Site suitability analysis of urban green parks in Ranchi city using GIS–AHP based multi-criteria decision analysis

Abstract

Purpose

The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand.

Design/methodology/approach

It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites.

Findings

The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect.

Originality/value

This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Citation

Joy, M.S., Jha, P., Yadav, P.K., Bansal, T., Rawat, P. and Begam, S. (2024), "Site suitability analysis of urban green parks in Ranchi city using GIS–AHP based multi-criteria decision analysis", Urbanization, Sustainability and Society, Vol. 1 No. 1, pp. 169-198. https://doi.org/10.1108/USS-10-2023-0008

1. Introduction

In the era of ongoing global urbanization and rapid economic expansion, catering to the needs of growing city populations poses a significant threat to ecological balance (Wu et al., 2014; Zhang et al., 2022). Most of the world’s urban population, approximately four-fifths (6.1 billion) of the world’s urban population in 2016 (7.5 billion), lived in developing nations (UNCTAD Handbook of Statistics, 2017), with only a small percentage of their land covered with green spaces. Several studies have focused on Far Eastern nations such as China (Li et al., 2022; Wang et al., 2023), Hong Kong, Korea and Japan, but there is limited research on urban green land expansion potential in emerging nations, particularly in Africa and Asia (Ustaoglu and Aydinoglu, 2020). Urban green parks (UGPs) provide a range of ecosystem services (Capotorti et al., 2016), a demand further accentuated during the COVID-19 pandemic (Sung et al., 2022). The solution is to create new UGPs to bridge the gap between what is available and what is needed. It is essential to use a robust framework for evaluating prospective park locations using methodologies that facilitate the identification of optimal areas for establishing these UGPs (Wang et al., 2021). When coupled with multicriteria decision analysis [also known as multi-criteria decision analysis (MCDA)], suitability analysis is a potent instrument for assisting with land-siting decisions (Falah et al., 2019; Halder et al., 2022; Seyedmohammadi and Navidi, 2022).

UGPs are indispensable assets that play a pivotal role in improving urban air quality, mitigating noise pollution, promoting sustainable urban development, stimulating local economies (Zhang et al., 2015; Li et al., 2022) and countering the urban heat island (UHI) effect (Kong et al., 2014; Gunawardena et al., 2017; Yu et al., 2020). It serves as a carbon sink, absorbing and storing carbon dioxide, reducing greenhouse gas levels and mitigating climate change effects. Heavy metals improve air quality by filtering pollutants and releasing oxygen, which is crucial for urban residents’ health (Lu et al., 2021). Natural features in parks, such as wetlands and bioswales, manage stormwater runoff, reducing flooding risks during heavy rainfall events (Feldman et al., 2019). UGP utilization is a pivotal strategy for curbing urban sprawl and mitigating the detrimental environmental consequences of urbanization (Jabareen, 2006). The normalized difference vegetation index (NDVI), UHI effect and particulate matter content (2.5) are all examples of these kinds of vital environmental parameters. The integration of these considerations ensures that the establishment of UGPs continues without jeopardizing the integrity of the natural environment, thereby increasing the long-term viability of the urban setting.

UGPs are vital habitats for diverse urban wildlife (Karuppannan et al., 2014). These parks provide critical resources for breeding, resting and raising young people (Threlfall et al., 2017). Additionally, they serve as transitional habitats, connecting fragmented natural areas within cities, supporting migratory species, offering safe refugia from urban disturbances and reducing predation risks for smaller wildlife (Aronson et al., 2017; Gavareski, 1976). Their presence also provides educational opportunities and fosters community engagement in local biodiversity conservation efforts (Lange-Kabitz et al., 2021). These parks are crucial for preserving urban wildlife and maintaining ecological balance within cities. The loss of habitats, invasion of nonnative species, effects of climate change and degradation of ecosystems pose serious threats to urban biodiversity, according to Aronson et al. (2014) and Grimm et al. (2008). Threlfall et al. (2017) predicted that the extent of urban land cover will triple by the year 2030, resulting in the substantial depletion of habitats, especially in places rich in biodiversity.

UGS implementation is influenced by socioeconomic factors such as employment rates, education and income levels, which can impact the distribution of benefits, particularly in economically disadvantaged neighborhoods. UGP planning can enhance the inclusivity and equity of green spaces by considering employment rates. Studies show that UGPs offer social, cultural and equitable benefits, such as reduced stress, improved mood and enhanced mental well-being. Parks provide physical activities, gathering places and support for residents of all ages, backgrounds and disabilities (Ma et al., 2019; Rakhshandehroo et al., 2015). They foster a sense of belonging and social cohesion, reducing feelings of social isolation and loneliness (Kazmierczak and James, 2007). Well-planned green parks ensure equal access to nature and its benefits for all community members, regardless of age, ability or socioeconomic status (Jennings et al., 2016).

UGPs enhance urban mobility by integrating public transit, cycling lanes and walkability initiatives; this paper suggests developing UGPs near bus stops and roads (Li et al., 2022). They serve as hubs connecting various transportation modes, offering amenities such as bike racks and pedestrian-friendly pathways (Li et al., 2022). UGPs enhance urban mobility and livability by facilitating seamless transitions between green spaces and transportation and improving public transit systems, bicycle infrastructure and walkability. These infrastructures foster a favorable environment for walkers and bicycles, promoting nonmotorized transportation (Forsyth, 2015). Physical education programs encourage physical activity, contributing to the prevention and management of chronic conditions such as obesity, diabetes and hypertension (Mackenbach et al., 2014). UGPs foster a more health-conscious, environmentally sustainable and habitable urban setting (Baobeid et al., 2021).

The Smart Cities Mission (SCM) in India aims to develop urban areas with essential infrastructure, a satisfactory standard of living, an environmentally friendly atmosphere and intelligent solutions. Local regulations significantly influence the creation of UGPs (Mugambwa et al., 2020). The NRPA (2017) studied sustainable practices by park and recreation authorities in 2017 and highlighted the importance of adopting green infrastructure for sustainability. Municipal policies play a crucial role in establishing UGPs, and the Smart City Mission is a practical policy framework for Ranchi city. Addressing policy deficiencies unique to Ranchi city is essential for seamlessly integrating UGPs with existing urban infrastructure (Mehta and Kumar, 2019). Not only the establishment of UGPs but also long-term sustainable management to keep parks healthy and viable must be considered (Pantaloni et al., 2022; Aly and Dimitrijevic, 2022; Lange and Rodrigues, 2021; Kiboi et al., 2014; Haq, 2015).

Traditional analytical hierarchical process (AHP) and fuzzy AHP (F-AHP) have been two primary research techniques in recent decades, with geographic information system (GIS) being used to address complex land-siting issues (Li et al., 2022). Zhang et al. (2019) mapped urban park viability using AHP and GIS, considering factors such as land availability, environmental impact, air pollution and proximity of residents. F-AHP (Li et al., 2022) uses fuzzy logic to classify factors by their importance and determine their relative weight, helping to identify suitable park areas by overlaying these factors on GIS maps. Pakfetrat et al. (2018) used fuzzy numbers to assign weights to physical, environmental, social and economic factors when choosing locations for regional parks. Zabihi et al. (2020) analyzed ecotourism’s potential using F-AHP. Weight determination for criteria allocation is critical in resolving land-siting challenges when dealing with numerous criteria. AHP is a prominent approach in multicriteria decision-making (Ustaoglu and Aydinoglu, 2020), with a significant portion of contemporary research relying on the amalgamation of AHP and GIS (Pussella and Lin, 2019) and the occasional incorporation of fuzzy methodologies (Seyedmohammadi et al., 2019a, 2019b; Mahmoud and El-Sayed, 2011; Chandio et al., 2011; Gül et al., 2006a, 2006b).

The city of Ranchi has experienced a significant increase in built-up area since 2000 but has experienced a decrease in open space and greenery (Table 3). The World Health Organization (WHO) has recommended providing individuals with UGSs. According to their guidelines, a minimum of 9 m² of UGS should be made available per person, while an ideal amount would be 50 m² per person. However, Ranchi currently has a UGS allocation of less than 8 square meters per citizen. However, the suitability of a site for UGP development varies in terms of accessibility, cost-effective maintenance, public benefits, ecological services, groundwater recharge and application. Managing sustainable UGPs is crucial in emerging nations such as India, where urban areas and residents are rapidly expanding. The primary objective is to determine the optimal sites for establishing UGPs using the analytic hierarchy process (AHP) and GIS-based multicriteria decision analysis (MCDA) technique (Seyedmohammadi et al., 2017).

This paper aims to use analytical hierarchical analysis (AHP) in conjunction with GIS to create a precise map for placing UGPs in Ranchi city and identifying ideal locations for their establishment. This study seamlessly integrates GIS and AHP methodologies (Figure 3), incorporating ecological and environmental factors into the criteria for selecting urban park sites and bridging a gap in the exploration of sustainable urban park development (Yigitcanlar and Dizdaroglu, 2015). The construction of a model for estimating the locations of UGPs in the Ranchi city area is undertaken, and its validation is conducted using a thorough study of the AUC-ROC (area under the curve – receiver operating characteristic) curve, as shown in the studies conducted by Mohsin et al. (2022). Finally, a comprehensive map illustrating the selection of UGP sites throughout Ranchi city is generated. The methodological framework enhances the synergy between AHP and GIS (Figure 3).

1.1 Study area

Ranchi city (Figure 1), located in eastern India, is the capital of Jharkhand and covers 174.9 km². The coordinates are marked between 23°14’44” and 23°25’25”N latitude and 85°15'39” and 85°23’31”E longitude. As of 2022, its built-up area was 81.3 km². Ranchi is part of the Smart City Mission, which was introduced by the Indian Government in 2016 (Prasad et al., 2021). Ranchi, a second-tier city, has a subtropical climate with hot summers from March to May and well-distributed rainfall during the southwest monsoon from June to October. Winters are dry and cold from November to February. The city’s elevation 650 m above sea level provides a pleasant climate, but the Tropic of Cancer passes through it. Global warming has increased temperatures, with average temperatures between 18°C and 29°C and maximum temperatures of approximately 44°C (Mohanta and Sharma, 2017). Ranchi, a second-tier city by class, has more than 121 parks by 2022 (Figure 2). The city aims to strategically design and locate UGPs to meet local needs.

2. Database and methodology

2.1 Data base

Based on the literature and guidelines for assessing the site suitability of UGPs (Uy and Nakagoshi, 2008; Ustaoglu and Aydinoglu, 2020; Gelan, 2021), fourteen essential criteria for determining urban green park suitability in Ranchi city were identified. These variables are 1) NDVI, 2) land use and land cover (LULC), 3) population distribution, 4) particulate matter 2.5, 5) urban heat island (UHI), 6) distance from a water body, 7) distance from a bus stop, 8) distance from a road, 9) distance from a river, 10) distance from a railway station, 11) elevation, 12) slope, 13) temperature, and 14) rainfall (Table 1). Indicator-specific GIS raster data sets were collected and processed from various sources. Multisource data from the CartoSat DEM, Sentinel-2A, Sentinel-5P, and Landsat 8 OLI were used to map the site suitability of the UGPs (Table 1). The Census of India and District Statistical Handbook provided wardwise population information. The average temperature and rainfall data were sourced from the Climate Research Unit and the Indian Meteorological Department. The ESRI shape file and Google Earth Pro were used to extract significant highways, rivers, bus stops, train stations, water bodies and parks that were already in place (Table 1).

2.2 Methodology

An analysis of the published research (Table 2) and geographical observations was carried out to discover the factors that contributed to the emergence of UGPs as a phenomenon. ArcGIS and Google Earth Engine extract all the component theme layers. Spatial analysis techniques were used to examine the information, including resizing, Euclidean distance, conversion, a raster calculator, reclassification and resampling with a resolution of 10 m. The AHP calculator determines the pairwise comparison matrix and the consistency ratio (CR) by weighting the thematic layers and subclasses. ArcGIS performs a weighted overlay of all the theme layers to determine the locational appropriateness of UGPs. A flowchart illustrating the study’s methodological approach is shown in Figure 3.

2.3 Identification of influencing factors

City planning is crucial for determining the locations of parks and green spaces, as they improve living standards, provide social connections and maintain a healthy environment. UGPs must be developed considering accessibility, size and proximity to residential areas. Natural plant cover is influenced by the NDVI and proximity to rivers, small bodies of water and rainfall. Reducing water use and minimizing tree planting can enhance the cost-effectiveness of the development process. However, steep slopes and high altitudes pose health risks due to soil erosion and costly maintenance. UGPs are designed for optimal utilization by urban dwellers and should occur close to highly populated regions. The relationship between PM 2.5 and UHIs is essential, as UGPs can mitigate PM 2.5 levels and alleviate the heat island effect. A strategic location is recommended to minimize the impacts of pollution and heat islands. Figure 3 provides a thorough explanation of the suitability analysis procedure. Further information on the elements influencing site suitability is given below.

2.3.1 Normalized difference vegetation index.

The foremost environmental factor for choosing an ideal site for a UGP is the NDVI. According to the related literature (Gelan, 2021), high NDVI values are suitable for sustainable UGPs because they promote a wide variety of biodiversity, a high degree of greenery, natural beauty, natural cooling, better air quality, low levels of noise, stress-free environments and support for human mental health (Abebe and Megento, 2017). The NDVI is computed using equation (1) for bands 8 and 4 of Sentinel-2A (scihub.copernicus.eu). Therefore, UGPs must be established in regions with high NDVI values to prevent such issues. The NDVI layer map in this investigation was divided into five classes: < −0.041, −0.041–0.136, 0.136–0.314, 0.314–0.492 and 0.492–0.67 [Figure 4(a)].

(1) NDVI=(B8−B4)/(B8+B4)

2.3.2 Land use and land cover.

An essential step in carrying out UGP development is assessing LULC. Using the maximum likelihood classification method and Sentinel-2A data (scihub.copernicus.eu), the LULC map of Ranchi city was classified as built-up, open land, industrial region, airport, vegetation cover or water body [Table 3; Figure 4(b)]. Open space and vegetation have the most potential for sowing UGPs, conveying the most weight (Li et al., 2022). A total of 1,316 random points were selected for the accuracy evaluation of the LULC layer, and they were validated in Google Earth Pro with a 93.01% kappa coefficient, as shown in Table 4. Built-up areas, industrial areas, airports and water bodies are the least favorable locations for the further development of UGPs.

2.3.3 Population distribution.

UGPs refer to a kind of urban infrastructure formed considering the population concentration in a specific area (Gelan, 2021; Li et al., 2022; Abebe and Megento, 2017). The ward-by-ward shapefile was updated from the Census of India to create the population distribution thematic layer (www.ranchimunicipal.com). More populated wards are considered highly suitable for the development of UGPs. The population distribution layer was categorized into five classes: < 13,288, 13,288–19,630, 19,830–25,873, 25,873–32,115 and 32,115–38,358, as shown in Figure 4(c).

2.3.4 Particulate matter 2.5.

High concentrations of PM 2.5 May harm human health, increasing the risk of heart disease and respiratory issues. To maintain a safe and healthy environment for locals, PM 2.5 levels must be closely monitored and managed. As a result, taking PM 2.5 data into account is critical for developing sustainable and ecologically friendly park sites. Ranchi city has a significant problem with PM 2.5 air pollutants [Central Pollution Control Board (CPCBs)]. Therefore, UGPs are more likely to flourish in places with low levels of airborne particulate matter (PM 2.5) (Li et al., 2022). The AOD layer of Sentinel-2A satellite data is used to calculate PM 2.5 using equation (2) station data from the Central Pollution Control Board (cpcb.nic.in), and a private organization (www.aqi.in) is used to verify the data. The PM 2.5 layer was divided into classes: < 90, 90–95, 95–100, 100–105 and >105 µg/m^3, as shown in Figure 4(d):

(2) PM2.5=Slope×AOD+Offset  (Hai & Kondragunta.,2021)

The slope is a coefficient representing the relationship between AOD and PM2.5, and the offset is a constant value.

2.3.5 Urban heat islands.

One of the most crucial environmental elements in determining a site for UGPs is the heat island effect. Heat stress, biodiversity support, influence on human health and opportunities for outdoor activities, which are areas less susceptible to the heat island effect, are preferred for UGPs (Li et al., 2018; Li et al., 2022). The UHI effect is computed by determining the land surface temperature (LST) using band 10 data from the Landsat 8 OLI. Equation (2) enables the UHI effect to be selected from the LST. The UHI layer was classified into the following classes: < 7, 7–11, 11–15, 15–19, and > 19°C, as shown in Figure 4(e):

(3) UHI=LST-LSTmLSTsd

where LST is the land surface temperature, LST_m is the mean LST and LST_sd is the standard deviation of the LST.

2.3.6 Distance from bus stops.

When selecting an ideal location for UGPs, the distance from the nearest bus stop is crucial (Li et al., 2022). This factor is significant because it directly affects how conveniently potential users can use UGPs. Locations within the walking distance of bus stations are preferred because they allow city dwellers to reach the UGPs quickly and easily. Figure 4(f) categorizes bus stop distances into five classes: <500, 500–1000, 1000–2000, 2000–3000 and >3000 m.

2.3.7 Distance from water bodies.

The proximity of water is crucial for UGPs because it offers a cool atmosphere, natural beauty and pure air and supports a broad spectrum of different species (Abebe and Megento, 2017). Furthermore, water in UGPs encourages leisure activities, making it a desirable travel destination. Additionally, it contributes to UGPs’ overall visual appeal and offers a calm, tranquil setting for rest and renewal. As shown in Figure 4(g), the same Euclidean distance is computed and divided into five categories: 500, 500–1000, 1000–2000, 2000–3000 and > 3000 m.

2.3.8 Distance from the river.

The location of UGPs near a river is an essential factor because it enhances the aesthetic appeal of its surroundings. Therefore, UGPs should sit in the area closest to the river to prevent these issues (Gelan, 2021). The distances from the river map layer were divided into five groups in this investigation [Figure 4(h)]: 500, 500–1,000, 1000–2000, 2000–2500 and > 2500 m.

2.3.9 Distance from roads.

The distance from roads is an essential factor to consider for UGPs. It is no secret that city dwellers are constantly looking for ways to reduce their travel time to their favorite spots of relaxation. Therefore, the best location for the siting of UGPs is conveniently close to major roads (Mahmoud and El-Sayed, 2011). This research divided the distance from the road layer into five categories [Figure 4(i)]: 200, 200–500, 500–1000, 1000–1500 and > 1500 m.

2.3.10 Distance from stations.

The location of stations is a critical consideration in the development of UGPs. In our study area, railways are a significant mode of transportation. Ranchi residents benefit more from a UGP closer to the station because it provides easy and time-efficient accessibility (Ustaoglu and Aydinoglu, 2020). According to Figure 4(j), there are five categories for the distance from the station map layers: 1000, 1000–3000, 3000–5000, 5000–7000 and > 7000 m.

2.3.11 Elevation.

The elevation of the area is one of the primary considerations in developing UGPs. A CartoSat DEM at a resolution of 2.5 m was used to generate the elevation map (bhuvan.nrsc.gov.in). According to a recently conducted study (Giordano and Riedel, 2008; Li et al., 2022), places characterized by high elevation are not regarded as appropriate for developing sustainable UGPs because of adverse weather conditions and rugged terrain for outdoor activities. On the other hand, low-elevation locations are regarded as suitable locations for UGPs. Figure 4(k) shows that the map was divided into five distinct classes for this investigation: < 570, 570–600, 600–630, 630–660 and > 660 m.

2.3.12 Slope.

The slope of an area is one of the most crucial aspects for the long-term viability of UGPs. A CartoSat DEM with a resolution of 2.5 m was used to create the slope map (bhuvan.nrsc.gov.in). It is a significant factor when determining an appropriate zone for the health management of UGPs (Gül et al., 2006a, 2006b; Li et al., 2022). As shown in Figure 4(l), the present study classified the slope map into five categories: 7°, 7°–14°, 14°–21°, 21°–28° and >28°.

2.3.13 Temperature.

The degree of thermal comfort should be given importance when planning the site suitability of UGPs. The temperature layer was derived from CRU gridded temperature data (Climatic Research Unit). High-temperature zones are more likely to experience extreme heat stress, endure fewer diversified species and make it difficult to engage in outdoor activities; low-temperature zones are preferable (Gül et al., 2006a, 2006b; Li et al., 2022). As shown in Figure 4(m), the temperature map was categorized into the following five classes for this investigation: 29.54°C, 29.54°C –29.64°C, 29.64°C –29.74°C, 29.74°C –29.84°C and >29.84°C.

2.3.14 Rainfall.

Rainfall is an essential factor in the evaluation process of finding a suitable location for UGPs. The IMD rainfall data gridded at 0.25 × 0.25 were used to produce a rainfall map (Pai et al., 2014). Due to the capacity to create natural greenery and natural beauty, improve air quality and outdoor comfort and maintain a broad variety of biodiversity, regions that receive plenty of rainfall are assumed to be more suitable (Li et al., 2022). This study divided the rainfall zones into five groups, as shown in Figure 4(n): <1480, 1480–1486, 1486–1492, 1492–1498 and > 1498 mm.

3. Analytical hierarchy process

Saaty created the AHP in 1980. It is a comprehensive approach to making informed decisions based on several criteria (Saaty, 1980; Halder et al., 2022). The technique requires ordering criteria in a hierarchical structure to quantify relative preferences in an inventory of choices on a ratio scale (Chandio et al., 2011). In addition, numerous investigators have highlighted the significance of using an analytical hierarchy method in land suitability assessments for UGPs (Gelan, 2021; Ustaoglu and Aydinoglu, 2020; Abebe and Megento, 2017; Gül et al., 2006a, 2006b). Decisions on the importance of various criteria were made using the AHP and expert opinions on the impact of multiple factors on the development of UGPs (Zhang et al., 2019; Li et al., 2022; Pakfetrat et al., 2018). A pairwise comparison matrix was calculated using aii =1 and aij =1/ai. The right eigenvector derived from the maximum absolute eigenvalue (λ_max, 1, 2) is used to determine the significance coefficients for the ranking criteria and subcriteria. All predicted values for the criterion are scaled to a value of 1.

The following equation uses the eigenvector method (Gelan, 2021; Kumar and Krishna, 2018) to determine the principal eigenvalue (λ):

Step 1: The following equation results from using the eigenvector method (Gelan, 2021; Kumar and Krishna, 2018) to determine the principal eigenvalue (λ):

(4) λmax=1n∑win(AW)iN

Step 2: A comparison matrix based on pairwise comparisons was constructed (Saaty, 1980).

(5) AW ={a11 a12… a1na21 a22…a2na31 a32… a3n⋮        ⋮          ⋮an1 an2… ann}×(W1, 2, 3…n)

W is the corresponding eigenvector of λ_max, and wi (i =1, 2, 3, n) is the weight value for the ranking. Saaty’s pairwise scale was used to determine the rank of each pairwise comparison to provide relative importance to the chosen criteria for land suitability for UGPs. As proposed and developed by Saaty (1980), the scale ranges from 1 to 9 points (Table 5).

Step 3: According to Saaty (1980), the consistency of the decision matrix needs to be evaluated using the consistency index (CI). The formula may be derived by following the instructions given below in equation (6).

(6) CI=λmax−1n−1

where CI is the consistency index, λ_max is the maximum or principal eigenvalue of the decision matrix and n is the order of the matrix.

Step 4: The approach proposed by Saaty (1980) is used while computing the CR coefficient. The CR coefficient represents the overall consistency of the pairwise comparison matrix, which should be less than 0.1. The decision matrix will be deemed “inconsistent” and needs a new evaluation if the CR rises above “0.1.” At the same time, a CR value of “0”, or somewhere between 0 and 0.09, will only be recognized as consistent. However, the following equation [equation (7)] expresses the CR (Saaty, 1980):

(7) CR=CIRI

where:

CR = the consistency ratio;

CI = consistency index;

RI = random index; and

RI = random index (Table 6).

3.1 Identifying appropriate locations for urban green parks

The multicriteria suitability map for UGPs is a dimensionality-free product that can predict which land-use sectors are most likely to provide sustainable UGP site locations worldwide. The locational appropriateness of UGPs has been mapped using weight overlay analysis on the GIS platform approach in the present assessment (Giordano and Riedel, 2008; Li et al., 2018; Navidi et al., 2022), as shown in equation (8).

(8) S=∑t=1m∑f=1n(Wt+Xf)

The formula calculates the rank value of each class by multiplying the normalized weight (W_t) of each thematic layer (t) by the rank value of the class in that layer and then summing the results for all the thematic layers. The number of thematic layers and the number of classes in each layer are represented by m and n, respectively (Duguma and Duguma, 2022).where W_t is the normalized weight of the t thematic layer, X_f is the rank value of each class for the f layer, m is the total number of thematic layers and n is the total number of classes in the thematic layer (Duguma and Duguma, 2022). The normalized weights based on AHP analysis are displayed in Table 7, while the weights assigned to each thematic layer can be seen in Table 6. Urban park suitability, measured across multiple criteria in a single layer, is computed by Equation (9).

(9) LM=NDVIwi×NDVIr+LULCwi×LULCr+PDwi×PDr+PMwi×PMr+UHIwi×UHIr+DWwi×DWr+DBSwi×DBSr+DRwi×DRr+DROwi×DROr+DRSwi×DRSr+ELwi×ELr+SLwi×SLr+TEMwi×TEMr+RFwi×RFr

Two examples of criteria are the NDVI weight index (NDVI_wi) and the NDVI rank (NDVI_r); similarly, the LULC weight index (LULC_wi) and the LULC rank (LULC_r) are two examples of criteria. The PM_wi value is the weight index for the particulate matter 2.5 criterion, and the PM_r value is the rank for those criteria. The weighted distribution index (PD_wi) and ranked distribution index (PD_r) are acronyms for the two measures used to classify populations. PM_wi and PM_r are the abbreviations for the weight index and rank of the particulate matter 2.5 criterion, respectively. Two criteria are used to identify UHIs: the weighted index (UHI_wi) and the rank (UHI_r). The water-body proximity weight index (DW_wi) and water-body proximity rank (DW_r) measure how close a location is to a body of water. Distance from bus stops (DBS_wi) and proximity to bus stops (DBS_r) are two examples of distance-based criteria; similarly, distance from rivers (DR_wi) and proximity to rivers (DR_r) are two examples of distance-based criteria. DRO_wi is a weight index representing the distance from roads criterion, and DRO_r is a rank indicating the proximity of the distance from roads criterion. The weight index of the distance from the rail station (DRSwi) and DRS_r is the proximity rank of this criterion. The weight index of elevation is ELwi, and EL_r represents the rank criterion. Sl_wi can represent the weight index for slope criteria, and SL_r is the rank of these criteria. The weight index for the temperature criterion is TEM_wi, and TEM_r is the criterion rank. The weight indices of the rainfall criteria, denoted by RF_wi, where RF_r is the rank of the criteria, from highest to lowest, are used.

Temperature and rainfall are widely acknowledged as crucial elements in establishing UGPs due to their capacity to provide favorable conditions for the proliferation of green vegetation, the creation of a conducive habitat and the facilitation of economic management. Nevertheless, it is worth noting that the city of Ranchi exhibits negligible fluctuations in both temperature (0.34°C) and rainfall (18 mm) throughout its whole [Figure 4(m) and 4(n)]. Consequently, the temperature and precipitation patterns in the city exhibit a notable degree of similarity, which is a factor of relatively less significance in the assessment of ideal sites for UGPs.

3.2 Validation

An accurate evaluation of the final output of spatial modeling is an essential extra step in producing a reliable output that allows for the best potential application of the study's findings (Mohsin et al., 2022). A map of UGP site selection was created for the research area, and its accuracy was measured using the area under the curve (AUC)-ROC curve (Figure 8). Google Earth Pro narrowed the search to 77 locations, ranging from very suitable to completely unsuitable.

3.2.1 Restricted zone for the development of new urban green parks.

The goal of establishing new UGPs necessitates the prohibition of specific land use categories. The feasibility of developing additional UGPs is hindered by the accumulation of water bodies, airports and industrial zones. As a result, the regions have been omitted from the site appropriateness study shown in Figure 6 and are therefore considered unsuitable.

4. Results and discussion

This study used spatial multicriteria technologies and GIS to create a site suitability map for ideal universal geographic places (UGPs). Fourteen factors, including the physical world, environment, resource availability and human behavior, were considered to determine the best locations for long-term UGP sustainability. The AHP method was used to find suitable park locations, and a pairwise comparison matrix was constructed to evaluate the relative importance of the criteria using the Saaty scale of 1–9. The criteria were weighted based on the input of 13 specialists, and the CR index was checked for consistency. With a CR of 0.056 (CR < 0.1), this method is acceptable for modeling and classification in reliable spatial decision-making. A spatial suitability map was generated when final weights were applied to each map in the criterion layers of the GIS environment (Figure 5, Tables 8 and 9). The multicriteria spatial modeling method for potential UGP locations in Ranchi city (Figure 7) revealed that 54% of the area was deemed inappropriate for additional UGP development, while only 5.78% was highly suitable (Table 10). The central and peripheral regions of Ranchi city were found to be ideal for planning new UGPs.

New parks seem to be spatially incompatible with existing parks. The ROC curve/area under the curve (AUC) approach was used for these sites to evaluate the accuracy of the final map. With an AUC value of 89.5% (Figure 8), the final map (Figure 7) of the planned UGP locations in this research has excellent accuracy (more than 85% accuracy) (Mohsin et al., 2022). Despite these strict criteria, this study demonstrated the use of GIS and computers for analyzing the suitability of urban greenway (UGP) sites. It allows for more flexible outputs, enhancing credibility and reliability. This research aligns with global studies on UGP suitability analysis and could serve as a foundation for further investigation. UGPs significantly improve environmental quality, including soil degradation, air pollution reduction, groundwater recharge, population health, landscape beauty and tourist attractions. The growth of city parks has a favorable effect on the welfare and economics of the community. Studies by Wang et al. (2021), Navidi et al. (2023), Seyedmohammadi and Navidi (2022) and Olaniyi et al. (2018) revealed similar findings. The study showed that green spaces disappeared after Ranchi became Jharkhand’s capital in 2000, indicating that environmental authorities struggled to control degradation. A quantitatively integrated AHP and GIS environment was used to create a computerized hierarchical method for selecting sustainable UGPs, contributing to the conservation of urban greening due to uncontrolled expansion (Seyedmohammadi and Navidi, 2022; Seyedmohammadi et al., 2019a, 2019b).

Considering socioeconomic variables, urban planners and lawmakers should consider community inputs in UGP siting. Low-income neighborhoods may lack private vehicles, so parks should be walkable or near public transit. Affluent neighborhoods may have remote green areas or recreational activities, reducing parking demand. Parks in low-income regions can provide leisure with free or cheap entrance costs. Using indigenous flora in landscape design can preserve local fauna while reducing water consumption. Limiting pesticide and fertilizer use is recommended. Solar panels can generate renewable energy for park services. The park should prioritize accessibility for disabled individuals, provide accessible pathways and offer educational materials for local ecosystems and sustainable practices. Events, seminars and guided tours should raise awareness about sustainable practices. Local participation in decision-making is encouraged, and efforts should be made to control invasive species and manage ecological health. Regular water, air and air quality evaluations should be conducted to understand visitor preferences. Pantaloni et al. (2022), Aly and Dimitrijevic (2022), Lange and Rodrigues (2021), Kiboi et al. (2014) and Haq (2015) proposed developing and executing comprehensive management strategies that emphasize the principles of sustainability and resilience. UGPs can become essential community assets that improve locals’ quality of life and protect the environment through the use of these sustainable management techniques.

4.1 Challenges and limitations

Every city has unique characteristics in regard to its climate, topography, geomorphology, population distribution, economics and land use. Consequently, the elements that influence suitability analysis vary city to city depending on the location. Based on their study area, prior research performed in various regions of the world has identified numerous variables that contribute to the selection of a suitable location for the development of UGPs (Table 2). Fourteen (14) factors were identified in this study based on the features of the study area. A few factors are based on prior studies, while others are determined by taking local requirements into account. For urban planners to create long-term, sustainable and workable UGPs, it is extremely difficult to account for the constant changes in LULC, population growth, climatic patterns and environmental conditions. Another significant obstacle for the establishment of UGPs is land acquisition from land holders or stakeholders.

After 2000, the LULC of Ranchi city changed quite rapidly. The study concluded that 54% of the region is not suitable for UGPs based on its analysis of suitability using current LULC, population distribution, environmental, accessibility and natural variables. The LULC, population distribution, PM 2.5 concentration, UHI impact, infrastructure development (e.g. new roads, rail lines and bus stops), temperature and rainfall patterns are highly likely to shift in the future if historical patterns continue. There is a substantial possibility that the suitability area might change in the future. Less suitable areas can be converted into moderately to highly suitable areas by constructing new roads, rail lines and bus stops. This spatiotemporal modification will provide an opportunity for future researchers.

Although the combination of GIS technology and the AHP technique has become a common way to address multicriterion land siting problems, it is important to recognize that the raster layer approach and linear overlay rely solely on the integrity of the recognized standard spatial data sources for accuracy. Furthermore, given the interdependence of the criteria used for the AHP index factor system, it is possible that using a crude spatial linear weighting would unintentionally cause some of the restrictions of important factors to be overridden by other criteria. This makes it more difficult for the assessor to properly address these limitations. Consequently, future research must fully consider the variation in criterion weights resulting from the wide range of preferences of those making decisions in this situation. We highly advise implementing other assessment procedures, such as OWA techniques, artificial intelligence and genetic algorithms, in future UGP site selection attempts to ensure the consistency of evaluation decisions across multiple methodologies.

There are many different kinds of urban parks, and they are all distinguished by unique goals, features and locations. Although this study’s criterion system considered UGPs, it did not consider the distinctive qualities that are particular to certain park types. Future in-depth research on urban parks should focus on developing a uniform approach suited to different urban park categories to fully address these constraints. Urban regions that possess excellent biological conditions and abundant natural vegetation, for example, might be identified as ideal sites for green parks. In the same way, locations that are easily accessible and near residential areas are excellent potential locations for community park development. It would be beneficial for decision-making authorities in this case study area to establish policies specific to the development of UGPs, informed by an investigation of the many configurations that these parks might adopt.

The model used in this study has the potential to be applied more broadly across the global, regional and local levels, providing a useful tool for accurate UGP development and planning initiatives to solve UGS shortage problems due to rapid and unplanned urbanization.

5. Conclusions

UGPs are crucial for addressing environmental issues and promoting sustainable urban development. They offer ecosystem services, improve air quality, reduce noise pollution, combat the UHI effect and promote mental well-being. However, many metropolitan areas lack adequate parks, necessitating the creation of new UGPs. The benefits of UGPs for public health, ecosystem services and environmental quality are significant. Selecting the best locations for UGPs is complex, considering physical, natural, environmental, access and human activities. Combining suitability analysis with multicriteria decision analysis is a robust method for determining suitable sites. Incorporating ecological considerations ensures the sustainable existence of UGPs while protecting the surrounding natural environment. Analytical hierarchical analysis (AHP) and GIS are often used approaches in the process of choosing UGP locations, sometimes supplemented by the inclusion of fuzzy methodologies. Fourteen assessment criteria were devised and used in spatial suitability modeling based on the literature and expert opinions. A few examples of these factors include the NDVI, LULC, population density, particulate matter (PM), UHI effect, river distance, bus distance, road distance, rail station distance, water body distance, elevation, slope, temperature and precipitation. GIS spatial overlay technology was used to generate the final suitability map for the UGPs. The places on the map were rated according to how appropriate they would be for the park: very highly suitable 5.78% (10.11 km²), highly suitable 11.52% (20.15 km²), moderately suitable 12.76% (22.32 km²), less suitable 15.55% (27.20 km²) and unsuitable 54.39% (95.12 km²). In addition, this map has an AUC of 89.5%. The results of this research provide local decision-makers and urban environmental planners with significant geographical insights into the difficulties impacting Ranchi city. However, the study has shown that the suitability map for the UGPs does not align with specific existing urban park sites. These areas need to be re-evaluated by the Ranchi municipal area’s metropolitan planning agency and government authorities. This research will consider various ecological and environmental issues throughout the decision-making process. The use of a specific methodology significantly improves the decision-making process in selecting urban growth and planning (UGP) sites, hence equipping urban planners with a comprehensive and empirically supported body of knowledge.

5.1 Policy recommendations

The implementation of the Miyawaki forest approach, which uses indigenous plant species, has the potential to accelerate ecological development inside UGPs. Additionally, it is essential to prioritize community participation by actively incorporating local citizens in the planning and development process. UGPs should be designed with community participation in mind to ensure that they are tailored to their unique needs and preferences. Integration solutions can address spatial incompatibility between existing and new parks, creating a unified network of green spaces. A monitoring and evaluation system is essential for future growth. GIS and computer-based methodologies can enhance decision-making credibility. Policy initiatives should prioritize UGPs for environmental quality, soil conservation, air pollution reduction and social interaction. Central and peripheral regions should be prioritized for UGP development while focusing on their economic potential for tourism and recreation. Urban growth regulations should be implemented to protect green spaces and improve transport infrastructure. Comprehensive strategies and sustainable managerial approaches are crucial for ensuring the long-term ecological, financial and societal viability of UGPs.

Figures

Figure 1.

Study area map

Figure 2.

Urban parks in Ranchi City

Figure 3.

Methodological framework

Figure 4.

Layer factors

Figure 5.

Final weight ratio of sub-criteria based on AHP

Figure 6.

Eliminated areas from suitability analysis due to restriction

Figure 7.

Final suitability of urban green parks sites

Figure 8.

AUC/ROC Accuracy assessment of the final map

Table 1.

Data and data sources of various factors

Sl no.	Data source		Year	Data type
1	Census of India (COI)		2011 and latest or Estimates	Demographic data (population, density, etc.)
2	District statistical handbook (DSH)		2011 and 2021	Demographic and land use information
3	IMD/CRU (climate research unit)		2010–2022	Rainfall and temperature
4	ESRI shape-file		—	Road, river, railway
5	Google earth pro		—	Roads, waterbodies, bus stop, railway station, existing parks
6	Jharkhand space application Centre			Ranchi city ward wise shape-file
7	Ranchi municipal corporation (RMC)		2011 and latest	Ward wise population
8	Satellite image	Landsat-8 OLI	2022	LST and UHI
		Cartosat DEM	—	Slope, aspect and elevation
		Sentinel −2A	2022	NDVI, LULC, AOT and PM 2.5
		Sentinel −5P	2022	Air pollutants (SO2, NO2, CO, O3)
9	CPCB/JPCB		2022	PM 2.5

Source: Prepared by the authors

Table 2.

A Literature review to select factors for suitability analysis of UGPs

Reference	NDVI	LULC	PD	PM2.5	UHI	DR	DBS	DRO	DRS	DW	EL	SL	TEM	RF
Gelan (2021)	☑	☑	☑	☒	☑	☑	☑	☑	☒	☒	☑	☑	☒	☒
Li et al. (2022)	☑	☑	☑	☑	☑	☑	☑	☑	☑	☒	☑	☑	☑	☑
Li et al. (2018)	☑	☑	☒	☑	☑	☒	☒	☑	☒	☑	☑	☑	☒	☒
Ustaoglu and Aydinoglu (2020)	☒	☑	☒	☒	☒	☑	☑	☑	☑	☑	☑	☑	☒	☒
Abebe and Megento (2017)	☑	☑	☑	☒	☒	☑	☒	☑	☒	☒	☒	☑	☒	☒
Mahmoud and El-Sayed (2011)	☑	☑	☒	☒	☒	☑	☒	☑	☒	☒	☑	☒	☑	☑
Gül et al. (2006a), (2006b)	☑	☑	☒	☑	☒	☑	☒	☑	☒	☑	☑	☑	☑	☒
Giordano and Riedel (2008)	☒	☑	☒	☒	☒	☑	☒	☒	☒	☑	☑	☑	☒	☒

Source: Prepared by the authors

Table 3.

Land use and land cover classification

Land use classes	Area (km2)	Area (%)
Waterbody	3.8	2.17
Built-up	81.3	46.48
Open land	61.7	35.28
Vegetation	20.6	11.78
Industrial zone	3.6	2.06
Airport	3.9	2.23
Total	174.9	100

Source: Prepared by the authors

Table 4.

Accuracy assessment of LULC

Land use classes	Waterbody	Built-up	Open land	Vegetation	Industrial zone	Airport	Total	User accuracy (%)
Waterbody	43	0	0	3	0	0	46	93.47
Built-up	0	541	24	0	3	1	569	95.07
Open land	0	18	421	0	0	1	440	95.68
Vegetation	7	0	0	205	0	0	212	96.70
Industrial zone	0	2	0	0	20	0	22	90.90
Airport	0	1	2	0	0	24	27	88.88
Total	50	562	447	208	23	26	1316
Producer accuracy (%)	86	96.26	94.18	98.56	86.96	92.31
Overall accuracy = 95.29% Kappa coefficient = 93.01%

Source: Prepared by the authors

Table 5.

The random inconsistency value

Intensity of importance	Definition	Explanation
1	Equal importance	Two activities contribute equally to the objective
2	Weak or slight
3	Moderate importance	Experience and judgment slightly favor one activity over another
4	Moderate plus
5	Strong importance	Experience and judgment strongly favor one activity over another
6	Strong plus
7	Very strong or demonstrated importance	An activity is favored very strongly over another; its dominance demonstrated in practice
8	Very, very strong
9	Extreme importance	The evidence favoring one activity over another is of the highest possible order of affirmation
Reciprocals of above	If activity i has one of the above non-zero numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i
Rationales	Ratio arising from the scale	If consistency were to be forced by obtaining n numerical values to span the matrix

Source: Prepared by the authors

Table 6.

The random inconsistency value

Number of criterion	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Random inconsistency (RI)	0.0	0.00	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51	1.54	1.56	1.57

Source: Prepared by the authors

Table 7.

Pairwise comparison matrix

Factors	NDVI	LULC	PD	PM2.5	UHI	DBS	DRO	DR	DW	DRS	EL	SL	TEM	RF
NDVI	1	2	3	3	4	5	5	6	6	6	7	7	8	9
LULC	0.5	1	3	3	4	4	4	5	5	6	7	7	8	9
Population	0.33	0.33	1	3	3	4	4	5	5	5	6	6	7	8
PM 2.5	0.33	0.33	0.33	1	2	3	3	4	4	5	5	5	6	7
UHI	0.25	0.25	0.33	0.5	1	3	3	4	4	4	5	6	7	7
Distance from bus stops	0.2	0.25	0.25	0.33	0.33	1	1	3	3	4	5	5	6	7
Distance from road	0.2	0.25	0.25	0.33	0.33	1	1	3	3	4	5	5	7	7
Distance from river	0.17	0.2	0.2	0.25	0.25	0.33	0.33	1	1	3	4	5	6	7
Distance from water	0.17	0.2	0.2	0.25	0.25	0.33	0.33	1	1	3	4	5	6	6
Distance from rail stations	0.17	0.17	0.2	0.2	0.25	0.25	0.25	0.33	0.33	1	3	3	4	5
Elevation	0.14	0.14	0.17	0.2	0.2	0.2	0.2	0.25	0.25	0.33	1	2	3	4
Slope	0.14	0.14	0.17	0.2	0.17	0.2	0.2	0.2	0.2	0.33	0.5	1	3	4
Temperature	0.12	0.12	0.14	0.17	0.14	0.17	0.14	0.17	0.17	0.25	0.33	0.33	1	2
Rainfall	0.11	0.11	0.12	0.14	0.14	0.14	0.14	0.14	0.17	0.2	0.25	0.25	0.5	1
Total	3.83	5.49	9.36	12.57	16.06	22.62	22.59	33.09	33.12	42.11	53.08	57.58	72.5	83

Source: Prepared by the authors

Table 8.

Normalized pairwise comparison matrix and computation of criterion weightage

Factors	NDVI	LULC	PD	PM2.5	UHI	DBS	DRO	DR	DW	DRS	EL	SL	TEM	RF
NDVI	0.261	0.364	0.321	0.239	0.249	0.221	0.221	0.181	0.181	0.142	0.132	0.122	0.110	0.108
LULC	0.131	0.182	0.321	0.239	0.249	0.177	0.177	0.151	0.151	0.142	0.132	0.122	0.110	0.108
Population	0.086	0.060	0.107	0.239	0.187	0.177	0.177	0.151	0.151	0.119	0.113	0.104	0.097	0.096
PM 2.5	0.086	0.060	0.035	0.080	0.125	0.133	0.133	0.121	0.121	0.119	0.094	0.087	0.083	0.084
UHI	0.065	0.046	0.035	0.040	0.062	0.133	0.133	0.121	0.121	0.095	0.094	0.104	0.097	0.084
Distance from bus stops	0.052	0.046	0.027	0.026	0.021	0.044	0.044	0.091	0.091	0.095	0.094	0.087	0.083	0.084
Distance from road	0.052	0.046	0.027	0.026	0.021	0.044	0.044	0.091	0.091	0.095	0.094	0.087	0.097	0.084
Distance from river	0.044	0.036	0.021	0.020	0.016	0.015	0.015	0.030	0.030	0.071	0.075	0.087	0.083	0.084
Distance from waterbody	0.044	0.036	0.021	0.020	0.016	0.015	0.015	0.030	0.030	0.071	0.075	0.087	0.083	0.072
Distance from rail stations	0.044	0.031	0.021	0.016	0.016	0.011	0.011	0.010	0.010	0.024	0.057	0.052	0.055	0.060
Elevation	0.037	0.026	0.018	0.016	0.012	0.009	0.009	0.008	0.008	0.008	0.019	0.035	0.041	0.048
Slope	0.037	0.026	0.018	0.016	0.011	0.009	0.009	0.006	0.006	0.008	0.009	0.017	0.041	0.048
Temperature	0.031	0.022	0.015	0.014	0.009	0.008	0.006	0.005	0.005	0.006	0.006	0.006	0.014	0.024
Rainfall	0.029	0.020	0.013	0.011	0.009	0.006	0.006	0.004	0.005	0.005	0.005	0.004	0.007	0.012
Total	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
λ = 15.134 n = 14 CI = 0.88 RI = 1.57 CR = 0.056 CR% = 5.6

Source: Prepared by the authors

Table 9.

Weights of the criteria and scores of the Sub-criteria

Sl No.	Criteria	Sub-Criteria	Suitability class	Rating	AHP weights (%)
1	NDVI	< −0.041	Unsuitable	1	20.9
		−0.041–0.136	Less suitable	2
		0.136–0.314	Moderately suitable	3
		0.314–0.492	Highly suitable	4
		0.492–0.67	Very highly suitable	5
2	LULC	Water bodies	Unsuitable	1	17.9
		Built-up	Unsuitable	1
		Open land	Highly suitable	4
		Vegetation	Very Highly suitable	5
		Industrial zone	Unsuitable	1
		Airport	Unsuitable	1
		Airport	Unsuitable	1
3	Population distribution (PD)	< 13288	Unsuitable	1	14
		13288–19630	Less suitable	2
		19630–25873	Moderately suitable	3
		25873–32115	Highly suitable	4
		32115–38358	Very highly suitable	5
4	PM 2.5 (ug/m3)	< 90	Very highly suitable	5	10
		90–95	Highly suitable	4
		95–100	Moderately suitable	3
		100–105	Less suitable	2
		> 105	Unsuitable	1
5	UHI (°C)	< 7	Very highly suitable	5	8.9
		7–11	Highly suitable	4
		11–15	Moderately suitable	3
		15–19	Less suitable	2
		> 19	Unsuitable	1
6	Distance from water bodies (DW) m	< 500	Very highly suitable	5	3.9
		500–1000	Highly suitable	4
		1000–2000	Moderately suitable	3
		2000–3000	Less suitable	2
		> 3000	Unsuitable	1
7	Distance from bus stops (DBS) m	< 500	Very highly suitable	5	6.1
		500–1000	Highly suitable	4
		1000–2000	Moderately suitable	3
		2000–3000	Less suitable	2
		> 3000	Unsuitable	1
8	Distance from river (DR) m	< 500	Very highly suitable	5	4
		500–1000	Highly suitable	4
		1000–2000	Moderately suitable	3
		2000–2500	Less suitable	2
		> 2500	Unsuitable	1
9	Distance from road (DRO) m	< 200	Very highly suitable	5	6.1
		200–500	Highly suitable	4
		500–1000	Moderately suitable	3
		1000–1500	Less suitable	2
		> 1500	Unsuitable	1
10	Distance from the rail station (DRS) m	< 1000	Very highly suitable	5	2.7
		1000–3000	Highly suitable	4
		3000–5000	Moderately suitable	3
		5000–7000	Less suitable	2
		> 7000	Unsuitable	1
11	Elevation (EL) m	< 570	Very highly suitable	5	1.8
		570–600	Highly suitable	4
		600–630	Moderately suitable	3
		630–660	Less suitable	2
		> 660	Unsuitable	1
12	Slope (SL) degree	< 7	Very highly suitable	5	1.6
		7–14	Highly suitable	4
		14–21	Moderately suitable	3
		21–28	Less suitable	2
		> 28	Unsuitable	1
13	Temperature (TEM) (°C)	< 29.54	Very highly suitable	5	1.1
		29.54–29.64	Highly suitable	4
		29.64–29.74	Moderately suitable	3
		29.74–29.84	Less suitable	2
		> 29.84	Unsuitable	1
14	Rainfall (RF) mm	< 1480	Unsuitable	1	1
		1480–1486	Less suitable	2
		1486–1492	Moderately suitable	3
		1492–1498	Highly suitable	4
		> 1498	Very highly suitable	5

Source: Prepared by the authors

Table 10.

Tabulate areas of UGPs suitability degrees

Degree	Suitability degree	Area (km2)	Area (%)
1	Unsuitable	95.12	54.39
2	Less suitable	27.20	15.55
3	Moderately suitable	22.32	12.76
4	Highly suitable	20.15	11.52
5	Very highly suitable	10.11	5.78

Source: Prepared by the authors