This study is an information modeling research. For that purpose, it’s practical and developmental research to measure the degree of the supply shortage or demand excess for urban park facilities. In other words, the present study uses situational weight index and distributive justice for ASD. This research is mixed regarding data collection methods, relies on library methods to review resources, and extracts the required parameters to create a database based on reference land information. at last, to check and calibrate digital information and maps, 50 maps have been used to view and capture geographic coordinates. This research will be dependent on the modeling analysis method and using GIS analysis functions. The analysis of input data, including qualitative and quantitative information, is performed using standard coding and weighting methods. In this paper, we utilize the ASD index used by Lee and Hong (2013) (9) to measure under or over-supply levels for urban park services. An innovative aspect of this paper is the Distributive Justice Index, which will be discussed in detail later. This research approach is that each park will have more or less impact due to spatial characteristics, except for the area and distance from the blocks. Figure 1 summarizes the process of ASD calculation and application of the analytical results. ASDi, which is the quantitative difference between supply and demand in park services per unit area (‘area’ hereafter), is defined as follows:

To measure the spatial justice of urban parks, mathematical relationships and formulas have been used in this research, which is mostly presented by Dadashpour and Rostami Used to measure the spatial justice of parks (Ai) (10). Distributive justice in parks includes the following characteristics:

This index is calculated in the form of relation number (1); the difference in this relation is that the disturbance factor for the blocks adjacent to the parks is eliminated while measuring spatial justice.

The main concern of this research is to develop ASD with respect to the spatial disparity between supply and demand and investigate its empirical applicability. Furthermore, when the centroids of the spatial units represent many starting points to divide a large area into small spatial units for analysis, computing network distances among all of the starting points (residential locations) and ending points (park locations) will be quite difficult and also time-consuming. For this specific reason, using the straight-line distance helps us to focus on the methodological highlight proposed in this study.

As pointed out by Equation [2], Si is the cumulative value of the service size provided by (K) parks (S_ij) (SW_ij denotes spatial weight). In Equation [2], Sij is inversely proportional to spatial weight (SW_ij) on the straight line between i and j, meaning the area of park j is allocated to residential location i. Similarly, the aggregated services supplied by k parks distributed in the area indicate the actual supply level of park services in area i. This research was used by combining the positional weight index and distribution justice for ASD to explain the new ASD index Equation [6].

The prime purpose of the article “Understanding recreation demands and visitor characteristics of urban green spaces: A use of the zero-inflated negative binomial model” is to understand the recreational needs and characteristics of visitors to urban green spaces. In this paper, Kim et al (2021) (11) propose a specific type of comprehensive reconstruction of the Sapphire area. During redevelopment, the spatial arrangement of urban green spaces should be investigated to provide fair recreational opportunities that meet visitors’ recreational needs and characteristics. This study focuses on 29 large urban green spaces in the Sapporo city area and aims to understand the issues mentioned earlier by using the individual travel expense method.

Gyorgy Csomós et al (2021) (12), in the paper “Measuring Socio-Economic Inequalities in Green Space Availability in Post-Socialist Cities,” use three post-socialist Hungarian cities as case studies. The relationship between different types of residential areas in terms of age, examines the education, level of people revenue, and the availability of urban green spaces.

To do this, they introduce a combined demand-supply index that contains specific urban green space availability indicators. The results show significant differences in UGS availability between different socio-economic groups and different types of neighborhoods in the studied cities. These results also show that environmental justice remains a concern for urban planning in post-socialist cities despite the efforts of local governments to provide dwellers with equal access to urban green spaces.

Shuying Zhang et al (2021) (13), in the article named “Characteristics of spatial-temporal distribution and the evolutionary mechanism of urban parks in Beijing, China,” first analyzed the dynamic evolution of urban parks in Beijing by comparing the characteristics of spatial distribution at three different time points in 2005, 2010 and 2017. In addition to changes in number and area, spatial structure and access are also used by direction distribution methods, core density estimation, and network analysis. Then the three-factor analysis method is used to explain the evolution mechanism. Stability factors, dynamic factors, and random factors play a critical role in the spatial development of urban parks through the constraint mechanism, supply demand mechanism, and incentive mechanism discretely.

In their article, Diao, Hu & He (2021) (14) declares that there are significant differences between vulnerable groups in terms of age and revenue and each one of the residents. Urban parks have no special value; however, the distribution of comprehensive parks, neighborhood parks, and small pleasures is unequal for all residents and vulnerable groups. In addition, the value of urban parks can be affected by the scale of the park, where the number of urban parks has a positive impact on access per capita and a negative impact on the Gini coefficient among all residents and vulnerable groups. In other words, there is no fundamental correlation between the number of parks and the proportion of available park which is shared in vulnerable groups. In general, the effect of the park scale on park equity is evident, and policymakers and planners are recommended for public health and environmental equality in urban communities.

Li and assistants conducted another study. Large urban parks, extracted from the vast amount of anonymous cellular signaling data as two fast indicators of recreational use efficiency of large urban parks for 11 cities in the Beijing-Tianjin-Hebei (BTH) area. The results show significant differences between cities in terms of park visits and service areas. Afterward, they carry out Hierarchical Linear Regression (HLR) models to investigate the relationship between two indicators, fourteen city-wide factors, and park-specific factors for 206 samples of large urban parks. The maximum writing clarity must be pursued, trying to use the.

Tehran is the capital and largest city of Iran and the largest city in Tehran Province. With a population of around 9 million in the city and around 16 million in the larger metropolitan area of Greater Tehran, Tehran is the most populous city in Iran and Western Asia, and has the second-largest metropolitan area in the Middle East, after Cairo. It is ranked 24th in the world by metropolitan area population.

The metropolis of Tehran is divided into 22 municipal districts, each with its own administrative center. Of the 22 municipal districts, 20 are located in Tehran County’s Central District, while districts 1 and 20 are respectively located in the counties of Shemiranat and Ray. Although administratively separate, the cities of Ray and Shemiran are often considered part of Greater Tehran.

Northern Tehran is the wealthiest part of the city, consisting of various districts such as Zafaraniyeh, Jordan, Elahiyeh, Pasdaran, Kamranieh, Ajodanieh, Farmanieh, Darrous, Niavaran, Jamaran, Aghdasieh, Mahmoodieh, Velenjak, Qeytarieh, Ozgol and Ekhtiarieh. While the center of the city houses government ministries and headquarters, commercial centers are located further north.

Region 1 of Tehran Municipality is located at the northeastern tip of this city and is considered the northernmost point of Tehran. This region is limited to the Alborz mountain range from the north, Evin region from the west, Ayatollah Sadr Highway from the south, and Lavasanat from the east.

According to the census of 2016, District One of Tehran has a population of 428,457 people, and according to the divisions of the municipality, it has 27 neighborhoods. Four hundred thirty-seven parks are located in this area. According to the division of hierarchical parks, Tehran Region 1 has 20 regional parks and 417 parks in the district and neighborhood, all of which are under study in this study. Figure 2 and 3 shows the location of neighborhoods in Zone 1, and Table 1 indicates the number of parks by type and the sum of their area by neighborhood.

Numerous ideas of landscape design place a strong emphasis on a planning procedure separate from urban social and spatial systems. The substance of landscape planning is reduced as a result of this viewpoint. Planning without a topic or substance is akin to having no idea or intellectual foundation, which prevents you from using your full potential. Justice is one of the essential principles to reach a great city. To study a just in each

City, it is necessary to examine the three components of equality, diversity, and democracy as essential elements of a just city. Order, public health, urban management, and city quality of life were the main concerns of post-industrial revolution urban planners and landscape architects. The major characters of this narrative were mostly urban planners who incorporated elements of equality, society, modernism, and new ethics with novel urban living forms. (15).

The current scientific and dominant political narratives around municipal interventions for land (re)development and urban greening park creation, waterfront restoration, or greenway

Construction emphasize the ecological, social, health, and economic benefits. In this context, landscape planners and investors, along with urban city planners and political decision-makers, play an essential role in producing a green city and, in return, helping to boost a city’s image as livable and desirable, which means “urban greenery” (16). Tensions and contradictions arise from subsequent inequalities that result from the effects of this unbalanced spatial development. The same concerns about the moral authority of greening can be applied to evaluating communication planning approaches for urban sustainability planning (17).

While avoiding top-down decision-making, the communication and dialogue-oriented planning approaches should encourage involvement and inclusion, foster agreement on sustainability planning goals and tactics, and assure lasting choices and plans (18-21). However, the moral weight that urban greening carries as well as the desire for a green city’s worldwide appeal have the potential to dominate agenda-setting, which would have extremely unsettling repercussions for the design of a just green city.

Greening aims can help to reduce the emphasis on asymmetric power relations and disputes over scarce resources, which run the danger of reenacting unfair results. This is similar to the notion of “public goods” and the assumed distributed advantages of access. (22). In this case, the unfair outcomes revolve around the mid-and long-term access to the benefits of urban (green) life.

Recent research on ecological or environmental gentrification has revealed that a combined approach of environmental cleanup, land restoration, and the construction of green amenities is progressively reconfiguring urban districts for residents who are wealthier and whiter. (23, 24).

The state of demand serves as the cornerstone of our methodology for identifying ground-breaking growth prospects. Our tried-and-true method is always changing, but it often necessitates identifying demand segments and studying them in depth to comprehend the requirements of a multidimensional framework. Analyzing the gap between ecosystem service provision and demand might offer pertinent insights for enhancing human well-being in metropolitan contexts.

Different demographic groups have varying degrees of accessibility to public goods, which is explored in some studies as a geographical inequality. In this study, the amount of under- or oversupply for urban park services is determined using the ASD index of Lee and Hong (2013) (9).

The research’s clever use of the Distributive Justice Index is one of its strongest points. The effectiveness of each park may be understood with the aid of this index, which is based on the index determined by calculations done in the supply and demand model. In other words, this study integrates the location quality of parks with the fairness of spatial distribution in the supply and demand model. The size and distance from residential blocks are not the only criteria used to evaluate parks. The methodology of this study assumes that each park will be more or less beneficial depending on its spatial qualities, with the exception of its location and distance from the blocks.

Because of this, the definition of spatial disparity in this work is completely different: it is the geographical difference between the levels of supply and demand for park services in a geographic unit (for instance, a 100 m 100 m square grid).

For every unit:

Service availability, which is quantified based on the gravity model, serves as a representation of supply.

The urban area of Tehran (region 1) was split into a lattice of 100 square meters by 100 square meters for the experimental assessments, and the ASD of each network was determined. The legislation on urban parks, greenways, etc. categorizes these parks into the following groups:

The most effective locations in the park are displayed in Table (2). The differences between places where regional parks are located and areas around the parks contribute to the large final divergences of the parks’ spatial justice.

The ultimate spatial justice index of parks in relation to their effectiveness is improved and expanded by neighborhoods.

In many large parks in the immediate vicinity, residents of less densely populated areas are supplied with a high level of service availability due to the active interaction between residents and parks.

On the other hand, residents of densely populated areas with few desirable parks nearby may lack services. Larger populations (higher demand) lower park size-to-distance ratios (low supply) increase the spatial disparity. Therefore, the spatial disparity is inversely proportional to accessibility.

The map in Figure 5 shows the ASD values for unit areas suffering from a park services supply shortage, resulting from a park-service level that does not correspond to the size of demand population in the areas with affirmative values in Equation [1] of all the lattices Figure 6 within the city. In Figure 5, the unit areas (lattices), where the actual parking service supply to the demanding population is inadequate, are marked in reddish tones; darker spots point out greater excessive demands or more people who are not supplied with the park services.

The closer a large urban park is located around area i, the higher the actual park-service supply, which means accessibility to parks is great in area i. Another way to look at it is that if a park is large but located far away, the low accessibility is offset by its size. In short, access to a small and nearby park can be comparable to the accessibility of a large park located far away.

Therefore, in addition to the repulsion due to spatial factors such as distance, the attraction due to non-spatial factors such as park size (area) should be taken into account when assessing accessibility to the park (25).

Figure 5 shows the distribution of population and parks in the Region 1 of Tehran. The population distribution represents the number of people in the square grids on the choropleth map, where parks have symbols of various sizes depending on their area.

Calculating the ASD based on the population (demand) and parks (supply) distribution makes it possible to identify an area and its residents that the proper park services are not provided.

This result can be applied to prioritize the construction of new parks in areas suffering from supply shortages despite high demand from residents. Interestingly, the methodological basis for derivation of Equation [1] is related to the dissimilarity index (D) introduced by Duncan and Duncan (1955) (26).

The index D is defined as follows:

While PCPA in Equation [1] is the stipulated criterion in institutional regulations, P/S denotes the inverse number of the empirical per capita park area defined as the ratio of the total population to service availability measured in square meters. If the stipulated per capita criterion is permitted in the empirically derived term, P/S may be considered as the quantity inversely equivalent to the per capita park area in Equation [1] (PCPA).

For this argument, the element of D, equal to P_i − S_i × (P/S), is similar to ADS_i. The interpretation is that the closer the index (D) is to one, the more problematic the area is with respect to the overall spatial disparity between supply and demand. This indicates that some localities with high ADSi values contribute significantly to the overall tendency.

In Figure 6, those unit areas (lattices) with inadequate supply of actual park-service against demand-side population are marked in reddish tones, darker spots indicating greater excessive demands, or more people that are not supplied with park services.