Apparatus and method for setting living area boundary

Abstract

The present invention relates to a method for establishing a living area plan on the basis of an ideal boundary that combines a realistic boundary of a local living area and a Voronoi polygon generated on the basis of a center point of a facility, and provides an apparatus and method for setting a scientific living area boundary that offsets a boundary effect by supplementing an existing administratively convenient administrative boundary by utilizing a Voronoi diagram on the basis of a complex system-based optimal arrangement theory, thereby providing a technique for analyzing supply imbalance and network optimization arrangement, and may be used to establish a scientific and efficient supply plan to prevent unnecessary waste of resources when supplying living facilities in a living area plan, and may activate cooperation between neighboring local governments by presenting a basis capable of utilizing available resources of nearby local living areas.

Description

Device and method for establishing living area boundaries

[0001] The present invention relates to an apparatus and method for establishing living area boundaries, and more specifically, to an apparatus and method for establishing living area boundaries that offset boundary effects that may occur when geographical distribution or spatial interaction crosses the boundary in order to access facilities and utilize services when establishing a living area plan.

[0002]

[0003] As stipulated in the guidelines for establishing urban master plans, the unit for establishing living area plans has been based mainly on administrative boundaries for administrative convenience; therefore, facility supply plans are also based on administrative boundaries, which has limitations in that they may conflict with actual geographical conditions.

[0004] If living areas are planned based on administrative districts formed for administrative convenience, movement may occur between local living areas within an autonomous district or between neighboring local living areas within a district when accessibility to nearby regions is high. This leads to problems such as reduced convenience for residents and resource waste resulting from duplicated supply facilities.

[0005] In other words, it can induce a phenomenon where usage is concentrated on facilities in adjacent living areas and carries the potential to generate unbalanced supply and demand, unlike the initial plan.

[0006] Therefore, in order to establish urban living areas that constitute an efficient network structure, a scientific method is required to minimize the possibility of boundary effects caused by movement between living area boundaries.

[0007]

[0008] The present invention was devised in response to the aforementioned necessity, and aims to provide an apparatus and method for establishing living area boundaries that complement existing administratively convenient administrative boundaries and offset boundary effects by utilizing Voronoi diagrams based on complex systems-based optimal placement theory when establishing living area plans.

[0009]

[0010] A living area boundary setting device according to an embodiment of the present invention for achieving the above-mentioned purpose comprises: a processor that sets a living area boundary based on an ideal boundary formed by combining a realistic boundary of a local living area and a Voronoi polygon generated based on the center point of a facility;

[0011] It consists of including

[0012]

[0013] In addition, the processor is characterized by utilizing a Voronoi diagram, which is a technique for calculating the plane of the set of points closest to each other, as a mathematical partitioning method in which a plane is divided such that exactly one point (Voronoi cell) composed of a polygon formed by the intersection of the perpendicular bisectors of two points is included.

[0014]

[0015] And the above processor comprises a boundary generation unit that generates Voronoi polygons centered on facilities within the living area boundary and generates new boundaries by connecting the boundaries of areas where these polygons overlap on the living area boundary, and

[0016] A population density distribution calculation unit that calculates the population density distribution within a theoretical boundary by utilizing grid populations, assuming a potential behavioral pattern in which facility users tend to visit the nearest facility regardless of administrative district boundaries by the boundary generation unit mentioned above;

[0017] A scaling index calculation unit that calculates the ratio of the number of facilities to the population and the scaling index between the facility distribution and the population density in order to analyze supply imbalance and the appropriateness of the supply amount by the above-mentioned population density distribution calculation unit, and

[0018] A network structure analysis unit that, by means of the population density distribution calculation unit above, first calculates the minimum distance from the center point of an individual population grid cell within each polygon to each facility in network analysis, then selects the maximum distance value for each boundary among them, calculates the maximum walking distance to generate a visualization map of pedestrian exclusion, and calculates the sum of each population density and the minimum distance between facilities to analyze an efficient network structure, and

[0019] It is characterized by having a change amount calculation unit that calculates a change amount through the difference from administrative living area boundaries based on existing supply amounts and pedestrian exclusion criteria, in order to compare the case of establishing a supply plan using theoretical boundaries in practice with the case of administrative boundaries based on the analysis information of the above-mentioned network structure analysis unit.

[0020]

[0021] A method for establishing a living area boundary according to an embodiment of the present invention comprises: (A) a step of generating a new living area boundary by combining an existing administrative boundary with a theoretical Voronoi diagram;

[0022] (B) A step of analyzing supply imbalance and network optimization using the living area boundaries generated above; and

[0023] (C) A step of quantifying the boundary effect offset through the living area boundary generated by comparison with existing supply standards;

[0024] It consists of including

[0025]

[0026] In addition, the method is characterized by the process of generating Voronoi boundaries centered on facilities using a Voronoi polygon algorithm in step (A) above, and generating new living area boundaries by combining two boundary attributes based on the existing administrative boundary and location while maintaining that the living facility belongs within the existing administrative boundary.

[0027]

[0028] In addition, the above-mentioned generated living area boundary is characterized by being based on the assumption of a potential behavioral pattern that facility users tend to visit the facility closest to their residential location, regardless of administrative boundaries.

[0029]

[0030] In addition, the method is characterized by a process of analyzing the imbalance in facility supply using the ratio of the number of facilities to the population and the scaling index in optimal placement theory in step (B) above, first calculating the minimum distance from the center point of an individual population grid cell to each facility within each polygon of the generated living area boundary, selecting the maximum distance value for each boundary to calculate the maximum walking distance corresponding to the number of living areas, and calculating the sum of the minimum distances between each population density and facilities to analyze the efficiency of the network.

[0031]

[0032] In addition, the population in step (B) above is characterized by securing a population density distribution within the theoretically generated boundary by utilizing a grid population.

[0033]

[0034] In addition, the method is characterized by calculating the degree of boundary effect offset through the amount of change in supply according to the boundary setting compared with the existing supply standard in step (C) above.

[0035]

[0036] In addition, prior to the above step (A), the spatial scope of the boundary between reality and theory is established, wherein the boundary of reality is based on administrative boundaries, and a theoretical framework is presented to compare differences in facility supply by introducing a Voronoi diagram, which is a theoretical boundary, in order to examine the validity of administrative boundaries.

[0037]

[0038] According to the means for solving the aforementioned problem, a technique is provided to generate new boundaries for Seoul's living areas using theoretical Voronoi boundaries and to analyze supply imbalances and network optimization arrangements. As a result, this can be utilized to establish scientific and efficient supply plans to prevent unnecessary resource waste when supplying living facilities in living area planning, and can also promote cooperation between adjacent local governments by presenting a foundation for utilizing available resources in nearby living areas.

[0039]

[0040] FIG. 1 is a block diagram of a living area boundary setting device according to an embodiment of the present invention.

[0041] FIG. 2 is a flowchart illustrating a method for setting living area boundaries according to an embodiment of the present invention.

[0042] FIG. 3 is an example diagram of a new boundary generation method by combining a Voronoi polygon generated around a living facility according to an embodiment of the present invention and a living area boundary.

[0043] Figure 4 is an example diagram of living facilities arranged within the boundaries of a regional living area in Seoul.

[0044] FIG. 5 is a diagram of a Voronoi diagram divided into a set of theoretically closest points according to an embodiment of the present invention.

[0045] FIG. 6 is an example of a Voronoi diagram generated centering on living facilities according to an embodiment of the present invention.

[0046] Figure 7 is an example of a newly created boundary by combining Figures 4 and 6.

[0047] Figure 8 is a graph showing the difference in the supply amount of living facilities between a newly created boundary and an existing boundary according to an embodiment of the present invention.

[0048] FIG. 9 is a graph showing the scaling index for population density and library density based on the living area boundary according to an embodiment of the present invention.

[0049] FIG. 10 is a graph showing the scaling index for population density and library density based on a newly created boundary standard according to an embodiment of the present invention.

[0050] FIG. 11 is a graph comparing the sum of the minimum distances of a newly created boundary and an existing boundary according to an embodiment of the present invention.

[0051] FIG. 12 is a graph comparing the maximum distance between a newly created boundary and an existing boundary according to an embodiment of the present invention.

[0052] FIG. 13 is a visualization map showing the change in pedestrian-excluded areas between a newly created boundary and an existing boundary according to an embodiment of the present invention.

[0053] FIG. 14 is a visualization map showing the change in pedestrian-excluded areas between a newly created boundary and an existing boundary according to an embodiment of the present invention.

[0054]

[0055] The configuration and operation of embodiments of the present invention will be described below with reference to the attached drawings.

[0056] It should be noted that identical components in the drawings are represented by the same reference numbers and symbols whenever possible, even if they are shown on different drawings.

[0057] In the following description of the present invention, if it is determined that a detailed description of related known functions or configurations may unnecessarily obscure the essence of the present invention, such detailed description will be omitted.

[0058] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0059]

[0060] FIG. 1 is a block diagram of a living area boundary setting device according to an embodiment of the present invention.

[0061] As illustrated in FIG. 1, a living area boundary setting device (100) according to an embodiment of the present invention is configured to include a processor (110), and the processor (110) comprises a boundary generation unit (111), a population density distribution calculation unit (112), a scaling index calculation unit (113), a network structure analysis unit (114), and a change amount calculation unit (115).

[0062] The above processor (110) establishes a living area boundary based on an ideal boundary created by combining a Voronoi polygon generated based on the realistic boundary of the local living area and the center point of the facility.

[0063] The boundary generation unit (111) generates Voronoi polygons centered on facilities within the living area boundary and creates new boundaries by connecting the boundaries of the areas where these polygons overlap on the living area boundary.

[0064] The above population density distribution calculation unit (112) assumes a potential behavioral pattern in which facility users tend to visit the nearest facility regardless of the administrative district boundary by the above boundary generation unit (111), and calculates the population density distribution within the theoretical boundary by utilizing the population of the grid (e.g., 100x100m).

[0065] The above scaling index calculation unit (113) calculates the ratio of the number of facilities to the population and the scaling index between the facility distribution and the population density in order to analyze the supply imbalance and the appropriateness of the supply amount by the population density distribution calculation unit (112).

[0066] The above network structure analysis unit (114) first calculates the minimum distance from the center point of each individual population grid cell to each facility within each polygon in the network analysis by the population density distribution calculation unit (112), then selects the maximum distance value for each boundary among them, calculates the maximum walking distance to generate a visualization map of pedestrian exclusion, and calculates the sum of each population density and the minimum distance between facilities to analyze an efficient network structure.

[0067] The above change amount calculation unit (115) calculates the change amount based on the difference from the administrative living area boundary and the existing supply amount and pedestrian exclusion criteria in order to compare the case where a supply plan is established using the theoretical boundary in reality with the case of the administrative boundary based on the analysis information of the network structure analysis unit (114).

[0068]

[0069] FIG. 2 is a flowchart illustrating a method for setting living area boundaries according to an embodiment of the present invention.

[0070] The method according to an embodiment of the present invention is configured as follows.

[0071] First, establish the spatial scope of the boundary between reality and theory.

[0072] Realistic boundaries are based on administrative boundaries, utilizing, for example, the regional living area boundaries established in the case of Seoul City, “Seoul Plan 2030.”

[0073] Furthermore, to examine the validity of administrative boundaries, a theoretical boundary (Voronoi diagram) is introduced to present a theoretical framework for comparing differences in facility supply.

[0074] Second, rules for behavior patterns are assigned to each boundary.

[0075] Administrative boundaries can be defined as hypothetical behavioral patterns, assuming that residents utilize facility supply services established based on those boundaries.

[0076] In other words, it involves using services within the administrative boundaries of the residence, rather than using nearby facilities.

[0077] On the other hand, Voronoi boundaries assume potential behavioral patterns.

[0078] This assumes that the facility closest to the residential location is used, regardless of administrative boundaries.

[0079] Third, as an analysis to verify the boundary effect, the results of analyzing the actual and theoretical boundaries based on a survey of supply volume and pedestrian usage conducted by the Seoul Metropolitan Government are compared through descriptive statistics and spatial visualization.

[0080] Fourth, verify the results of behavior patterns assigned differently according to two different boundaries.

[0081] By comparing and analyzing the previously investigated real-world and theoretical results with Seoul Metropolitan Government standards to identify the extent of the differences, implications regarding the boundary effect in establishing living area boundaries are derived.

[0082]

[0083] Based on these four configurations, as illustrated in FIG. 3, a method for scientifically establishing living area boundaries using a Voronoi diagram, which is a mathematical model according to an embodiment of the present invention, includes the step of generating new living area boundaries by combining existing administrative boundaries and a theoretical Voronoi diagram (S202), the step of analyzing supply imbalance and network optimization using the generated living area boundaries (S204), and the step of quantifying the boundary effect offset through the generated living area boundaries by comparing with existing supply standards (S206).

[0084] The above-mentioned living area boundary generation step (S202) includes a process of generating Voronoi boundaries centered on facilities using a Voronoi polygon algorithm, and generating a new boundary by combining two boundary attributes based on the existing administrative boundary and location while maintaining that the living facility belongs within the existing administrative boundary.

[0085] The living area boundaries generated at this time should preferably be based on the assumption of a potential behavioral pattern in which facility users tend to visit the facility closest to their residential location, regardless of administrative boundaries.

[0086] The above supply imbalance and network optimization analysis step (S204) includes a process of analyzing the facility supply imbalance using the ratio of the number of facilities to the population and a scaling index from optimal placement theory, first calculating the minimum distance from the center point of an individual population grid cell to each facility within each polygon of the generated living area boundary, selecting the maximum distance value for each boundary among them to calculate the maximum walking distance corresponding to the number of living areas, and calculating the sum of the minimum distances between each population density and facilities to analyze the efficiency of the network.

[0087] In addition, in the above supply imbalance and network optimization analysis step (S204), it is desirable to secure a population density distribution within the theoretically generated boundary by utilizing the grid population.

[0088] Then, the aforementioned supply imbalance and network optimization analysis are performed, and the degree of boundary effect offset is calculated through the amount of supply change resulting from boundary setting compared with the existing supply standard.

[0089]

[0090] The embodiments of the present invention described above will be explained in more detail as follows.

[0091] Recently, chrono-urbanism, such as the concept of n-minute urban living zones like 15 minutes and 20 minutes, is a new paradigm of urban planning that aims for a time-centered society.

[0092] Changes in the future urban living environment caused by climate change, carbon neutrality, and the Covid-19 pandemic aim to transition to a decentralized, multi-centered urban form where people live within a short distance, thereby reducing the time and energy required to reach neighborhood facilities necessary for daily life, enhancing the efficiency of natural and social resources, and improving the quality of life.

[0093] This new urban planning paradigm departs from existing urban planning and development centered on spatial diffusion through automobile-centric outward expansion, and instead recreates urban and regional life by establishing networks based on neighborhood living spaces and transforming time into practical necessities for daily life.

[0094] In other words, efficiently organizing a network of neighborhood living facilities can be considered a key element in n-minute urban living zone planning.

[0095] However, as stipulated in the guidelines for establishing urban master plans, the unit for establishing living area plans is primarily based on administrative boundaries for administrative convenience, and facility supply plans are also based on administrative boundaries; therefore, there is a limitation in that they may conflict with actual geographical conditions.

[0096] For example, if nearby areas have high accessibility, movement may occur between local living areas within an autonomous district or between neighboring local living areas within the same district; this can reduce residents' convenience and lead to resource waste due to duplicated supply facilities.

[0097] In other words, this can lead to a concentration of usage on facilities in adjacent living areas and, contrary to the initial plan, generate unbalanced supply and demand, making it difficult to establish n-minute urban living areas through an efficient network structure.

[0098] Therefore, when establishing a living area plan, it is advisable to consider the possibility that boundary effects may occur due to movement between boundaries.

[0099] The aforementioned boundary effect refers to situations where geographical distribution or spatial interaction can occur across boundaries, such as when there are no restrictions on boundary movement for facility access and service utilization, or when resources can influence activities within other spatial units by crossing arbitrarily given administrative boundaries.

[0100] In reality, when establishing a supply plan for living SOC based on administrative boundaries for administrative convenience, it may be difficult to establish a balanced supply and demand system. This is because administrative boundaries are based on geographical and customary boundaries rather than theoretically close locations, which increases the likelihood of imbalance in actual facility accessibility.

[0101] In other words, supply planning for Living SOC based on administrative boundaries increases movement between local living areas to utilize closer facilities, and accessibility to Living SOC planned on a local living area basis leads to unclear results in terms of availability or proximity.

[0102] Optimizing facility layout to minimize the distance to the nearest facility suggests a method to enhance the clarity of supply planning and accessibility to facility use.

[0103] Public facilities such as Living SOC are essential for everyone, and geographical accessibility and equity are key aspects of supply policy. Unlike profit-seeking facilities, which are influenced by the 'number of visitors,' public facilities are characterized by the 'distance' from users being the most important variable; the former are concentrated in densely populated areas, while the latter are evenly distributed even in sparsely populated areas. Furthermore, the indiscriminate placement of public facilities may result in more than 50% higher social costs.

[0104] Therefore, the two basic conditions for satisfying network efficiency and optimization when arranging public facilities are as follows.

[0105] First, ensure that the sum of the shortest distances in the network connecting two points is not too long than the straight-line distance between the two points.

[0106] Second, to ensure economic efficiency in terms of construction and maintenance, the sum of all distances in the network is minimized.

[0107] As such, Voronoi diagrams can be utilized for the location selection of public facilities closely related to daily life for efficiency and optimization. This allows for the optimization of time-efficient facility placement by distributing facilities at locations where the sum of the average distances from each individual to the nearest facility is minimized.

[0108] This complex system-based optimal placement theory can be expressed as Equation 1, and theoretically, a scaling index between population density and facility density in Equation 2 can be derived.

[0109]

[0110] Here, F is the total distance, ρ(x) is the population density, x1, x2, … , x n is the facility location.

[0111]

[0112] Here, ρ(f) is the facility density.

[0113] This means that by generating a Voronoi diagram with the set of points closest to each facility so that the sum of the distances to the nearest facility is minimized for each individual population density distribution, theoretically the density of public facilities is calculated to have a scaling factor of 2 / 3 of the population density.

[0114]

[0115] The theoretical approach to such complex systems is based on several important characteristics.

[0116] Unlike conventional statistical analysis that treats the entire system as an average, complex systems track the behavioral patterns of individual elements; understanding these elements individually rather than viewing them as a whole is based on nonlinear dynamics, which assumes that the sum of the elements is different from the whole.

[0117] In other words, unlike conventional location analysis which primarily considers the total population at the aggregation unit level, the optimal placement algorithm enables the analysis of non-linear phenomena by applying a spatially dependent local population distribution function based on complex systems theory.

[0118] This implies that patterns represented by the average value of all elements can differ from patterns non-linearly generated by individual elements, indicating that conventional statistical analysis based on average values ​​has limitations in explaining complex systems.

[0119] The Voronoi diagram, which serves as the standard for the optimal arrangement theory of complex systems, is a diagram divided into sets of theoretically closest points as shown in Fig. 5, and consists of polygons (Voronoi forms and spaces) generated by Voronoi vertices formed by the intersection of the perpendicular bisectors of two points.

[0120] Consequently, it is a mathematical partitioning method in which a plane is divided such that exactly one point (Voronoi cell) is included, and it is a technique for calculating the plane of the set of points closest to each other.

[0121] This function enables scientific and rational zone demarcation by allowing the evaluation of distances within the same area based on living facilities, and the Voronoi technique is generally useful for nearest neighbor analysis, jurisdictional zone division, or influence range analysis.

[0122] As such, the theoretical definition of the Voronoi diagram effectively results in the formation of a boundary by the set of residents closest to a living facility in reality, thereby lowering the probability of using a facility in a nearby area under the assumption that all other conditions are equal.

[0123] Therefore, given that living area plans are established based on administrative boundaries for the sake of administrative convenience, achieving the theoretically achievable optimal layout will be difficult, and the Voronoi method can compensate for the potential discrepancy between the plan and its practical application.

[0124] Furthermore, rather than establishing fixed boundaries, the Voronoi diagram constructs new boundaries that enable optimal placement as they flexibly change according to the supply of new facilities.

[0125] This is because when a point (facility) is added, the Voronoi space generated by the perpendicular bisectors with surrounding facilities changes.

[0126] Such changes in boundaries reflect the boundary effect, as they correspond to the fact that the behavioral patterns of residents living nearby change when a new facility is established in reality.

[0127]

[0128] In the following, to verify the boundary effects of living areas established based on administrative boundaries in living area planning, the “Seoul Plan 2030” living area plan was selected and carried out as an example.

[0129] This refers to living area boundaries established to efficiently handle administrative tasks prior to the implementation of the recently revised "Guidelines for the Establishment of City and County Basic Plans," and it is far removed from the scope of living areas that consider the actual daily activities of residents, such as commuting to work and school, recreation, and shopping, in addition to administrative tasks.

[0130] The “Seoul Plan 2030” Living Area Plan was established by dividing the entire city of Seoul into 116 local living areas to support balanced regional development and the daily lives of residents.

[0131] One local living area consists of 3 to 5 administrative districts and a population of approximately 100,000, and one autonomous district consists of about 3 to 7 local living areas. In terms of spatial scope, the present invention primarily limits the target scope to 116 local living areas and sets the local living service facilities presented in the spatial management guidelines of the local living area plan as the secondary target scope.

[0132] Figure 4 is an example of a living facility arranged within the boundaries of a local living area in Seoul. In the present invention, among the seven local living service facilities (park, parking lot, library, elderly leisure and welfare facility, youth and child welfare facility, childcare facility, and public sports facility), a library facility is finally selected as an example by considering the sample size of the data and facilities that are relatively evenly distributed across the entire local living area.

[0133]

[0134] As described above, since the Voronoi diagram can be considered the most reasonable and ideal boundary in the nearest-neighbor analysis, two spatial analysis ranges were established based on the ideal boundary created by combining the realistic boundary of each regional living area divided into 116 parts as shown in Fig. 7 and the Voronoi polygon generated based on the center point of the facility.

[0135] The establishment of such spatial boundaries theoretically has the effect of rearranging facility layouts according to newly created boundaries, enabling comparative analysis of the two spaces; consequently, the supply volume and pedestrian usage of facilities included in each polygon cell were analyzed for the two different boundaries.

[0136] Specifically, to identify boundary effects, a new theoretical boundary map was generated by combining 116 local living area boundaries in Seoul with Voronoi polygons.

[0137] As illustrated in Fig. 5, the generation process first generates Voronoi polygons centered on facilities within the living area boundary and connects the boundaries of the areas where these polygons overlap on the living area boundary to form new boundaries, based on the assumption of a potential behavioral pattern that, as explained above, facility users tend to visit the nearest facility regardless of the administrative district boundary.

[0138] Figure 3 forms a new boundary by combining a Voronoi polygon generated around the library (yellow dot) facility and the boundary of the living area.

[0139] The boundary effect analysis was performed by calculating and comparing the Seoul Metropolitan Area's local living zones and the theoretically generated new boundaries, respectively.

[0140] First, the supply imbalance in basic statistics was analyzed as the ratio of the number of facilities to the population.

[0141] In addition, the appropriateness of the supply volume was analyzed from the relationship between facility distribution and population density using a scaling index based on complex systems-based optimal placement theory.

[0142] Second, for the pedestrian use analysis, the minimum distance from the center point of each individual population grid cell to each facility within each polygon was first calculated, and the maximum distance value for each boundary was selected from among them to calculate 116 maximum walking distances.

[0143] Service areas where pedestrian exclusion occurs were defined based on the standards of Seoul Plan 2030, and visualization maps were generated (e.g., libraries were set at a walking distance of 800m).

[0144] In addition, to assess the efficiency of the facility network, the sum of the minimum distances between each population density and facility was calculated, and the realistic and theoretical boundaries were compared.

[0145] In other words, the smaller the sum of the minimum distances, the more efficient the network structure the facility layout can be considered to be.

[0146] As a megacity, Seoul accounts for approximately one-fifth of the country's population and has one of the highest population densities in the world. Furthermore, because the pattern of urban development is based on a high-density urban structure of high-rise apartment complexes and the density of living facilities is also very high, there is a high possibility that the arrangement of population and facilities will be dense.

[0147] Although this distribution can accommodate the population of adjacent neighboring areas, boundary effects are ignored when viewed based on administrative boundaries, which may lead to an oversupply in supply planning.

[0148] As a result of analyzing administrative boundaries and Voronoi-based generated boundaries, it was found that the average values ​​of the number of facilities per capita for the two different realistic and ideal boundaries were similar; however, in the case of the latter, the gap between the maximum and minimum values ​​narrowed somewhat, which was confirmed to have the effect of reducing the range of supply volume by region.

[0149] The minimum number of library facilities per 10,000 people is 0.2 at the living area boundary, whereas it is 0.3 at the new Voronoi boundary.

[0150] This confirmed the effect of reducing the new supply of library facilities by reducing the boundary effect according to theoretical boundaries and encouraging the use of nearby facilities.

[0151] As can be seen in Figure 8, it can be observed that the width of the library number range in the pattern of the new Voronoi boundary has decreased somewhat.

[0152] Since the average number of library facilities in Seoul as presented in the Seoul Plan 2030 is 0.94 per 10,000 people, applying the new Voronoi boundary to local living areas with one or fewer libraries may affect supply plans in areas where the supply increases due to the use of facilities in neighboring local living areas. Similarly, in local living areas with one or more libraries per 10,000 people, there are areas where the supply of libraries decreases or increases based on the new Voronoi boundary, suggesting that more efficient supply plans can be established depending on the interrelationship with neighboring areas.

[0153] In other words, depending on how the boundaries are set, the redistribution of population distribution results in the number of library facilities relative to the regional population becoming somewhat more uniform at the new Voronoi boundaries compared to the living area boundaries.

[0154] This phenomenon suggests that even with the same total supply volume, the distribution can vary by reducing or increasing the boundary effect depending on the boundary setting; thus, when supply plans are established based on the administrative boundaries of living areas, there is a possibility that overall oversupply or resource waste may occur due to the boundary effect.

[0155] In the analysis of the scaling index, compared to the living area boundary, the boundary effect decreased at the new Voronoi boundary, and as the population distribution was redistributed, the rate of increase in library density with respect to the increase in population density decreased slightly.

[0156] As shown in Fig. 9, the scaling index at the living area boundary has a value close to 0.9, which indicates that the number of libraries increases at a similar rate relative to the increase in population, but as shown in Fig. 10, at the Voronoi boundary, the rate of increase in the number of libraries with the increase in population is 0.83, which is a slightly lower rate of increase.

[0157] These results were obtained by measuring the distribution of library users based on the shortest distance of residents near the region, without considering administrative boundaries, due to the redistribution of population distribution that reduces boundary effects.

[0158] In the case of public facilities, the index value is theoretically the most ideal value when it is close to 2 / 3, so the distribution of libraries in some areas of Seoul was analyzed to be somewhat oversupplied relative to the population.

[0159] In other words, since there are more libraries than are needed in densely populated areas, applying the new Voronoi boundaries results in a slight redistribution to neighboring areas, but the number still exceeds what is required. This suggests that if supply is determined based on area boundaries without considering boundary effects, there is a high possibility of oversupply.

[0160] In network analysis, the shorter the shortest distance and the smaller the sum of the shortest distances, the more the network can satisfy efficiency and optimization conditions.

[0161] In a comparison of descriptive statistics between realistic local living area boundaries and ideal new Voronoi boundaries, the minimum distance value decreased on average in the latter case compared to the former, due to the boundary effect of utilizing nearby facilities without relying on administrative boundaries.

[0162] In addition, the range of the total statistics decreased by nearly 200m as the minimum value remained the same but the maximum value decreased, and the new Voronoi boundary was significantly reduced compared to the living area boundary as a result of the interaction with the boundary effect of the nearby area in the sum of the minimum distances.

[0163] In other words, the sum of the distances from each individual's location to the nearest facility within a region was shortened by an average of more than 8.5 km, and as shown in Figure 11, the deviation of the sum of the minimum distances in each living area decreased significantly from the ideal boundary, confirming that regional disparities can be reduced and network efficiency improved.

[0164] Finally, looking at the maximum values ​​among the shortest distances within each area, the average value increased by about 35m, but the minimum and maximum values ​​decreased, so the range of the overall statistics decreased by about 1.5km.

[0165] As shown in Fig. 12, the deviation of the maximum distance value was larger in the case of the living area boundary, and when viewed based on the new Voronoi boundary, it was confirmed that the overall maximum distance value was adjusted within a similar range due to the boundary effect.

[0166] In other words, utilizing ideal boundaries reduces the likelihood of boundary effects and effectively redistributes the overall population distribution and library locations, thereby forming a more uniform distribution that enhances network efficiency and increases the potential for optimization.

[0167] Figures 13 and 14 show areas where pedestrian exclusion occurs, based on the Seoul Metropolitan Government setting the distance of pedestrian exclusion for libraries at 800m or more.

[0168] When overlaying the Living Area layer and the New Voronoi layer, if the Living Area (blue) layer is on top, layers where pedestrian exclusion occurs in the New Voronoi (green) do not appear, whereas if the New Voronoi layer is on top, parts of the Living Area layer appear.

[0169] From this, it can be seen that compared to the living area boundaries, when using the new Voronoi boundaries as the standard, the pedestrian-excluded areas decrease by the amount of the blue section.

[0170] Therefore, if planning is based on the new Voronoi boundaries rather than living area boundaries, the boundary effect is resolved and areas excluded from pedestrian access are reduced, thereby inducing a reduction in the supply volume of library facilities in the supply plan.

[0171] Furthermore, when planning the placement of additional facilities, it can also assist in selecting a more appropriate location closer to the green areas than the blue ones.

[0172] In this way, the supply volume and layout plan of living service facilities can be more efficiently optimized by utilizing ideal Voronoi boundaries that can reduce boundary effects based on complex systems-based optimal layout theory.

[0173]

[0174] Based on the analysis of the changes in facility distribution and walking distance mentioned above, a comparison was made between cases where supply plans are established using theoretical boundaries and cases where administrative boundaries are used.

[0175] First, when measuring the supply amount by each living area boundary, the change was compared with the average supply amount in Seoul, which is 0.94 per 10,000 people, and second, the difference in the additional supply plan amount per 10,000 people was compared with the criterion of 800m or more, which is used as a pedestrian-excluded area in the Seoul Living Area Plan.

[0176] It was found that when Voronoi boundaries, to which the minimum distance behavior pattern rule is applied, are used as the standard for establishing living areas compared to the administrative boundaries currently used as the standard for establishing local living areas, the number of areas satisfying above the Seoul average increases by 5.

[0177] Furthermore, it was confirmed that the number of facilities requiring additional supply in areas lacking pedestrian access decreased significantly; by utilizing libraries located in nearby living areas when using Voronoi boundaries as the standard instead of living area boundaries, the supply plan could be reduced by nearly half (a decrease of 17.6 areas).

[0178] These results suggest that establishing supply plans based on existing administrative boundaries can lead to unnecessary waste of resources.

[0179]

[0180] As described above, the present invention is a mathematical partitioning method in which a plane is divided such that exactly one point (Voronoi cell) composed of a polygon formed by the intersection of the perpendicular bisectors of two points is included. By utilizing the Voronoi diagram, which serves as a standard for the optimal placement theory of complex systems, as a technique for calculating the plane of the set of points closest to each other, it is possible to evaluate the distance within the same zone centered on living facilities by partitioning into the set of theoretically closest points. Furthermore, it possesses the function of enabling scientific and rational zone setting, thereby improving the inefficiency of supply that occurs during living zone planning based on administrative boundaries for the sake of existing administrative convenience.

[0181]

[0182] Although the technical concept of the present invention has been described above together with the accompanying drawings, this is merely an illustrative description of preferred embodiments of the present invention and is not intended to limit the invention.

[0183] Furthermore, it is evident that anyone with ordinary knowledge in this technical field can make various modifications and imitations within the scope of the technical concept of the present invention without departing from it.

Claims

1. A processor that establishes a living area boundary based on an ideal boundary formed by combining a Voronoi polygon generated based on the realistic boundary of the local living area and the center point of a facility; A living area boundary setting device including 2. In Paragraph 1, A living area boundary setting device characterized by utilizing a Voronoi diagram, which is a technique for calculating the set plane of points closest in distance, as a mathematical partitioning method in which a plane is divided such that exactly one point (Voronoi cell) composed of a polygon formed by the intersection of the perpendicular bisectors of two points is included.

3. In Paragraph 1, The above processor comprises a boundary generation unit that generates Voronoi polygons centered on facilities within the living area boundary and generates new boundaries by connecting the boundaries of areas where these polygons overlap on the living area boundary, and A population density distribution calculation unit that assumes a potential behavioral pattern in which facility users tend to visit the nearest facility regardless of administrative district boundaries by the above boundary generation unit, and calculates the population density distribution within the theoretical boundary using grid population numbers, and A scaling index calculation unit that calculates the ratio of the number of facilities to the population and the scaling index between the facility distribution and the population density in order to analyze supply imbalance and the appropriateness of the supply amount by the above-mentioned population density distribution calculation unit, and A network structure analysis unit that, by means of the population density distribution calculation unit above, first calculates the minimum distance from the center point of an individual population grid cell within each polygon to each facility in network analysis, then selects the maximum distance value for each boundary among them, calculates the maximum walking distance to generate a visualization map of pedestrian exclusion, and calculates the sum of each population density and the minimum distance between facilities to analyze an efficient network structure, and A living area boundary setting device characterized by having a change amount calculation unit that calculates a change amount through the difference from the administrative living area boundary based on existing supply volume and pedestrian use exclusion criteria, in order to compare the case of establishing a supply plan using theoretical boundaries based on analysis information from the above-mentioned network structure analysis unit with the case of administrative boundaries. 4.(A) A step of generating new living area boundaries by combining existing administrative boundaries and theoretical Voronoi diagrams; (B) A step of analyzing supply imbalance and network optimization using the living area boundaries generated above; and (C) A step of quantifying the boundary effect offset through the living area boundary generated by comparison with existing supply standards; A method for establishing living area boundaries including 5. In Paragraph 4, A method for establishing a living area boundary characterized by the process of generating a Voronoi boundary centered on a facility using a Voronoi polygon algorithm in step (A) above, and generating a new living area boundary while maintaining the living facility belonging within the existing administrative boundary by combining two boundary attributes based on the location of the existing administrative boundary.

6. In Paragraph 5, A method for establishing a living area boundary, characterized by the assumption that the above-mentioned living area boundary is based on a potential behavioral pattern in which facility users tend to visit the facility closest to their residential location, regardless of administrative boundaries.

7. In Paragraph 4, A method for establishing living area boundaries characterized by the following steps: analyzing the imbalance in facility supply using the ratio of the number of facilities to the population and the scaling index in optimal placement theory in step (B) above; first calculating the minimum distance from the center point of each individual population grid cell to each facility within each polygon of the generated living area boundary, selecting the maximum distance value for each boundary to calculate the maximum walking distance corresponding to the number of living areas, and calculating the sum of the minimum distances between each population density and facilities to analyze the efficiency of the network.

8. In Paragraph 7, A method for establishing a living area boundary characterized by securing a population density distribution within a theoretically generated boundary using grid population in step (B) above.

9. In Paragraph 4, A method for setting living area boundaries characterized by calculating the degree of boundary effect offset through the amount of change in supply according to boundary setting compared with existing supply standards in step (C) above.

10. In Paragraph 4, A method for establishing living area boundaries, characterized by establishing a spatial range of the boundary between reality and theory prior to the above step (A), wherein the above-mentioned boundary of reality is based on an administrative boundary, and a theoretical framework is presented to compare differences in facility supply by introducing a Voronoi diagram, which is a theoretical boundary, in order to examine the validity of the administrative boundary.

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