Optimization of a Layout for Public Toilets Based on Evaluation of Accessibility Through the Gaussian Two-Step Floating Catchment Area Approach

Abstract

Urban public toilets are essential for improving urban and rural living environments. Traditional evaluations have relied on statistical indicators such as total numbers and network coverage, but have overlooked population demand, limiting their ability to reflect actual service levels and optimize spatial allocation. This study assesses the public toilet service capacity according to spatial accessibility and offers insights into layout optimization. The main urban area of Kunming was considered as the case study. First, the Gaussian two-step floating catchment area (G2SFCA) method was used to calculate public toilet accessibility. The service level of public toilets at the community scale was assessed based on the calculation results. Finally, recommendations for the optimization of the spatial layout of public toilet provision are proposed based on the evaluation findings. Results indicate that (1) 57 communities lacked access to public toilets within a 5 min walk, while only two lacked access within 20 min; all communities had access within 30 min; (2) increasing stalls in old public toilets by 50% would meet the policy requirements for most residents; (3) transportation accessibility has a significant impact on residents’ convenience in accessing public toilets. Areas with lower transportation connectivity tend to have poorer toilet accessibility. The construction of new public toilets near road networks can effectively enhance overall restroom convenience for residents in the study area. By integrating public toilet accessibility with resident restroom demand, this study proposes targeted strategies for optimizing the spatial layout of urban public toilets, offering valuable insights and feasible solutions for improving the scientific and rational allocation of urban public resources.

1. Introduction

Urban living environments are spatial locations closely related to human activity [1]. As a crucial component of urban sanitation facilities, public toilets serve as a “window” reflecting a city’s image and civilization, playing a vital role in improving the urban living environment [2,3,4]. Evaluating public toilet accessibility not only helps assess the overall quality of public toilet infrastructure, but also reveals disparities in facility distribution, guiding necessary improvements [5].

Common standards for assessing urban public toilet service efficiency include the total number of public toilets, per capita availability, and network coverage [6,7]. However, these indicators fail to comprehensively and objectively reflect actual service levels because service capacity is also influenced by the balance between resource allocation and accessibility.

The concept of accessibility was first introduced by Hansen [8], referring to the ease of reaching a destination using a given transportation network [9]. As a geographically significant concept, spatial accessibility is widely studied in various fields such as education, healthcare, urban green spaces, public transport planning, and spatial equity [10,11]. Therefore, measuring the spatial accessibility of public toilets is important for scientifically evaluating the level and equity of urban public health services.

At present, from the perspective of supply–demand relationships, methods for measuring spatial accessibility can generally be classified into two categories: single-supply models and supply–demand ratio models. The single-supply model evaluates accessibility based on the proximity between the demand and supply points. This model can be further divided into distance-based and gravity models depending on whether the supply capacity of the service points is considered. Distance-based models assume unlimited supply capacity and measure accessibility based solely on the distance between the supply and demand points. For example, Obeidat and Alourd [12] used buffer analysis, nearest neighbor analysis, and service area analysis to evaluate healthcare accessibility and facility distribution in Irbid Governorate, Jordan, revealing a random spatial distribution pattern. Similarly, Dogan et al. [13] used the Mapo District as a case study, applying network analysis to simulate apartment accessibility under three street conditions, and conducted site relocation to generate the shortest path scenarios. Another approach is to minimize travel time. For instance, Meire et al. [14] assessed the accessibility in Australia’s inland and airport-adjacent activity zones by aggregating the shortest travel times of three route segments.

However, distance-based models do not account for the supply capacity of each service point in relation to the demand, which may lead to inaccurate accessibility measurements. For example, two public toilets under conditions of equal distance with different numbers of stalls provide different levels of service to nearby residential areas. A toilet facility with more stalls offers higher-quality services and greater convenience. However, the distance-based model assumes equal service capacity for both toilets, and thus yields the same accessibility value, which does not reflect the actual differences in the service.

To address these limitations, scholars have proposed the gravity model as an alternative approach to measuring spatial accessibility [15,16]. The gravity model incorporates the decay of the supply capacity with increasing distance, thereby producing smoother and more objective evaluations of accessibility. For example, Chang et al. [17] applied the gravity model to analyze accessibility across different housing types based on three travel modes. Their study found that while public transportation generally improved access to parks, it also intensified the spatial inequality between public and private housing residents. However, both the distance-based and gravity models share a common limitation; they assume that the level of demand has no impact on the supply capacity. This assumption is inconsistent with real-world conditions, in which higher demand can strain available resources and reduce service effectiveness.

To overcome the limitations of single-supply models, scholars have proposed supply–demand ratio models. These models evaluate accessibility by integrating the size of the demand population and the supply capacity within a defined area. In other words, spatial accessibility at a given location depends not only on the supply capacity and travel distance to service points but also on the number of consumers distributed in the surrounding area. Common methods for calculating supply–demand ratio models include the ratio, cumulative opportunity, potential model, kernel density estimation, and the Two-Step floating catchment area (2SFCA) method. For example, Hare and Barcus [18] applied the ratio method to assess the accessibility of cardiovascular-related healthcare services in Kentucky. Stępniak et al. [19] used the cumulative opportunity method in Szczecin, Poland, to compare four sampling methods, four public transport frequency scenarios, three accessibility metrics, and seven types of destinations, analyzing changes in accessibility outcomes across different time thresholds (from 1 to 15 min). They found that cumulative opportunity measures were highly sensitive to time thresholds and thus less suitable for multi-temporal accessibility analysis. HIGGS [20] employed a potential model to estimate healthcare accessibility in Wales by calculating accessibility scores within two service range thresholds. Moore [21] used kernel density estimation to evaluate the accessibility of recreational facilities and parks in different ethnic communities.

However, these methods often overlook the competition among demand groups or fail to capture detailed spatial variations within a given area, compromising the objectivity of spatial accessibility measurements. Compared with other types of supply–demand ratio models, the 2SFCA method offers a relatively simple calculation process and produces more objective accessibility values [22]. Originally proposed in [23] and later refined in [24], the 2SFCA method has been widely applied in various accessibility assessments [25,26]. The 2SFCA calculation involves two steps. First, for each supply location, a catchment area is defined, and the supply-to-demand ratio within that area is calculated, representing how the supply is distributed among competing consumers. Second, for each demand location, the supply-to-demand ratios of all accessible supply points within the catchment are summed to derive the accessibility score for that demand point. By fully incorporating the influence of supply–demand relationships on accessibility, the 2SFCA method significantly enhances the objectivity of accessibility evaluations.

However, some scholars have argued that the results of the 2SFCA method are highly sensitive to the size of the search radius (distance threshold), which makes it difficult to accurately reflect the effect of distance decay on accessibility. To address this issue, an extended 2SFCA method has been proposed that replaces the fixed search radius with a distance decay function such as a Gaussian function, resulting in a smoother evaluation of accessibility [27,28,29]. Thus, the 2SFCA-based supply–demand ratio model comprehensively considers the influence of supply–demand relationships on accessibility, effectively overcoming the main shortcomings of single-supply models. In particular, the G2SFCA method provides a more continuous measure of accessibility changes with distance, avoiding the abrupt discontinuities inherent in the traditional 2SFCA approach.

This analysis indicates that the G2SFCA method is an ideal model for measuring the spatial accessibility of public toilets. However, evaluating spatial accessibility alone is insufficient to reflect the rationality and equity of public toilet resource allocation intuitively. A reasonable layout for public toilets must avoid resource waste caused by overconstruction, while preventing shortages that negatively impact residents’ convenience. Therefore, optimizing the spatial layout of public toilets based on accessibility evaluation results is critical for scientifically assessing the efficiency and equity of public toilet resource distribution.

The key to addressing this issue lies in integrating spatial accessibility assessments with spatial layout strategies. Accordingly, this study aimed to use the G2SFCA method to evaluate public toilet accessibility under different policy scenarios, identifying areas with efficient services and those with insufficient coverage. Simultaneously, through policy scenario analysis, this study sought to optimize the location and capacity allocation of public toilets based on accessibility results to enhance the spatial balance of public toilet distribution, thereby promoting a configuration that better reconciles efficiency and equity.

Accordingly, this study used Kunming, a popular tourist city in Yunnan Province, China, as a case study. Drawing on the “5 min, 10 min, and 15 min living circle” concept proposed in the Urban Residential Area Planning and Design Standards (Ministry of Housing and Urban–Rural Development of the People’s Republic of China, 2018) and incorporating practical daily experience, walking times of 5, 10, 15, 20, and 30 min were set as thresholds for restroom demand. These thresholds were used to evaluate the spatial accessibility of public toilets and the rationality of related policies in the study area, thereby providing direct decision-making support for optimizing urban public toilet resource allocation.

2. Materials and Methods

2.1. Study Area and Data Sources

Kunming is located in the central part of the Yunnan–Guizhou Plateau in southwest China (102°10′–103°40′ E, 24°23′–26°22′ N), with an urban built-up area of 446.13 km². As an important tourism and commercial hub in western China, Kunming ranks among the country’s top ten tourist destinations and has been designated as a national-level tourist demonstration city. The study area covered parts of the Wuhua, Panlong, Guandu, and Xishan districts, with a total area of approximately 205.9 km², representing the core region of Kunming’s socioeconomic development. To date, Kunming has constructed 3578 urban public toilets, including 1172 first-class, 1848 second-class, and 558 second-class toilets. Within the study area, 1028 public toilets are distributed across 31 subdistricts and 110 community groups. Their spatial distributions are shown in Figure 1.

2.2. Data and Processing

The key factors influencing the accessibility of urban public toilets include the following three aspects, as illustrated in Figure 2.

• Supply side factors: Even when public toilets have the same scale and spatial location, variations in the population distribution in the surrounding area may lead to significant differences in user evaluations of the service. Therefore, the supply side must consider the match between the population density of public toilets and the target service population;
• Demand-side factors: The spatial distribution pattern and total population size are critical determinants of the intensity of the demand for public toilet services and have a direct impact on accessibility levels;
• Connectivity factors: As an essential channel for residents traveling from origins to destinations such as public toilets, the urban transportation system, including its network structure, road density, and connectivity, greatly influences the accessibility assessment results. Typically, ring- or grid-like road structures are conducive to enhancing the spatial accessibility of public toilets.

As shown in

Table 1. Data sources.

Application Modules	Data Type	Data Sources
Supply side	public toilets POI	Gaode Map (https://ditu.amap.com/) and the “You Yunnan” app
Demand side	population distribution	The Seventh National Population Census
Connected media	road data	OpenStreetMap (https://openstreetmap.org)
	administrative boundary	Tianditu (https://www.tianditu.gov.cn/) and DataV.GeoAtlas platform (https://datav.aliyun.com)

, the connectivity factors that influence accessibility include administrative boundaries and road data. Administrative boundary data for Kunming’s main urban area were obtained from Tianditu and the DataV.GeoAtlas platform with precision down to the community level. These data were primarily used to define the study area and its spatial units. The road data were sourced from the OpenStreetMap (OSM) open-source mapping platform.

On the demand side, population data were derived from the Seventh National Population Census, which was officially released by the government. Community-level census data were integrated into vector datasets to enable precise calculations.

On the supply side, information on public toilets was collected using Python3.8 web scraping to extract point-of-interest (POI) data from Gaode Maps. Additionally, detailed toilet stall information was retrieved from the “Software Tour Yunnan” platform, including the total number of stalls, as well as the separate counts for male and female stalls. The data were effectively integrated into a POI dataset.

2.3. Methods

2.3.1. Research Framework

As shown in Figure 3, this study consists of the following three main parts:

• Data collection and processing. This section, detailed in the “Data Sources and Processing” chapter, covers the acquisition, screening, and standardization of the spatial and attribute data required for the study;
• Measurement of public toilet accessibility in Kunming. This section focuses on quantifying the spatial accessibility of public toilets in Kunming using the G2SFCA method and analyzing the spatiotemporal variation in service coverage under different restroom travel-time demand scenarios.
• Evaluation of service efficiency and layout optimization recommendations. Based on the accessibility results, this section identifies areas with low service efficiency and service gaps within the study area and proposes feasible optimization strategies for public toilet facility layouts to enhance the overall service equity and rationality of spatial allocation.

2.3.2. The Gaussian Two-Step Floating Catchment Area

This study focused on residential areas and public toilets in Kunming’s main urban areas. A walking speed of 60 m/min was set, and the travel time was used as the core metric for travel difficulty. It was assumed that all roads allowed two-way traffic. This study aims to construct a comprehensive transportation network dataset for a main urban area. Specifically, using residential areas as origins and public toilets as destinations, network analysis tools were employed to generate a complete origin (O-D) cost matrix. Travel time was used as an impedance factor to calculate the shortest travel-time paths from each residential point to each public toilet. The calculation principle of the G2SFCA method is shown in Figure 4, where j represents the supply point (public toilet) and i represents the demand point (residential area).

First, for each public toilet $j$ (supply point), the residential areas $d_0$ (demand points) within the specific service catchment were identified. Subsequently, a Gaussian decay function was applied to assign distance-based weights to the network paths between the supply and demand points, and to estimate the potential service population of each supply point. Finally, the service capacity of each public toilet $j$ was divided by the total demand population within its catchment to obtain the supply demand ratio, $R_j$:

$$R_j={\displaystyle \frac{S_j}{{\sum }_{i\in \left\{d_{ij}\le d_0\right\}}^k{D}_i\times G\left(d_{ij}\right)}}$$

(1)

where $d_{ij}$ represents the travel time in min between two points i and j; $d_0$ defines the service catchment of the public toilet; $G(d_{ij},d_0)$ is the Gaussian function introduced to model the time decay effect; $D_i$ denotes the demand population within the service area, effectively reflecting the resident population in the region; and $S_j$ represents the total service supply, where the number of toilet stalls at each public toilet is used as the supply indicator in this study. To avoid calculation errors caused by very small values, the supply demand ratio was multiplied by 1000, indicating the number of toilet stalls per thousand residents.

The Gaussian time decay function is defined as follows:

$$G\left(d_{ij}\right)=\left\{\begin{array}{c}{\displaystyle \frac{{\mathrm{e}\mathrm{x}\mathrm{p}}^{-\left(1/2\right)\times {\left(d_{ij}/d_0\right)}^2}-{\mathrm{e}\mathrm{x}\mathrm{p}}^{-\left(1/2\right)}}{1-{\mathrm{e}\mathrm{x}\mathrm{p}}^{-\left(1/2\right)}}},d_{ij}\le d_0\\ 0,d_{ij}>d_0\end{array}\right.$$

(2)

The second step was to search for all supply points $j$ within the distance threshold $d_0$ (i.e., the search area) for each demand point $i$, and assign weights using the Gaussian equation. Finally, the weighted ratios were summed to obtain the spatial accessibility $A_i$ of the demand point, representing accessibility to public toilet services.

$$A_i=\sum _{i\in \left\{d_{ij}\le d_0\right\}}^m{R}_j\times G\left(d_{ij}\right)$$

(3)

where $d_{ij}$ represents the time distance from $i$ the $j$ residence to the public toilet, and the meanings of the other symbols are consistent with those in Equation (1). During the calculation process, the Gaussian function from Equation (2) was applied again to introduce a distance decay within the threshold, yielding the value for the second step. The value $A_i$ is defined as the time–cost accessibility of public toilets for a residential point, reflecting the average quality of public toilet services accessible to residents in the area. A higher value indicates better accessibility to public toilets at that location, whereas a lower value signifies poorer accessibility.

3. Results

To facilitate a global and local comparison of the accessibility of public toilets in the main urban area of Kunming, we used an indicator of the number of public toilets per 1000 people (toilet stalls per 1000 people), denoted as $A_i$. The accessibility classification levels and standards are listed in

Table 2. Accessibility classification level criteria.

Accessibility Grading Criteria (Toilets/1000 Persons)
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
	0	0–5	5–10	10–15	15–20	>20

.

The changes in community accessibility levels under the 5 min, 10 min, 15 min, 20 min, and 30 min search thresholds are shown in Figure 5. As the accessibility distance threshold gradually increased, the number of inaccessible communities significantly decreased within the 30 min threshold, and no inaccessible communities remained in the study area. Meanwhile, the number of highly accessible communities increased from 1 to 19, indicating a relatively good overall accessibility level for public toilets within this range. However, this result does not fully satisfy the relevant infrastructure service standards. To further reveal the spatial distribution characteristics under different thresholds, a detailed analysis of the spatial patterns within the five accessibility threshold ranges is presented below.

3.1. Public Toilet Accessibility Analysis at a 5-min Threshold

Within the 5 min threshold range, there was only one highly accessible community and one moderately accessible community, while there were five communities with relatively low accessibility. The remaining communities were classified as having low accessibility or inaccessibility. The distribution of communities according to accessibility level is shown in

Table 3. Distribution of accessibility in communities within the 5 min living circle.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 5 min Living Circle
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
	57	46	5	1	0	1

.

In Figure 6, different accessibility levels are represented by different colors. Transparent areas indicate regions where accessibility is not available within 5 min. The distribution of public toilet accessibility evaluation results across communities within a 5 min walking time was relatively uneven. Only one community, the Yangjia Community, has high accessibility, while most communities do not have public toilets accessible within a 5 min living circle. From a citywide perspective, the majority of inaccessible areas are located on the outskirts of the center, with accessibility decreasing outward from the center. The primary reason for this, as observed from the mapping results, is the low number of public toilets in the outer areas, which are far fewer than those in the central areas. Therefore, new public toilets should be constructed along the road networks.

3.2. Public Toilet Accessibility Analysis at a 10-min Threshold

The distribution of community public toilet accessibility at the 10 min threshold is shown in

Table 4. Distribution of 10 min accessibility in communities.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 10 min Living Circle
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
	16	64	17	8	2	3

, with four communities exhibiting high accessibility. Compared with the 5 min walking distance standard, the number of inaccessible communities significantly decreased by 41.

As shown in Figure 7, overall accessibility improved across the entire area, but some peripheral communities and even some communities closer to the center still lack accessibility despite having an adequate number of public toilets. This suggests that road network connectivity is poor and should be optimized. The distribution of the layout fairness evaluation results across communities was uneven. In the eastern region, the fairness index for communities at the same level was more consistent and the overall accessibility index for the region was the highest, whereas those for the western region were more scattered. In comparison to accessibility within the 5 min threshold range, Fufa community’s public toilet accessibility improved from inaccessible to relatively highly accessible.

3.3. Public Toilet Accessibility Analysis at the 15-min Threshold

The distribution of communities by accessibility level at the 15 min threshold is presented in

Table 5. Distribution of 15 min accessibility in communities.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 15 min Living Circle
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
	8	55	21	7	8	11

. Compared with the 10 min threshold, the number of inaccessible communities decreased by eight, while the number of highly accessible communities increased by eight.

As shown in Figure 8, the number of inaccessible public toilets in the region was low under the 15 min threshold. The eastern region achieved accessibility for all communities, with accessibility levels decreasing in a regular pattern from the center to the outskirts, demonstrating high coherence. Distribution in the western region was more scattered, with inaccessible areas found only in the west of this area. From a north–south perspective, the southern area’s accessibility results were significantly better than those in the north. Both the north and south had the same number of inaccessible communities, four each, with an even distribution. Compared with accessibility under the 10 min threshold, the accessibility of public toilets in the Xiliyuan and Hongmiao communities improved from low to relatively high.

3.4. Public Toilet Accessibility Analysis at the 20 min Threshold

The distribution of communities at different accessibility levels under the 20 min threshold is shown in

Table 6. Distribution of 20 min accessibility in communities.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 20 min Living Circle
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
	2	54	20	11	7	16

. Compared with the 15 min search threshold, the number of inaccessible areas decreased by six.

As shown in Figure 9, at this stage, except for the Dianchi National Tourism Resort, public toilets remain inaccessible only to the Fuhai community, located in the southwestern corner of the area. The figure indicates that the core issue in this community is the lack of public toilets, as there are none within the area. In the northern part of the region, accessibility levels are relatively low, but exhibit good continuity, with most areas falling within the same category. In contrast, the southern region shows a more fragmented distribution of accessibility levels, yet overall accessibility is significantly better than in the north. However, accessibility in the south varies significantly, with dispersed levels lacking continuity.

3.5. Public Toilet Accessibility Analysis at the 30 min Threshold

As shown in

Table 7. Distribution of 30 min accessibility in communities.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 30 min Living Circle (Units)
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher Accessibility	High accessibility
	0	46	21	17	7	19

, under the 30 min threshold, there were no longer any communities where toilets were inaccessible, and the overall accessibility level improved in all communities.

As shown in Figure 10, on a regional scale, the highest accessibility areas are concentrated in the central region, where public toilets are abundant and the road network is dense. The accessibility level generally follows a regular decreasing pattern from the center to the periphery. However, some communities deviate from this pattern because of an excessive or insufficient number of public toilets. For instance, certain peripheral communities have unusually high accessibility values because they have an excessive number of public toilets, whereas some inner-city communities have fewer public toilets, resulting in lower expected accessibility values than their surroundings. Overall, the southern region exhibits higher accessibility levels than the northern region. Compared with the 20 min threshold, the accessibility of public toilets in the Datang Community improved from low to high.

4. Discussion

4.1. The Optimization Goal of Public Toilet Layout in the Main Urban Area of Kunming

The Kunming municipal government and relevant institutions successively launched a rural toilet reform project and a three-year action plan (2018–2020). Following the principle of “scientific planning and appropriate allocation”, they plan to build 250 new public toilets (including 15 tourist toilets) and renovate 164 existing ones (120 of which are tourist toilets) over the next three years, in addition to the current 3924 urban public toilets (including social toilets). The goal is to ensure that all renovated toilets meet at least the second-class standards. The plan aims to provide public toilets within 400 m in commercial areas, 400–600 m in residential areas, and 600–1200 m along major transportation routes, ensuring that pedestrians can find the nearest toilet within 3–5 min.

4.2. Optimization Strategy of Public Toilet Layout in the Main Urban Area of Kunming

This study systematically assesses the service coverage level and spatial disparities of public toilets in Kunming’s main urban area from a spatial accessibility perspective. However, it is necessary to further reflect on the more complex real-world constraints underlying the optimization of public toilet layouts. Therefore, this study proposes optimization strategies based on urban policies and land-use planning, focusing on both the construction of new public toilets and the expansion of outdated existing facilities.

4.2.1. Build New Public Toilets near the Road Network

To promote urban environmental civility, enhance residents’ satisfaction with the urban living environment, and ensure that pedestrians can conveniently find a toilet within 3 to 5 min, the top priority is to improve areas that currently lack accessibility within a 5 min walking distance. The fundamental reason for inaccessibility within 5 min is poor connectivity in the transportation network. The optimization approach involves constructing new public toilets along residential road networks or improving road network connectivity.

Based on the accessibility evaluation results and optimization objectives, this study proposes a priority distribution map for public toilet addition, as shown in Figure 11. A total of 57 communities were identified as priority renovation targets, primarily concentrated in the peripheral areas of the study region. Public toilets should be built along road networks in these areas. Additionally, it was observed that the farther from the city center, the greater the shortage of public toilets, as the road network in the outer areas was less connected and denser than that in the inner areas.

4.2.2. Expansion of Old Public Toilets

Therefore, a comprehensive field survey should be conducted to assess the current layout and classification of urban and rural public toilets. Through urban renewal, road expansion, and demolition of illegal or temporary structures, sufficient public toilets should be newly built, reconstructed, or renovated. In new urban districts, large-scale integrated development projects, and newly built commercial and residential areas, public toilet construction should follow the Standards for Urban Environmental Sanitation Facilities, ensuring that at least one Class II or higher public toilet is planned and constructed within every 500 m radius. This approach aims to address the shortage of public toilets in old urban areas while preventing underdevelopment in new districts, thereby avoiding difficulties in site selection and construction in later stages.

To achieve this, this study proposes increasing the number of toilet stalls in existing old public toilets by 50%, which would significantly enhance accessibility overall. The changes in accessibility before and after optimization are illustrated in Figure 12.

As shown in

Table 8. 30 min threshold for 50% expansion of public toilets compared with community data.

Distribution of Numbers of Communities with Different Levels of Accessibility Within the 30 min Living Circle
Ai	Inaccessible	Low accessibility	Lower accessibility	General accessibility	Higher accessibility	High accessibility
Before expansion	0	46	21	17	7	19
After expansion	0	12	26	17	8	47

, after the expansion, the number of high-accessibility communities increases by 28 compared with before the expansion, representing a 147.37% increase in high-accessibility communities. The number of low-accessibility areas decreases by 24, resulting in a 52.17% reduction in low-accessibility communities.

However, the allocation of public health infrastructure within urban spaces often faces physical challenges, such as limited land resources and high population density, as well as multifaceted governance issues, including interdepartmental coordination, allocation of fiscal resources, and efficient policy implementation. Therefore, further optimization efforts should comprehensively understand the influencing mechanisms of urban public health facility provision, considering both institutional management aspects and social dimensions such as public feedback.

4.3. Research Deficiencies and Future Efforts

This study relied primarily on static resident population distribution data to represent the potential demand for public toilets. However, population mobility in urban spaces exhibits significant spatiotemporal dynamics, particularly in areas with frequent tourism, commuting, and recreational activities. As a well-known tourist city in China, Kunming attracts numerous domestic and international visitors year-round. The toileting demand from this transient population during peak periods places far greater pressure on public service facilities than the average demand of the resident population.

In this context, using only the resident population data as the basis for demand tends to overlook the service needs of temporary populations, potentially leading to a systematic overestimation of public toilet accessibility. Future research should thus incorporate spatiotemporal big data that reflect dynamic population changes, such as mobile phone signaling data [30,31] and GPS trajectory data [32], to capture urban population mobility patterns and latent demand more comprehensively and objectively. This approach not only improves the accuracy of accessibility assessments but also better reflects the spatiotemporal matching between service resources and populations.

Furthermore, there is currently no unified authoritative standard for categorizing urban public toilet accessibility levels; classification mainly relies on heuristic rules or context-specific criteria. In this study, the accessibility levels were classified according to the actual conditions of the study area. However, due to the lack of a systematic theoretical foundation and widely applicable quantitative standards, such classification methods possess a degree of subjectivity and may exhibit considerable variability in applicability across different study areas or scenarios, potentially affecting the identification of inaccessible regions.

Therefore, future research should further explore more universal and scientifically grounded accessibility classification methods at both the theoretical and empirical levels, promoting the development of a unified, objective, and quantifiable service-level evaluation system for public facilities.

In accessibility modeling, travel speed is a critical parameter for measuring service ranges and time thresholds. This study uniformly adopted 60 m/min as the average walking speed of the population, which is a widely used value. However, this implicitly assumes an “average person,” neglecting individual differences in actual travel capacity. Notably, mobility and toileting needs vary significantly across demographic factors, such as gender, age, and health status. For example, elderly individuals, pregnant women, children, and people with mobility impairments may experience greater toileting pressures over the same distance.

Consequently, future research should incorporate more refined user profiling techniques to categorize populations based on individual characteristics (e.g., sex, age, and health status) and establish differentiated travel threshold parameters. This would enable an improved “dynamic” threshold the G2SFCA model for personalized public toilet accessibility assessments catering to diverse demand groups. When considering the supply capacity, toilet seat allocations can also be sex-differentiated to better reflect real-world needs. Such improvements would enhance the social equity and precision of public toilet planning and advance urban public services toward greater inclusivity and a human-centered design.

5. Conclusions

This study established a systematic evaluation framework to thoroughly investigate the accessibility of public toilets in Kunming’s central urban area under different time thresholds and to assess whether the current provision adequately meets the needs of local residents. The aim was to identify areas of supply–demand imbalance and propose optimization strategies. The results indicated significant service gaps within short travel times. In total, 57 communities had no access to any public toilets within a 5 min walking distance; even when this was extended to 15 min, 8 communities remained uncovered; however, within a 30 min walking range, all communities had access to basic public toilet services.

To improve service efficiency and spatial equity, corresponding optimization measures are proposed. Based on the 5 min service radius, renovating existing facilities and increasing toilet seat supply by 50% can effectively achieve the goal of providing rapid access within 3–5 min. This approach not only enhances toilet accessibility and service efficiency, but also more effectively improves the urban living environment, better serving residents with strong feasibility and potential for wider application.

Moreover, considering certain limitations of the current datasets, research methods, and classification standards employed in this study, future work will focus on refining data standards and exploring dynamic classification systems based on residents’ behavior data and service perceptions. Improvements to modeling approaches will also be pursued to enhance the scientific rigor and policy relevance of the evaluation outcomes.