A method and system for health impact assessment applied to urban planning schemes

Abstract

A health impact assessment method and system applied to urban planning schemes, the assessment method comprising the steps of: (1) based on the spatial data and attribute data of the present situation and the urban planning scheme; (2) calculating the PM2.5 change (Delta Air) and the physical activity change (Delta PA) of the present situation and the urban planning scheme scenario relative to the present situation; (3) calculating the disease incidence / death number change (H) caused by the urban planning scheme; (4) whether the planning scheme optimization is needed is judged by the evaluation personnel according to the output of step 3. The system comprises an index calculation module, a PM2.5 / physical activity change calculation module, a health result calculation module, a planning scheme optimization module and the like. Based on the evaluation path of "built environment-health risk / health behavior-health result", a complete quantitative health impact assessment method is constructed, which can face the urban planning scheme, calculate the health result change of the urban planning scheme relative to the present situation, and support the planning decision and the urban planning scheme optimization.

Description

Technical Field

[0001] This invention pertains to the field of urban planning, specifically a method and system for simulating and predicting health outcomes for urban planning schemes. Background Technology

[0002] Urban planning schemes serve as blueprints for urban development. During the planning phase, it's crucial to consider and quantitatively predict the potential health impacts on urban residents after implementation. This proactive approach helps identify the health risks associated with urban planning schemes, thereby mitigating adverse health effects on residents after the schemes are completed.

[0003] Urban planning can influence health outcomes by affecting health risk exposures (such as air, water, and soil pollution) and health behaviors (such as physical activity and exercise), which is the "urban planning – health risk exposure / health behavior – health outcome" path. Existing health impact prediction schemes generally follow this path, but can be further divided into two categories depending on the specific prediction stage.

[0004] The first type of approach directly addresses the predictive relationship between urban planning and health outcomes. This type of approach typically uses regional health statistics and built-up environment data, employing traditional statistical regression models or machine learning models to construct statistical regression and predictive relationships between health outcomes and the built-up environment, thus serving as a health impact assessment tool. The "National Public Health Assessment Model (N-PHAM)" is a representative example of this type of health impact assessment tool. This tool is based on statistical models of the US natural and built-up environment and their relationship to health behaviors and outcomes, deriving significantly influential built-up environment / natural environment indicators and their impact effects as built-in parameters. Then, using spatial data as input, scenario planning software is used to analyze and obtain built-up environment / natural environment characteristic indicators such as street intersection density, open space coverage, and forest cover. Based on the aforementioned built-in parameters, output results such as commuting walk, leisure walk, commuting cycling, motor vehicle travel, body mass index (BMI), health status, and mental health are obtained.

[0005] Another type of approach focuses on addressing the predictive relationship between "health risk exposure / health behavior – health outcomes." This type of approach typically relies on substantial evidence from the medical field regarding the link between health risks / health behaviors and health outcomes. Based on a "comparative risk framework," it constructs a predictive relationship from health risks / health behaviors to health outcomes, thus serving as a health impact assessment tool. A representative example of this type of health impact assessment tool is the "Urban and Transit Planning Health Impact Assessment (UTOPHIA) tool." This tool compares recommended and current levels of health risk / health behavior exposure to obtain the difference. Then, using exposure response functions (ERF), it calculates the relative risk (RR) and population attributable fraction (PAF) at the level of exposure difference, thus determining the preventable morbidity / mortality.

[0006] The second type of health impact assessment tools mentioned above mainly focus on the relationship between health risks / health behaviors and health outcomes, and do not involve built environment elements. Therefore, they are not suitable for urban planning schemes and cannot be directly used to assess and predict the health impacts of urban planning schemes. Summary of the Invention

[0007] To address the numerous shortcomings of existing health impact assessment tools, this invention discloses a health impact assessment method and system for urban planning schemes. It innovatively solves the bottleneck problem of quantitatively linking "urban planning - health risk exposure / health behavior", thereby realizing the prediction of the health impact of urban planning schemes throughout the entire process. It overcomes the deficiencies of the two types of existing tools mentioned above and has the advantages of good adaptability to urban planning schemes, localizable parameters, and simple and efficient calculation process.

[0008] By constructing an application system, it is possible to achieve end-to-end prediction from urban planning schemes to final health outcomes. This system can quantitatively predict the health impacts of urban construction implemented according to urban planning schemes, specifically changes in the incidence / mortality rates of specific diseases in the population. The prediction results can be fed back to the urban planning scheme development unit for comparison and optimization of planning schemes, reducing the adverse health impacts of urban construction on urban residents from the planning and design stage.

[0009] The theoretical principle, or reasoning process, of the technical solution of this invention in the first part is as follows:

[0010] Changes in health outcomes caused by urban planning schemes are represented by the change in the number of disease morbidity / mortality, H, calculated using the following formula:

[0011] (1)

[0012] Among them, H j The change in the number of morbidities / mortities caused by disease j can be calculated using the following formula:

[0013] (2)

[0014] Among them, H j ′ represents the current number of cases caused by disease j, PAF j This represents the percentage change in the incidence of disease j resulting from the urban planning scheme. Further, we have:

[0015] (3)

[0016] Among them, PAF j (ΔAir) and PAF j (ΔPA) represents the percentage change in the incidence of disease j caused by the urban planning scheme's influence on PM2.5 and physical activity levels, respectively. Further, we have:

[0017] (4)

[0018] (5)

[0019] Among them, RR j (ΔAir) and RR j (ΔPA) represents the relative risk of disease j when the changes in PM2.5 and physical activity are ΔAir and ΔPA, respectively. That is, the PM2.5 in ΔAir and the physical activity in ΔPA will make the incidence of disease j a multiple of its original value.

[0020] further:

[0021] (6)

[0022] (7)

[0023] Where ΔAir and ΔPA represent the changes in PM2.5 and physical activity levels resulting from urban planning schemes, respectively; RR j (Air) and RR j (PA) represent the changes in PM2.5 and physical activity, respectively. j (Air) and E j The relative risk of disease j at (PA).

[0024] The formulas for calculating ΔAir and ΔPA are as follows:

[0025] (8)

[0026] (9)

[0027] Among them, X i and X i ′ represent the values ​​of indicator i in the urban planning scheme and the current situation, respectively, and C i and W i P represents the change in car trip volume and walking time corresponding to a 1% change in indicator i, respectively; c The value represents the proportion of PM2.5 generated by car traffic; Air′ represents the current PM2.5 concentration; Walk′ represents the current walking time; PA w This represents the amount of physical activity corresponding to a unit of walking time.

[0028] Part Two: Technical Solution of the Invention

[0029] Technical Solution 1

[0030] A health impact assessment method applied to urban planning schemes, characterized by comprising the following steps:

[0031] (1) Based on the spatial and attribute data of the current situation and urban planning scheme, vectorized indicators of the drawings are extracted, and the numerical values ​​X of indicator i in the urban planning scheme are calculated respectively. i The numerical value X of the current situation i ′;

[0032] (2) Calculate the change in PM2.5 (ΔAir) and the change in physical activity (ΔPA) under the urban planning scenario relative to the current situation;

[0033] (3) Based on the PM2.5 change ΔAir and physical activity change ΔPA obtained in step (2) relative to the current situation, calculate the change in the number of disease incidence / deaths caused by the urban planning scheme (H);

[0034] (4) The evaluator determines whether the planning scheme needs to be optimized based on the output of step 3. If optimization is required, corresponding suggestions are provided. The planner adjusts and optimizes the planning scheme based on the suggestions. The optimized planning scheme is provided to step 1 and enters a loop iteration until the health effect of the planning scheme reaches the expected level.

[0035] Technical Solution Two

[0036] Based on the above methodology, a health impact assessment system for urban planning schemes is developed, including an indicator calculation module. Based on spatial and attribute data from the current situation and the urban planning scheme, the system extracts vectorized indicators from the drawings and calculates the numerical value X of indicator i within the urban planning scheme. i The numerical value X of the current situationi ′;

[0037] The PM2.5 / Physical Activity Change Calculation Module, connected to the output of the Index Calculation Module, is used to calculate the PM2.5 change (ΔAir) and physical activity change (ΔPA) relative to the current situation and urban planning scenarios.

[0038] The health outcome calculation module connects to the output of the PM2.5 / physical activity change calculation module and is used to calculate the change (H) in the number of disease incidence / mortality caused by urban planning schemes.

[0039] The planning scheme optimization module connects to the output of the health outcome calculation module to determine whether the planning scheme needs to be optimized. If optimization is required, it provides planning optimization suggestions. After the planner implements the planning scheme, it is resubmitted to the indicator calculation module for iteration until the health effect of the planning scheme reaches the expected level.

[0040] The advantages of this invention are mainly reflected in:

[0041] 1) A complete quantitative health impact assessment method was constructed based on the assessment path of "built environment - health risk / health behavior - health outcome". It can be used to calculate the changes in health outcomes of urban planning schemes relative to the current situation, so as to support planning decisions and optimization of urban planning schemes.

[0042] 2) As an example, the parameters used to assess health outcomes have been localized; the selected urban planning indicators are consistent with the urban planning scheme and the actual spatial situation, which can conveniently and effectively assess the health benefits of the urban planning scheme. Attached Figure Description

[0043] Figure 1 The method steps of the present invention

[0044] Figure 2 This is a flowchart illustrating step 1 of the "index calculation" method in the present invention.

[0045] Figure 3 Drawings illustrating the calculation process of current status indicators for the implementation case.

[0046] Figure 4 Drawings illustrating the process of calculating indicators for the implementation of the case study planning scheme. Detailed Implementation

[0047] like Figure 1 As shown, a health impact assessment method applied to urban planning schemes includes four steps:

[0048] Step 1: Based on the spatial and attribute data of the current situation and urban planning scheme, extract vectorized indicators from the drawings and calculate the value X of indicator i in the urban planning scheme.i The numerical value X of the current situation i ′;

[0049] Step 2: Calculate the changes in PM2.5 (ΔAir) and physical activity (ΔPA) relative to the current situation and urban planning scenarios.

[0050] Step 3: Based on the PM2.5 change ΔAir and physical activity change ΔPA obtained in Step 2 relative to the current situation, calculate the change in the number of disease incidence / mortality caused by the urban planning scheme (H);

[0051] Step 4: The evaluator determines whether the planning scheme needs to be optimized based on the output of Step 3. If optimization is required, corresponding suggestions are provided. The planner adjusts and optimizes the planning scheme based on the suggestions. The optimized planning scheme is then provided to Step 1, and the process begins to iterate until the health impact of the planning scheme reaches the expected level.

[0052] Further specifying, the calculation of the value X of urban planning scheme index i in the urban planning scheme in step 1 is... i The numerical value X of the current situation i ′, where indicator i is any one of the following 9 indicators.

[0053] Based on current and planned spatial data and attribute data, calculate the value X of index i in the urban planning scheme. i The numerical value X of the current situation i A total of nine indicators need to be calculated, including: 1) Population density (people / km²) 2 ), 2) Employment density (people / km) 2 3) Commercial floor area ratio, 4) Land use mix, 5) Road network density (km / km) 2 6) Percentage of four-way intersections (%) 7) Public transport accessibility to workplace 8) Distance to shops (m) 9) Distance to the nearest public transport stop (m)

[0054] The design of the indicator system and the calculation process of each indicator are as follows: Figure 2 As shown, the specific calculation method is as follows (the following methods are used to calculate the value X of index i in the urban planning scheme and the current situation respectively). i and X i ′):

[0055] S1.1 Population density (people / km) 2 ): Total population within the planning area divided by the total land area;

[0056] S1.2 Employment density (persons / km) 2 ):

[0057] S1.2.1 Extract work sites (including industrial and warehousing land, public management and public service facilities land, and commercial service land) based on the current situation and urban planning scheme land use data, and calculate the land area of ​​each type of work site (the building area of ​​public management and public service facilities land and commercial service land needs to be calculated);

[0058] S1.2.2 Based on the empirical values ​​of the employment population capacity corresponding to various work locations (Table 1), the total number of employed people is calculated using formula (10);

[0059] S1.2.3 The employment density is obtained by dividing the total number of employed people by the total area of ​​the planned area.

[0060] The methods for calculating building area differ between urban planning schemes and current scenarios. In urban planning schemes, building area is obtained by multiplying the land area by the floor area ratio (FAR), which is directly extracted from the land use plan. In current scenarios, building area calculation requires overlaying building data (including the number of floors), and is obtained by multiplying the building footprint area by the number of floors and summing the results.

[0061] POP employment =∑LU×POP employment_unit (10)

[0062] Among them, POP employment LU represents the total number of employed people, and LU represents the land area or building area of ​​the workplace.

[0063] POP employment_unit This refers to the empirical value of the employment population capacity corresponding to the land area or building area of ​​various workplace units.

[0064] Table 1. Empirical values ​​of employment capacity corresponding to various work locations.

[0065]

[0066] S1.3 Commercial plot ratio: The commercial plot ratio of the urban planning scheme is obtained by extracting commercial land from land use data and taking the average plot ratio; the existing commercial plot ratio needs to be superimposed with building data (including the number of floors), and the plot ratio of each commercial plot is calculated by formula (11) and the average is taken.

[0067] (11)

[0068] Among them, FAR c The current floor area ratio, area BB The area represents the current building footprint, floor represents the number of building floors, and area represents the total building footprint. LU This refers to the land area.

[0069] S1.4 Land Use Mixing: Calculate the mean of Shannon's diversity index (SHDI).

[0070] S1.4.1 Merge land use patches (excluding roads) to obtain block data, calculate the area of ​​each block, and spatially connect it with the land use data;

[0071] S1.4.2 Calculate the Shannon diversity index for each block according to formula (3) and take the average value.

[0072] SHDI=-∑P i lnP i (3)

[0073] Among them, SHDI is the Shannon Diversity Index of the neighborhood, P i It is the ratio of the area of ​​the block to the area of ​​the i-th type of land use.

[0074] S1.5 road network density (km / km) 2 ):

[0075] S1.5.1 The total road length is obtained using computational geometry and statistical tools;

[0076] S1.5.2 The road network density is obtained by dividing the total length of the road by the total area of ​​the planned area.

[0077] S1.6 Percentage of four-way intersections:

[0078] S1.6.1 Create a new network dataset based on road network data and generate intersection point data;

[0079] S1.6.2 Spatially connects road line features and point features, and filters intersecting nodes (the join_count field in the attribute table is greater than 2);

[0080] S1.6.3 Calculate the percentage of four-way intersections (join_count equals 4).

[0081] S1.7 Public transportation accessibility to workplace: Calculated as the percentage of workplaces within 800m of public transportation stations.

[0082] S1.7.1 Convert the areal work site data extracted from land use data into point data;

[0083] S1.7.2 Using network analysis tools, with public transportation stops as facility points and workplaces as event points, solve for the shortest path from workplaces to the nearest public transportation stop;

[0084] S1.7.3 Calculate the proportion of paths with a length of 800 meters or less to the total number of paths;

[0085] Distance from S1.8 to the store:

[0086] S1.8.1 Convert the area-based commercial and service land data extracted from the land use data into point data, and at the same time extract the residential land data and convert it into point data;

[0087] S1.8.2 Using network analysis tools, with commercial service locations as facility points and residential locations as event points, solve for the shortest path from residential land to the nearest commercial service location;

[0088] S1.8.3 Calculate the average path length.

[0089] Distance from S1.9 to the nearest public transport stop:

[0090] S1.9.1 Based on residential location data and public transportation station data, network analysis tools are used to solve for the shortest path from residential land to the nearest public transportation station, with public transportation stations as facility points and residential locations as event points.

[0091] S1.9.2 Calculate the average path length.

[0092] Further specifying, step (2) calculates the change in PM2.5 (ΔAir) and the change in physical activity (ΔPA) relative to the current situation and urban planning scheme scenarios.

[0093] Based on the values ​​X of each indicator in the urban planning scheme i The numerical value X of the current situation i Using formulas (8) and (9), the changes in PM2.5 (ΔAir) and physical activity (ΔPA) resulting from the urban planning scheme are calculated. Among them, parameter C... i and W i The values ​​are shown in Table 2; P c Data can be obtained through on-site collection, with 30% of the empirical value used; Air′ represents the current PM2.5 concentration, which can be obtained through on-site collection; Walk′ represents the current walking time, which can be obtained through on-site collection; PA w This represents the amount of physical activity corresponding to a unit of walking time; the empirical value is 4.

[0094] Table 2. Changes in car trips and walking time corresponding to urban planning indicators.

[0095]

[0096] This invention provides a method for calculating air pollution and physical activity based on urban planning indicators by constructing formulas (8) and (9).

[0097] Further specifying, step (3) calculates the changes in the number of disease morbidity / mortality caused by the urban planning scheme.

[0098] First, calculate RR. j (ΔAir) and RR j (ΔPA)

[0099] Based on the changes in PM2.5 (ΔAir) and physical activity (ΔPA) resulting from urban planning schemes, the relative risk of disease j, i.e., RR, is calculated using formulas (6) and (7) when the changes in PM2.5 and physical activity are ΔAir and ΔPA, respectively. j (ΔAir) and RR j (ΔPA). Where RR j (Air), RR j (PA), E j (Air) and E j (PA) is a parameter that can be adjusted according to different countries and regions. The localized values ​​are shown in Table 3.

[0100] Table 3. Exposure-Response Function of Air Pollution, Physical Activity, and Health Outcomes

[0101]

[0102] Further refine the calculation of PAF j (ΔAir) and PAF j (ΔPA)

[0103] Based on RR j (ΔAir) and RR j (ΔPA), using formulas (4) and (5), calculate the percentage change in the number of people suffering from disease j caused by the urban planning scheme's influence on PM2.5 and physical activity level, i.e., PAF. j (ΔAir) and PAF j (ΔPA).

[0104] Further specifying, the calculation focuses on changes in disease incidence / mortality caused by urban planning schemes.

[0105] Based on PAF j (ΔAir) and PAF j (ΔPA), using formulas (1), (2) and (3) to calculate the change in the number of disease incidence / deaths caused by urban planning schemes.

[0106] Further specifying, step (4) is the optimization of the urban planning scheme.

[0107] Based on the health outcome calculations, it is determined whether the expected goals have been achieved and whether further optimization of the urban planning scheme is needed. If optimization is required, corresponding planning optimization suggestions are proposed based on the direction and magnitude of the health impact of the planning indicators, the adjustable space of the scheme, and the cost-benefit ratio. For example, if the health outcomes obtained from the assessment do not meet the expected goals and further reduction of air pollutant concentration is needed, then the urban planning scheme will be further adjusted to reduce car travel. First, based on the direction of the impact of the planning indicators on car travel, the planning indicators to be considered for optimization are determined, namely, population density, land use mix, road network density, percentage of four-way intersections, accessibility to public transportation to the workplace, and improvement of distance to the nearest bus stop. Second, based on the magnitude of the impact coefficients, the priority of adjusting the planning indicators is determined, in the following order: road network density, percentage of four-way intersections, land use mix, accessibility to public transportation to the workplace, distance to the nearest bus stop, and population density. Then, based on the priority, the adjustable space and cost-benefit ratio of these planning indicators within the planning area are considered, such as whether there is room to further increase road network density, and whether this process will cause economic and social challenges such as large-scale demolition. Finally, the adjusted urban planning scheme is determined, and the health benefits of the optimized scheme are simulated and predicted again in steps (1)-(3). If the expected goal is achieved, the optimized scheme is determined; if the goal is still not achieved, further optimization is carried out until the health results of the urban planning scheme can achieve the expected goal.

[0108] Implementation Cases

[0109] A specific region was selected as a case study. Using a traffic mode selection model, the changes in the proportion of car and walking trips brought about by the planning and the changes in the existing built environment were calculated, as well as the resulting changes in PM2.5 and the incidence of physical activity-related diseases. The prediction and evaluation system of this invention was then used to output optimization suggestions for urban planning schemes.

[0110] The quantitative health impact assessment system for urban planning schemes was applied to the above case studies.

[0111] The indicator calculation module is used to calculate the initial urban planning scheme indicators (X). i ) and current status indicators (X) i ′)

[0112] Focusing on air pollution and physical activity related to transportation, the index values ​​for urban planning schemes and current scenarios are obtained based on the output of the index calculation module (Table 4).

[0113] Table 4. Percentage Changes in Indicators and Travel Modes in Case Study Areas

[0114]

[0115] The PM2.5 / Physical Activity Change Calculation Module is used to calculate the change in PM2.5 (ΔAir) and the change in physical activity (ΔPA) under different urban planning scenarios relative to the current situation.

[0116] Based on the relationship between the indicators in Table 1 and the percentage changes in car and walking, the combined impact of multiple indicators was calculated. The results show that the urban planning scheme will, relative to the current situation, promote a total of 44.13% increase in walking and reduce car use by 22.65% (Table 4).

[0117] Based on the percentage change in travel volume, we can obtain the percentage change in traffic-related PM2.5 concentration and physical activity equivalent, combined with the current pollutant concentration (annual average PM2.5 concentration of 35 ug / m³). 3 Of this, private car traffic contributes 26% of PM2.5. Based on the physical activity equivalent (average commuting walking time of 17.38 minutes per person per day, or 486.64 METmin / week), the corresponding change is calculated: the planned change in private car travel (-22.65%) could lead to a reduction of 2.06 ug / m³ in traffic-related PM2.5 concentration. 3 The change in walking volume brought about by the plan (44.13%) corresponds to an increase in physical activity equivalent of 214.75 METmin / week, which is equivalent to an increase of 7.7 minutes of walking per day.

[0118] The health outcome calculation module is used to calculate the changes in the number of disease incidence / mortality caused by urban planning schemes.

[0119] First, calculate RR. j (ΔAir) and RR j (ΔPA)

[0120] Taking cardiovascular disease as an example, regarding air pollution, the exposure response function between PM2.5 and the incidence of cardiovascular disease is: RR = 1.11, where the unit of exposure is 10 ug / m³. 3 That is, for every 10ug / m³ decrease in PM2.5 3 The individual's risk of developing cardiovascular disease is reduced by 11%. The relative risk at the exposure difference level is:

[0121] RR exposure difference =exp(((ln(1.11)) / 10) (2.06))=1.022

[0122] This means that for every 2.06 ug / m³ decrease in PM2.5 concentration... 3 The risk of developing cardiovascular disease in individuals is reduced by 2.2%.

[0123] Regarding physical activity, the physical activity equivalent and cardiovascular disease exposure response function is RR = 1.091, with exposure measured in units of 600 MET min / week. This means that for every 600 MET min / week increase in physical activity, the individual's risk of developing cardiovascular disease decreases by 9.1%. The relative risk at the exposure difference level is:

[0124] RR exposure difference =exp(((ln(1.091)) / 600) (214.75))=1.032

[0125] This means that for every 214.75 METmin / week increase in physical activity level, the individual's risk of developing cardiovascular disease decreases by 3.2%.

[0126] Further, calculate PAF j (ΔAir) and PAF j (ΔPA)

[0127] Regarding air pollution, based on the population attribution score calculation formula, the reduction in cardiovascular disease incidence rate attributable to increased physical activity is:

[0128] PAF=(1.022-1) / 1.022=2.15%

[0129] That is, compared to the current situation, the physical activity promoted by urban planning schemes can prevent 3.1% of cardiovascular diseases in the population.

[0130] Regarding physical activity, based on the population attribution score calculation formula, the reduction in cardiovascular disease incidence attributable to increased physical activity is:

[0131] PAF=(1.032-1) / 1.032=3.1%

[0132] That is, compared to the current situation, the physical activity promoted by urban planning schemes can prevent 3.1% of cardiovascular diseases in the population.

[0133] Taking into account the above two factors, the urban planning scheme for this region will reduce the incidence of cardiovascular disease by 5.25% compared to the current situation.

[0134] Furthermore, the changes in disease incidence / mortality caused by urban planning schemes were calculated.

[0135] Based on current cardiovascular disease incidence data, the cardiovascular disease incidence rate under different urban planning scenarios can be calculated and output. In this case, no relevant data was available, therefore no calculation was performed.

[0136] The urban planning optimization module is used to output optimized urban planning schemes.

[0137] To determine whether the urban planning scheme requires further optimization and adjustment, this case study compares air pollution and physical activity levels under the planning scenario with standards. Regarding air pollution, after the implementation of the plan, the annual average PM2.5 concentration in the area decreased to 32.94 ug / m³. 3 Therefore, to further improve the health benefits of urban planning schemes, the focus should be on optimizing indicators related to car travel, which is linked to air pollution. This could include increasing land use mix, road network density, and the percentage of four-way intersections, as well as concentrating industrial land around public transportation hubs. During the adjustment process, implementation costs and social equity must be comprehensively considered to ensure the feasibility of the scheme. For example, denser road networks may require urban renewal in existing areas, creating a financial burden; increasing industrial land around transportation hubs may lead to a reduction in residential land and social equity issues. Ultimately, an optimized urban planning scheme should be developed based on these considerations to achieve the goals of reducing disease incidence and promoting public health.

Claims

1. A health impact assessment method applied to urban planning schemes, characterized in that, Including the following steps: (1) Based on the spatial and attribute data of the current situation and urban planning scheme, the value X of index i in the urban planning scheme is calculated by extracting vectorized indicators from the drawings. i The numerical value X of the current situation i ′, where indicator i is any one of the following nine indicators, including: 1) Population density, unit: people / km 2 2) Employment density, unit: people / km 2 3) Commercial floor area ratio 4) Land use mix 5) Road network density, unit: km / km 2 6) Percentage of four-way intersections 7) Public transportation accessibility to the workplace 8) Distance to the store, unit: m 9) Distance to the nearest public transportation stop, in meters; (2) Calculate the change in PM2.5 ΔAir and the change in physical activity ΔPA under the urban planning scenario relative to the current situation; (3) Based on the PM2.5 change ΔAir and physical activity change ΔPA obtained in step (2) relative to the current situation, calculate the change H of the number of disease incidence / deaths caused by the urban planning scheme; (4) The evaluator determines whether the planning scheme needs to be optimized based on the output of step (3). If optimization is required, corresponding suggestions are provided. The planner adjusts and optimizes the planning scheme based on the suggestions. The optimized planning scheme is provided to step (1) and enters a loop iteration until the health effect of the planning scheme reaches the expected level. Based on the values ​​X of each indicator in the urban planning scheme i The numerical value X of the current situation i The formulas for calculating the changes in PM2.5 (ΔAir) and physical activity (ΔPA) resulting from urban planning schemes are as follows: （8） （9） Among them, X i and X i ′ represent the values ​​of indicator i in the urban planning scheme and the current situation, respectively, and C i and W i P represents the change in car trip volume and walking time corresponding to a 1% change in indicator i, respectively; c The value represents the proportion of PM2.5 generated by car traffic; Air′ represents the current PM2.5 concentration; Walk′ represents the current walking time; PA w This represents the amount of physical activity corresponding to a unit of walking time.

2. The evaluation method as described in claim 1, characterized in that, The following methods are used to calculate the numerical values ​​X of index i in the urban planning scheme and the current situation, respectively. i and X i ′: S1.1 Population density: Total population within the planning area divided by the total land area; S1.2 Employment Density: S1.2.1 Extract work sites based on the current situation and urban planning scheme land use data, including industrial and warehousing land, public management and public service facilities land and commercial service land, and calculate the land area of ​​each type of work site. Among them, the building area of ​​public management and public service facilities land and commercial service land needs to be calculated. S1.2.2 Based on the empirical values ​​of the employment population capacity corresponding to various work locations, the total number of employed people is calculated using formula (10); S1.2.3 The employment density is obtained by dividing the total number of employed people by the total area of ​​the planning scope; The calculation methods for building area differ between urban planning schemes and current scenarios. In urban planning schemes, building area is obtained by multiplying the land area by the plot ratio, which is directly extracted from the land use plan. In current scenarios, building area calculation requires overlaying building data and is obtained by multiplying the building footprint area by the number of building floors and summing the results. POP employment =∑LU×POP employment_unit (10) Among them, POP employment Total employed population, LU represents the workplace land area or building area, POP employment_unit The empirical value for the employment population capacity corresponding to the land area or building area of ​​various workplaces; S1.3 Commercial plot ratio: The commercial plot ratio of the urban planning scheme is obtained by extracting commercial land from land use data and taking the average plot ratio; the existing commercial plot ratio needs to be superimposed with building data, and the plot ratio of each commercial plot is calculated by formula (11) and the average is taken. (11) Among them, FAR c The current floor area ratio, area BB The area represents the current building footprint, floor represents the number of building floors, and area represents the total building footprint. LU The land area; S1.4 Land Use Mixing: Calculate the mean of the Shannon Diversity Index (SHDI); S1.4.1 Merge land use patches to obtain block data, calculate the area of ​​each block, and spatially connect it with the land use data; S1.4.2 Calculate the Shannon diversity index for each block according to formula (12) and take the average value; SHDI=-∑P i lnP i (12) Among them, SHDI is the Shannon Diversity Index of the neighborhood, P i It is the ratio of the area of ​​the block to the area of ​​the i-th type of land use; S1.5 road network density: S1.5.1 The total road length is obtained using computational geometry and statistical tools; S1.5.2 The road network density is obtained by dividing the total length of the road by the total area of ​​the planned area; S1.6 Percentage of four-way intersections: S1.6.1 Create a new network dataset based on road network data and generate intersection point data; S1.6.2 Spatially connects road line features and point features, and filters intersecting nodes; S1.6.3 Calculate the percentage of four-way intersections; S1.7 Public Transportation Accessibility to Workplace: Calculate the percentage of workplaces within 800m of public transportation stops; S1.7.1 Convert the areal work site data extracted from land use data into point data; S1.7.2 Using network analysis tools, with public transportation stops as facility points and workplaces as event points, solve for the shortest path from workplaces to the nearest public transportation stop; S1.7.3 Calculate the proportion of paths with a length of 800 meters or less to the total number of paths; Distance from S1.8 to the store: S1.8.1 Convert the area-based commercial and service land data extracted from the land use data into point data, and at the same time extract the residential land data and convert it into point data; S1.8.2 Using network analysis tools, with commercial service locations as facility points and residential locations as event points, solve for the shortest path from residential land to the nearest commercial service location; S1.8.3 Calculate the average path length; Distance from S1.9 to the nearest public transport stop: S1.9.1 Based on residential location data and public transportation station data, network analysis tools are used to solve for the shortest path from residential land to the nearest public transportation station, with public transportation stations as facility points and residential locations as event points. S1.9.2 Calculate the average path length.

3. The evaluation method as described in claim 1, characterized in that, Based on the values ​​X of each indicator in the urban planning scheme i The numerical value X of the current situation i The relative risk of disease j, i.e., RR, is calculated using formulas (6) and (7) based on the changes in PM2.5 ΔAir and physical activity ΔPA brought about by the urban planning scheme and the changes in PM2.5 and physical activity ΔAir and ΔPA, respectively. j (ΔAir) and RR j (ΔPA), the calculation formula is as follows: (6) (7) Where ΔAir and ΔPA represent the changes in PM2.5 and physical activity levels resulting from urban planning schemes, respectively; RR j (Air) and RR j (PA) represent the changes in PM2.5 and physical activity, respectively. j (Air) and E j The relative risk of disease j at (PA).

4. The evaluation method as described in claim 1, characterized in that, Calculate PAF j (ΔAir) and PAF j (ΔPA) based on RR j (ΔAir) and RR j (ΔPA), using formulas (4) and (5), calculate the percentage change in the number of people suffering from disease j caused by the urban planning scheme's influence on PM2.5 and physical activity level, i.e., PAF. j (ΔAir) and PAF j (ΔPA); (4) (5) Among them, RR j (ΔAir) and RR j (ΔPA) represents the relative risk of disease j when the changes in PM2.5 and physical activity are ΔAir and ΔPA, respectively. That is, the PM2.5 in ΔAir and the physical activity in ΔPA will make the incidence of disease j a multiple of its original value.

5. The evaluation method as described in claim 1, characterized in that, Step (3) Calculate the changes in the number of disease morbidity / mortality caused by the urban planning scheme; Based on PAF j (ΔAir) and PAF j (ΔPA), using formulas (1), (2) and (3) to calculate the change in the number of disease incidence / deaths caused by urban planning schemes; (1) Among them, H j The change in the number of morbidities / mortities caused by disease j can be calculated using the following formula: (2) Among them, H j ′ represents the current number of cases caused by disease j, PAF j This represents the percentage change in the number of people suffering from disease j resulting from the urban planning scheme. Further, we have: (3) Among them, PAF j (ΔAir) and PAF j (ΔPA) represents the percentage change in the number of people suffering from disease j caused by the urban planning scheme's influence on PM2.5 and physical activity levels, respectively.

6. The evaluation method as described in claim 1, characterized in that, Step (4) Optimization of urban planning scheme: Based on the health outcome calculation results in step (3), determine whether the expected goals have been achieved and whether the urban planning scheme needs to be optimized. If optimization is required, propose corresponding planning optimization suggestions based on the health impact direction and magnitude of the planning indicators, the adjustable space of the scheme, and the cost-benefit ratio. Finally, the adjusted urban planning scheme is determined, and the health benefits of the optimized scheme are simulated and predicted again in steps (1)-(3). If the expected goal is achieved, the optimized scheme is determined; if the goal is still not achieved, further optimization is carried out until the health results of the urban planning scheme can achieve the expected goal.

7. A health impact assessment system applied to urban planning schemes, characterized in that, The health impact assessment system applied to urban planning schemes is used to implement the health impact assessment method applied to urban planning schemes as described in any one of claims 1-6, wherein the health impact assessment system applied to urban planning schemes includes: The indicator calculation module, based on spatial and attribute data from the current situation and urban planning schemes, extracts vectorized indicators from the drawings and calculates the current situation indicator X. i ′ and urban planning scheme indicator X i Indicator i can be any one of the following nine indicators, which include: 1) Population density, unit: people / km 2 2) Employment density, unit: people / km 2 3) Commercial floor area ratio 4) Land use mix 5) Road network density, unit: km / km 2 6) Percentage of four-way intersections 7) Public transportation accessibility to the workplace 8) Distance to the store, unit: m 9) Distance to the nearest public transportation stop, in meters; The PM2.5 / physical activity change calculation module is connected to the output of the indicator calculation module and is used to calculate the PM2.5 change ΔAir and physical activity change ΔPA under the urban planning scenario relative to the current situation. The health outcome calculation module connects to the output of the PM2.5 / physical activity change calculation module and is used to calculate the change H in the number of disease incidence / mortality caused by urban planning schemes. The planning scheme optimization module connects to the output of the health outcome calculation module to determine whether the planning scheme needs to be optimized. If optimization is required, it provides planning optimization suggestions. After the planner implements the planning scheme, it is resubmitted to the indicator calculation module for iteration until the health effect of the planning scheme reaches the expected level.

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