CN112487309B - Uncertainty medical reachability calculation method based on track data - Google Patents
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Abstract
The invention relates to an uncertainty medical accessibility computing method based on track data, which specifically comprises the following steps: step 1, data acquisition and preprocessing; step 2, spatial processing and grid generation; step 3, extracting historical travel time; step 4, constructing an OD matrix; step 5, calculating the trip completion probability; step 6, calculating the accessibility under the reliability constraint; the method provided by the invention has the following advantages: (1) obtaining medical accessibility more in line with the reality; (2) The model is simple, easy to understand and calculate, and has strong application; (3) Hexagonal has fewer adjacent patterns, smaller side area ratios than square; meanwhile, the method has isotropic geometric properties, and errors in reachability calculation can be reduced.
Description
Technical Field
The invention belongs to the technical field of geographic information technology and the like, and relates to an uncertainty medical accessibility measurement method based on floating car track data.
Background
Reachability is a basic concept in traffic geography and city planning, and has been widely used for traffic assessment and facility location. Medical accessibility is an important index for measuring medical infrastructure construction, and plays an important role in policy formulation. Common spatial reachability metrics models include: a gravity model, a two-step mobile search model, an accumulated opportunity model, a space-time constraint model and a space blocking model. The cumulative opportunity model focuses on the difficulty level of approaching opportunities of nodes. The number of all opportunities that are contacted within the threshold is defined by a given threshold as the reachability of a particular node. The cumulative opportunity model has better interpretability and simple and convenient calculation, and is widely applied to reachability research.
With the development and popularization of network communication technology, vehicle-mounted positioning navigation and satellite remote sensing technology, it becomes easier to acquire massive space-time trajectory data. The accessibility study using trajectory data is becoming a new trend for accessibility studies. In large and medium-sized cities crowded in China, uncertainty exists in travel time of travel among the same ODs due to frequent occurrence of random phenomena such as traffic jam and the like. Previous studies have generally employed a fixed time threshold to measure the reachability of a location, ignoring the impact of uncertainty in travel time on reachability. Thus introducing reliability constraints in the traditional reachability model can yield more accurate reachability results.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide an uncertainty medical accessibility calculation method based on track data, which specifically comprises the following steps: reliability constraint is introduced into the traditional reachability model by calculating travel completion probability, and reachability calculation is promoted from deterministic to uncertain. The method can obtain the space accessibility which is more in line with the actual situation.
The aim of the invention is realized by the following technical scheme:
An uncertainty medical accessibility computing method based on track data considers the reliability of travel time, and comprises the following steps:
Step 1, data acquisition and preprocessing;
Firstly, crawling medical facility POI data by using an API interface provided by an Internet map company; the original track data is often a GB-level large file, and contains data information of 1 week or even several weeks, so that the file needs to be extracted in a daily/hour unit according to the requirement;
then, abnormal information generated by building shielding and GPS signal loss is removed, and time fields are standardized. Meanwhile, calculating the time difference between adjacent track points, the time difference between the track points and the starting point, the running distance and the average speed of the track by taking the track as a unit, and finishing the preprocessing of track data; the time difference between the track point and the starting point is the travel time.
Step2, spatial processing and grid generation;
In ArcGIS, setting a geographic coordinate system and space projection of a newly built data frame, adding an administrative division map of a research area and a main city road, and dividing the research area into a plurality of hexagonal grids with determined side lengths by using a Thiessen Polygon toolbox;
And (3) adding the track data processed in the step (1), and spatially connecting the hexagonal grid with the track data according to the positions.
Adding medical facility POIs (point of interest) and performing space connection with the hexagonal grids, setting weights of different types of medical facilities by referring to the number of beds of the medical facilities, and calculating to obtain a medical facility score of each grid to obtain a formula (1):
Let G j be the healthcare facility score for grid j, The weight of the facility h in the grid j is:
wherein s is the total number of medical facilities in the grid j;
step 3, extracting historical travel time;
Extracting all OD rows on each track based on the track data obtained by processing in the step 2, representing as (i, j), assuming that the track tau consists of k track points, Q τ represents a set of grid numbers through which the track tau passes, and m represents the number of grids through which the track tau passes, and obtaining a formula (2):
In the method, in the process of the invention, The number of OD pairs included in the track is/>, indicating the grid number where the track point k on the track τ is locatedT 1,t2,t3,…,tk-1,tk represents the dotting time of the track points 1,2,3, …, k-1, k;
assuming that the number of track points of the track τ in the grid i is n i, equation (3) is obtained:
Then The departure time of the grid i is represented, and the value of the departure time is the dotting time of the last point of the track tau in the grid i;
Therefore, the travel time between the grids (i, j) is denoted as Td j-Tdi, and Td j-Tdi > 0;
Step 4, constructing an OD matrix;
On the basis of the step 3, the travel time of the same OD is combined to obtain a historical travel time matrix of the OD travel:
ODij＝{Tdj-Tdi|i＝0，1，2…，1101，j＝0，1，2，…，1101，i≠j} (4)
Wherein OD ij is the set of historical travel times between ij, 0,1,2, …,1101 is the hexagonal grid number;
Step 5, calculating the trip completion probability;
assuming that the travel time is subject to normal distribution, the travel completion probability is as shown in formula (5):
Wherein CDF ij (T) is a cumulative distribution function of travel time between ij, sigma ij is a sample standard deviation of travel time between ij, and mu ij is a sample mean of travel time between ij;
step 6, calculating the accessibility under the reliability constraint;
The cumulative opportunity model expression is as shown in the formula (6):
Where CUM i is the spatial reachability for trellis i without reliability considerations, The number of opportunities in the grid is a 0-1 variable, and when the travel time Td j-Tdi between the grids (i, j) is less than or equal to the set time threshold T,/>Otherwise/>
Introducing reliability constraints in (6), then obtaining (7):
wherein PCUM i(T,r0) is the accessibility when the reliability requirement is r 0 and the time threshold is T, The number of opportunities in the grid is 0 or 1, and the travel time between (i, j)/>When the time threshold value T is less than or equal to the time threshold value T,/>The resident at i gets the opportunity of j grid, otherwise,/>
Under the constraint of time threshold and reliability, the total number of opportunities that residents can obtain is the reachability of the grid i.
On the basis of the scheme, the Internet map company in the step 1 comprises a hundred-degree map and a high-Germany map.
Based on the scheme, the side length of the step 2 is 500m.
Based on the above scheme, the different types of medical facilities described in step 2 are: primary hospitals, secondary hospitals, tertiary hospitals;
the invention has the beneficial effects that:
the invention provides a method for calculating medical accessibility, which utilizes track data and considers the influence of uncertainty of travel time, and has the following advantages:
(1) Medical accessibility which is more in line with the actual situation can be obtained;
(2) The model is simple, easy to understand and calculate, and has strong application;
(3) Hexagonal has fewer adjacent patterns, smaller side area ratios than square; meanwhile, the method has isotropic geometric properties, and errors in reachability calculation can be reduced.
Drawings
The invention has the following drawings:
FIG. 1 is a flow chart of the calculation method of the present invention.
Fig. 2 is a schematic illustration of a medical facility distribution.
Fig. 3 is a schematic view of the study area and meshing.
FIG. 4 is a schematic view of a medical score distribution.
Fig. 5 is a schematic view of the spatial distribution of medical reachability with reliability requirement of 0.1.
Fig. 6 is a schematic view of the spatial distribution of medical reachability with reliability requirement of 0.5.
Fig. 7 is a schematic view of the spatial distribution of medical reachability with reliability requirement of 0.9.
Detailed Description
The present invention will be described in further detail with reference to fig. 1 to 7, but is not limited thereto.
The invention provides a method for calculating medical accessibility by using track data, which establishes a mathematical model.
The existing track data is the track data of the Hide floating vehicle of 9 months in 2015, and the data fields comprise acquisition time, vehicle number, instantaneous speed, longitude and latitude and road number; in the processing process, 2015.9.1-2015.9.7 data are extracted, track points with abnormal longitude and latitude fields are removed, meanwhile, the time difference between adjacent track points, the time difference (and travel time) between the track points and the starting point, the travel distance and average speed of the track are calculated by taking the track as a unit, and the data are stored by taking the hour as a unit. Through the API interface provided by the german, the medical facility data 2248 in beijing city is crawled, and the fields include: name, address, longitude, latitude, category. Thereafter, the medical facilities are classified into three categories of tertiary hospitals, secondary hospitals and primary hospitals according to medical resources, and the distribution of each medical facility is shown in fig. 2:
The five rings in Beijing city are selected as research areas, and the research areas are divided into 1102 hexagonal grids (shown in fig. 3) with 500m as side length.
Statistical analysis was performed on different categories of hospital information, the results are shown in the following table:
table 1 medical facility information
A medical score for each grid can be obtained according to equation (1), as shown in fig. 4;
setting the time threshold to be 30min, and calculating to obtain medical accessibility results under different reliability requirements.
Fig. 5 to 7 show, wherein fig. 5 is a schematic view of the spatial distribution of the medical reachability with reliability requirement of 0.1, fig. 6 is a schematic view of the spatial distribution of the medical reachability with reliability requirement of 0.5, and fig. 7 is a schematic view of the spatial distribution of the medical reachability with reliability requirement of 0.9.
It can be seen that: under different reliability constraints, the spatial distribution of medical reachability has significant differences. With the increase of reliability requirements, medical accessibility has a trend to decrease; and the central urban area has relatively high medical accessibility.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
What is not described in detail in this specification is prior art known to those skilled in the art.
Claims (4)
1. The uncertainty medical accessibility computing method based on the track data is characterized by comprising the following steps:
Step 1, data acquisition and preprocessing;
Firstly, crawling medical facility POI data by using an API interface provided by an Internet map company;
Then, eliminating abnormal information generated by building shielding and GPS signal loss, and carrying out standardized processing on time fields; meanwhile, taking the track as a unit, calculating the time difference between adjacent track points, the time difference between the track points and the starting point, the running distance and the average speed of the track, and finishing the preprocessing work of track data, wherein the time difference between the track points and the starting point is the travel time;
Step2, spatial processing and grid generation;
In ArcGIS, setting a geographic coordinate system and space projection of a newly built data frame, adding an administrative division map of a research area and a main city road, and dividing the research area into a plurality of hexagonal grids with determined side lengths by using a Thiessen Polygon toolbox;
Adding the track data processed in the step 1, and spatially connecting the hexagonal grid with the track data according to the position;
adding medical facility POIs (point of interest) and performing space connection with the hexagonal grids, setting weights of different types of medical facilities by referring to the number of beds of the medical facilities, and calculating to obtain a medical facility score of each grid to obtain a formula (1):
Let G j be the healthcare facility score for grid j, The weight of the facility h in the grid j is:
wherein s is the total number of medical facilities in the grid j;
step 3, extracting historical travel time;
Extracting all OD rows on each track based on the track data obtained by processing in the step 2, representing as (i, j), assuming that the track tau consists of k track points, Q τ represents a set of grid numbers through which the track tau passes, and m represents the number of grids through which the track tau passes, and obtaining a formula (2):
In the method, in the process of the invention, The number of OD pairs included in the track is/>, indicating the grid number where the track point k on the track τ is locatedT 1,t2,t3,…,tk-1,tk represents the dotting time of the track points 1,2,3, …, k-1, k;
assuming that the number of track points of the track τ in the grid i is n i, equation (3) is obtained:
Td i＝max(tn i) represents the departure time of the grid i, the value of which is the dotting time of the last point of the track τ in the grid i;
Therefore, the travel time between the grids (i, j) is denoted as Td j-Tdi, and Td j-Tdi > 0;
Step 4, constructing an OD matrix;
On the basis of the step 3, the travel time of the same OD is combined to obtain a historical travel time matrix of the OD travel:
ODij＝{Tdj-Tdi|i＝0，1，2…，1101，j＝0，1，2，…，1101，i≠j} (4)
Wherein OD ij is the set of historical travel times between ij, 0,1,2, …,1101 is the hexagonal grid number;
Step 5, calculating the trip completion probability;
assuming that the travel time is subject to normal distribution, the travel completion probability is as shown in formula (5):
Wherein CDF ij (T) is a cumulative distribution function of travel times between ij, sigma is a sample standard deviation of travel times between ij, and mu is a sample mean of travel times between ij;
step 6, calculating the accessibility under the reliability constraint;
The cumulative opportunity model expression is as shown in the formula (6):
Where CUM i is the spatial reachability for trellis i without reliability considerations, The number of opportunities in the grid is a 0-1 variable, and when the travel time Td j-Tdi between the grids (i, j) is less than or equal to the set time threshold T,/>Otherwise/>
Introducing reliability constraints in (6), then obtaining (7):
wherein PCUM i(T,r0) is the accessibility when the reliability requirement is r 0 and the time threshold is T, The number of opportunities in the grid is 0 or 1, and the travel time between (i, j)/>When the time threshold value T is less than or equal to the time threshold value T,/>The resident at i gets the opportunity of j grid, otherwise,/>
Under the time threshold and reliability constraints, the total number of opportunities that a resident can obtain is the reachability of grid i.
2. The method of claim 1, wherein the internet map company of step 1 comprises a hundred degree map and a high-german map.
3. The method for calculating the uncertainty medical reachability based on the trajectory data as claimed in claim 1, wherein the side length in the step 2 is 500m.
4. The method for calculating uncertainty medical reachability based on trajectory data as claimed in claim 1, wherein said different types of medical facilities in step 2 are: primary hospitals, secondary hospitals, tertiary hospitals.
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