each ward's percentage of accessible open green space within a 400-meter distance from centroid of open green space was initially computed using network analysis. This approach ensures a realistic measure of accessibility, considering actual walking paths and barriers, providing a comprehensive understanding of how green spaces serve urban populations. Utilizing the formula:
AGS i = (Accessible Open Green Space Area within 400m of ward i / Total Built-Up Area of ward i) * 100 …………(2)
However, the propensity for excessively high values distorting the data necessitated the introduction of a capping mechanism. A cap (Schoonjans, De Bacquer et al., 2011) was thus instituted, representing a high percentile (for example, the 95th or 99th percentile) of the dataset. All AGS Accessibility scores exceeding this cap were set equal to the cap, thereby moderating the effect of outliers:
Capped AGS i = min (AGS i, Cap) …………(3)
Subsequent to capping, the data was normalized to align with the 0-1 range, similar to other components of the INGI. This was accomplished by dividing each capped AGS Accessibility score by 100:
Normalized Capped AGS i would be AGS i Score = Capped AGS i / 100 …………(4)
This meticulous process of scoring the AGS component ensured a standardized format for each ward's AGS score. This representation of accessibility to green spaces not only reduced the distortion effect of extreme values but also provided a comprehensive and accurate measure contributing to the overall INGI.
Green Space Availability (GSA)Per Capita Green Space (PCGS): The PCGS component gauges the amount of green space per person in each neighbourhood. This is a critical measure as it accounts for population density. In densely populated areas, even large green spaces can become overcrowded, reducing their utility and accessibility. Thus, a high PCGS indicates a potentially higher quality of life, with ample green spaces available per person.
The Per capita open green space (PCOGS), on the other hand, focuses on the portion of green space that is publicly accessible. These open spaces are typically more active sites for recreation, social interaction, and contact with nature, and they play a crucial role in enhancing public health and well-being.
By incorporating these two in the INGI can effectively gauge not only the quantity of green space but also its accessibility and potential for use by residents, providing a more comprehensive understanding of green space distribution and its associated benefits in the urban landscape.
First, the data is normalized. For each region in the dataset, green space per capita and open green space per capita are calculated by dividing the total area of each type of green space by the respective population:
PCGS = Total Green Space (sqm) / Total Population ……(5)
PCOGS = Total Open Green Space (sqm) / Total Population ……(6)
Then, based on the World Health Organization's (WHO) recommendation of 10 square meters (sqm) per person, a continuous scoring system is developed. The scores for green space per capita and open green space per capita are calculated by dividing each value by the WHO recommendation and multiplying by 100:
PCGS Score = (PCGS / 10 sqm) * 100 …………(7)
PCOGS Score = (PCOGS Capita / 10 sqm) * 100 …………(8)
This scoring system is applied to each observation in the dataset to generate green space per capita and open green space per capita scores.
Normalized PCGS i Score = PCGS i Score / 100 (if PCGS i Score > 100, it is capped at 100) …………(9)
Normalized PCOGS i Score = PCOGS i Score / 100 (if PCOGS i Score > 100, it is capped at 100) …………(10)
Since the INGI operates on a scale of 0 to 1, the scores are then rescaled. If a score exceeds 100, it is capped at 100 for consistency. After capping, scores are divided by 100 to bring them within the 0 to 1 range.
The formula for Green Space Availability (GSA) would be:
GSA i score = (Norm_PCGS i Score in neighbourhood + Norm_PCOGS i Score in neighbourhood)/ 2 …………(11)
Land Surface Temperature (LST)Land Surface Temperature (LST): This component provides a measure of the local environmental conditions, particularly those affected by green spaces. Urban green spaces can significantly impact the urban microclimate by reducing temperatures, mitigating the urban heat island effect, and improving overall urban climate resilience. In areas with high temperatures, green spaces can provide essential cooling effects, making the urban environment more liveable and comfortable.
The LST data is sourced from satellite imagery provided by LANDSAT 8, corresponding to the month of June 2023, typically representative of mid-year climatic conditions. To achieve appropriate granularity of data, the geographic region under study was segmented into a grid format, each cell measuring 250m x 250m. The LST value was extracted from the centroid of each of these grid cells, thereby collating a comprehensive dataset of temperature values.
Owing to the inherent spatial variations in LST across any given ward, the decision was made to compute the median LST value (Med) for each ward. This median value was used as the representative or 'ideal' temperature for that ward, providing a baseline for comparisons across geographically diverse locations.
Normalization of the LST values was achieved using a specifically devised scoring methodology, which unfolded as follows:
The absolute deviation of each LST value (LST i) from the median LST of its respective ward (Med) was calculated. This was represented as Di, where Di = |LST i - Med|. …………(12)
The maximum deviation (M) across all wards was identified from the array of D i values.
The normalized score for each LST value was then computed using the formula LST i Score = 1 - (D i / M).…………(13)
The scoring scale was designed to range between 0 and 1. A score of 1 would imply that the LST value of a given ward precisely matches the 'ideal' temperature (the median), whereas a score of 0 would indicate the maximum possible deviation from this ideal.
This intricate methodology ensured an objective and equitable comparison of LST values across different wards. By transforming the raw LST data into a standardized format, the methodology successfully represented the deviation of each ward's temperature from optimal conditions. In turn, the LST component analysis served as an essential contribution to the comprehensive urban landscape view that the INGI was designed to encapsulate.
Calculation of INGI and BenchmarkThe classification breaks for benchmarking the INGI scores are determined based on statistical methods. The threshold values are derived from percentile distributions:
Excellent Adequacy (INGI > 0.8): Represents urban areas with optimal green space distribution and access, aligning with sustainable development goals.
High Adequacy (0.7 < INGI ≤ 0.8): Indicates well-performing areas but with room for minor improvements.
Moderate Adequacy (0.5 < INGI ≤ 0.7): Reflects neighborhoods with sufficient green spaces but gaps in distribution.
Low Adequacy (INGI ≤ 0.5): Denotes critical areas requiring intervention for improved green space access.
Please refer to Table 3 for detailed classification of benchmarking for INGI.
| INGI Score | Category | Description | Interpretation |
|---|---|---|---|
| INGI > 0.8 | Excellent Adequacy | Outstanding green space distribution and accessibility. | Benchmark for sustainable urban development. |
| 0.7 < INGI ≤ 0.8 | High Adequacy | High level of green space adequacy. Good coverage and accessibility with moderate to high environmental benefits. | These neighborhoods are performing well in terms of urban green space provision, though minor improvements may be needed. |
| 0.5 < INGI ≤ 0.7 | Moderate Adequacy | Sufficient green spaces with gaps in distribution or accessibility. | While these areas are generally satisfactory, targeted improvements in green space distribution and access can enhance urban livability and environmental health. |
| INGI ≤ 0.5 | Low Adequacy | Insufficient green space availability and accessibility. | Significant interventions are needed in these neighborhoods to improve green space provision and realize associated benefits. |
INGI i = w1DGS i score + w2AGS i score + w3GSA i score + w4T i score ……(14)
Where w1, w2, w3, and w4 are the weights assigned to each component.
INGI I city = (Σ (w1DGS i score + w2AGS i score + w3GSA i score + w4T i score)) / N …………(15)
Where:
INGI city is the city-level INGI,
DGS i score is the Distribution of Green Spaces score for ward i,
AGS i score is the Accessibility to Green Spaces score for ward i,
GSA i score is the Green Space Availability score for ward i,
T i score is the Land Surface Temperature score for ward i,
w1, w2, w3, and w4 are the weights given to each component,
i stand for each individual ward,
Σ represents the sum over all wards, and
N is the total number of wards in the city.
In our study INGI Bhopal = (Σ (w1DGS i score + w2AGS i score + w3GSA i score + w4T i score)) / N = (0.569 + 0.493 + 0.565 + 0.883)/85 = 0.627 City of Bhopal. …………(16)
In the course of the research, outliers were identified within the ward area data, which could potentially impact the accuracy of the Integrated Neighbourhood Greenness Index (INGI) calculations. These outliers, found in ten wards such as Bhanpur Ward, Mahatma Gandhi Ward, among others, had ward areas considerably larger than the majority. The Interquartile Range (IQR) method was utilized for outlier detection due to the continuous nature of the ward area values. A decision was made to set the upper limit for the ward area at the 95th percentile of the third quartile, amounting to 940.5681 sqm. To mitigate the undue influence of these outliers on the overall data set, each outlier was replaced with this upper limit value. This capping or Winsorizing technique effectively limited the impact of extreme values, ensuring a more accurate and representative INGI calculation.
Table 4 presents a detailed assessment of various factors that contribute to the urban greenness of Bhopal. This comprehensive evaluation includes metrics on the total open green space, per capita open green space (PCOGS), total green space area, and per capita green space (PCGS). Additionally, it covers the percentage of built-up areas with accessible green spaces within a 400-meter radius (AGS), and key land surface temperature (LST) statistics such as minimum, maximum, and average temperatures recorded in June.
| Data | Value |
|---|---|
| Total Open Green Space (Ha) | 1180.541 |
| PCOGS (sqm) | 6.136255 |
| Total Green Space Area (Ha) | 15794.6 |
| PCGS (Sqm) | 82.09767 |
| % of Built-up area AOGS (within 400m) | 46.2947 |
| Land Surface Temp (June) | |
| Min (oC) | 40.7833 |
| Max (oC) | 16.9943 |
| Average LST (oC) | 32.8167 |
| Average NDVI of Bhopal | 0.438743 |
The city of Bhopal's existing environmental conditions is characterized by a range of diverse attributes. The total open green space in the city extends to 1180.541147 Ha, providing a per capita open green space (PCOGS) of 6.136254653 sqm. Furthermore, the total green space area within the city measures 15794.59745 Ha, corresponding to a per capita green space (PCGS) of 82.09766544 sqm. Interestingly, about 46.3% of the city's built-up area comprises accessible green space within a 400-meter radius.
Land surface temperature (LST) data, derived from satellite imagery for the month of June, shows a minimum temperature of 16.9943°C, a maximum of 40.7833°C, and an average of 32.8167°C. The city's average Normalized Difference Vegetation Index (NDVI), a measure of vegetation health and density, stands at 0.438743.
The spatial analysis of urban greenness in Bhopal is illustrated through a series of maps that highlight various aspects of green space distribution and accessibility. Figure 2 depicts the Distribution of Green Spaces (DGS) across the city, categorized into five levels of greenness: Low Greenness, Moderate Greenness, Average Greenness, High Greenness, and Very High Greenness. The wards with darker shades of green represent areas with higher vegetation density, indicating better green coverage. This spatial distribution helps in understanding the overall spread and concentration of green spaces within the city.
Figure 3 presents the Accessibility to Open Green Spaces (AGS) in Bhopal, highlighting the areas within a 400-meter radius of green spaces. Accessible built-up areas are depicted with patterns, while built-up areas without accessible green spaces are marked in orange. This visual representation allows for a clear understanding of which parts of the city have sufficient access to green spaces and which areas may require further development to improve accessibility.
Figure 4. illustrates the Land Surface Temperature (LST) across Bhopal, with temperatures ranging from low (depicted in blue) to high (depicted in red). The LST data, derived from satellite imagery for June, offers insights into the microclimatic conditions of different wards. Areas with higher temperatures are more prone to urban heat island effects, highlighting the importance of green spaces in mitigating temperature extremes. Finally, Figure 5 showcases the Integrated Neighbourhood Green Index (INGI) scores for Bhopal, classified into five categories: Poor, Below Average, Average, Good, and Excellent. The wards are color-coded to reflect their respective INGI scores, providing a comprehensive view of urban greenness, including green space distribution, accessibility, and environmental conditions. This map is crucial for identifying areas that excel in urban greenness and those that need significant improvements.
| Ward No | Best Performing Ward | Score | Ward No | Poor Performing Ward | Score |
|---|---|---|---|---|---|
| Distribution of Green Spaces (DGS) | |||||
| 84 | Ratanpur Sadak Ward | 0.744 | 8 | Royal Market Ward | 0.102 |
| 85 | Katara Ward | 0.690 | 40 | Aishbagh Ward | 0.105 |
| 81 | Kanhakunj Ward | 0.654 | 20 | Mahavir Swami Ward | 0.105 |
| 79 | Navibag Ward | 0.653 | 22 | Moti Masjid Ward | 0.121 |
| 62 | Hatai Kheda Ward | 0.644 | 21 | Jain Mandir Ward | 0.123 |
| Accessibility to Open Green Space (AOGS) | |||||
| 63 | Gautam Buddha Ward | 1.000 | 52 | Misraud Ward | 0.001 |
| 68 | Ayodhya Nagar Ward | 1.000 | 73 | Bhopal Memorial Aspataal Ward | 0.012 |
| 57 | Saket Shakti Ward | 1.000 | 53 | Jaatkhedi Ward | 0.014 |
| 56 | Barkheda Pathani Ward | 1.000 | 18 | Ram Mandir Ward | 0.017 |
| 29 | Maulana Abdul Kalam Azad Ward | 1.000 | 12 | Nariyal Kheda Ward | 0.022 |
| Green Space Availability (GSA) | |||||
| 3 | Bhauri Ward | 1.000 | 21 | Jain Mandir Ward | 0.005 |
| 13 | Geetanjali Ward | 1.000 | 14 | Shahjahanabad Ward | 0.014 |
| 24 | Rani Kamlapati Ward | 1.000 | 20 | Mahavir Swami Ward | 0.017 |
| 26 | Dr.Ambedkar Ward | 1.000 | 71 | Dusshera Maidan Ashoka Garden Ward | 0.032 |
| 51 | Shahpura Ward | 1.000 | 40 | Aishbagh Ward | 0.041 |
| Land Surface Temperature (LST) | |||||
| 10 | Eidgaah Hills Ward | 1.000 | 8 | Royal Market Ward | 0.000 |
| 43 | Maharana Pratap Ward | 0.999 | 4 | Hemu Colony Ward | 0.098 |
| 67 | Indrapuri Ward | 0.999 | 5 | Sadhu Waswani Ward | 0.542 |
| 78 | Karond Ward | 0.996 | 84 | Ratanpur Sadak Ward | 0.655 |
| 23 | Islampura Ward | 0.993 | 81 | Kanhakunj Ward | 0.656 |
| INGI | |||||
| 59 | Barkheda Bhel Ward | 0.941 | 8 | Royal Market Ward | 0.326 |
| 56 | Barkheda Pathani Ward | 0.892 | 21 | Jain Mandir Ward | 0.352 |
| 57 | Saket Shakti Ward | 0.888 | 14 | Shahjahanabad Ward | 0.354 |
| 63 | Gautam Buddha Ward | 0.880 | 4 | Hemu Colony Ward | 0.417 |
| 58 | Kasturba Ward | 0.872 | 20 | Mahavir Swami Ward | 0.422 |
The performance of various wards in Bhopal based on different INGI components is summarized in Table 5. For the Distribution of Green Spaces (DGS), Ratanpur Sadak Ward achieved the highest score (0.744) while Royal Market Ward scored the lowest (0.102). For Accessibility to Open Green Spaces (AGS), Gautam Buddha Ward and several others achieved the highest possible score of 1.000, while Misraud Ward had the least accessibility with a score of 0.001. When considering Green Space Availability (GSA), five wards including Bhauri Ward and Geetanjali Ward achieved perfect scores of 1.000. Conversely, Jain Mandir Ward struggled in this category with a score of only 0.005. Regarding Land Surface Temperature (LST), the best-performing ward was Eidgaah Hills Ward with a perfect score of 1.000, while Royal Market Ward again scored the lowest with 0.000.
In the combined INGI, Barkheda Bhel Ward emerged as the best-performing ward with a score of 0.941, while Royal Market Ward was the least-performing with a score of 0.326. This evaluation of INGI components provides a comprehensive assessment of the distribution, accessibility, and availability of green spaces, as well as land surface temperatures across Bhopal's wards, presenting a nuanced understanding of urban greenness within the city.
In conclusion, our research contributes significantly to the body of knowledge on the evaluation and importance of Urban Green Spaces (UGS) in the context of rapid urbanization. We proposed and implemented the Integrated Neighbourhood Greenery Index (INGI), a novel, comprehensive tool for quantifying and assessing the availability, distribution, and environmental conditions of UGS. Through the application of INGI to the city of Bhopal, India, we provided a nuanced and multi-dimensional understanding of the urban green space landscape.
Our study revealed a city-wide INGI score of 0.627 for Bhopal, indicating the presence of substantial urban greenery, albeit with uneven distribution across different wards. We further detailed Bhopal's urban green landscape, which comprises 1180.541 Ha of open green space and an average Land Surface Temperature (LST) of 32.8167°C.
Moreover, our methodology exemplified an effective approach to handling outliers in ward area data, thereby ensuring the robustness and validity of our results. We also highlighted potential improvements for the INGI, including broadening the scope of indicators used, integrating population density and temporal components, and leveraging advanced statistical and machine learning techniques.
Policy and Decision-Making Applications of Benchmark Scores
The INGI benchmark scores provide a data-driven approach for urban planners, policymakers, and local governments to make informed decisions regarding green space allocation and urban development. The classification system helps in identifying priority areas for intervention, ensuring equitable distribution of green spaces and mitigating urban heat island effects. Urban Planning and Zoning Regulations: Areas with low adequacy (INGI ≤ 0.5) can be prioritized for green infrastructure investments, including the development of parks, tree planting programs, and improved accessibility to existing green spaces. Climate Resilience Strategies: Regions classified under moderate to high adequacy (0.5 < INGI ≤ 0.8) can serve as benchmarks for sustainable urban development, incorporating policies that maintain and expand green cover. Health and Well-being Initiatives: Public health departments can use INGI scores to promote community-based interventions, advocating for increased green spaces in vulnerable neighborhoods to reduce heat stress and improve overall mental well-being. Resource Allocation and Funding: The classification helps local governments allocate resources efficiently, ensuring that funds are directed toward areas where green space interventions will have the greatest impact on urban sustainability and social equity. Monitoring and Evaluation: The benchmark scores allow for long-term assessment of urban greenery policies, enabling city administrations to track improvements over time and refine strategies based on empirical data.
Overall, this study underscores the significance of a comprehensive approach to evaluating UGS, illustrating the INGI's potential role in informing sustainable urban development strategies, policies, and interventions. Ultimately, our research underscores the need for urban green spaces as a crucial component for enhancing urban resilience, mitigating environmental impacts, and improving the health and well-being of urban inhabitants.
Several recommendations can be made for the enhancement of the Integrated Neighbourhood Green Index (INGI). First, broadening the scope of indicators used in the index calculation would offer a more comprehensive view of the green status of urban areas. Variables such as biodiversity, human interaction with green spaces, and the presence of water bodies could be incorporated. Second, the model would benefit from recognizing the impact of the human population on green spaces by integrating population density into its calculations. Third, incorporating a temporal component into the INGI could reflect the dynamism of urban green spaces, providing insights into trends over time. Fourth, using advanced statistical and machine learning techniques could boost the accuracy and reliability of the index. Fifth, calibrating the INGI based on local conditions would enhance its relevancy, helping in shaping local policies and urban planning strategies. Lastly, engaging with stakeholders like local communities, urban planners, and policymakers during the data collection and analysis stages could help ground the index in reality and enhance its effectiveness. Thus, the INGI could become a more powerful tool for creating sustainable urban green spaces.
Conceptualization, M.C.V.; methodology, M.C.V.; software, M.C.V; investigation, R.K.; resources, M.C.V.; data curation, M.C.V.; writing—original draft preparation, M.C.V. and R.K.; writing—review and editing, M.C.V. and R.K.; supervision, R.K. All authors have read and agreed to the published version of the manuscript.
The authors declare that they have no conflicts of interest regarding the publication of the paper.
I would like to extend my deepest appreciation to my supervisor, Dr. Rajshree Kamat, for her invaluable guidance, patience, and support throughout this research. Her insight and wisdom have been instrumental in the completion of this work.
I am grateful to my colleague, Vikram Kumar Chourasia, for his significant contribution, collaboration, and constant encouragement.
Lastly, heartfelt thanks to my family for their unwavering support, love, and understanding throughout this journey. Their faith in my abilities has been a constant source of strength.
