CN113342873B - Population analysis unit division method based on city morphology and convergence mode - Google Patents
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Abstract
The invention discloses a population analysis unit dividing method based on city morphology and convergence mode, which comprises the following steps: s1, considering the space structure and population analysis fine granularity requirements of the city, fitting the natural form of the city facing the functional heterogeneity of the micro-scale population activity area, and dividing basic analysis units; s2, considering the spatial distribution characteristics of urban population flow in local convergence and the long-term stable characteristics depending on dynamic changes of entrances and exits, and constructing a population convergence preference model; s3, using the microstructure elements in the basic Analysis unit, dividing FPAZ (Fine Population Analysis Zone) suitable for expressing and analyzing Population distribution and variation characteristics. According to the method, the urban morphological characteristics and the population convergence mode are considered, and based on the urban morphological elements and the population convergence preference model, the FPAZ suitable for expressing population distribution and change characteristics is divided, so that the analysis and mining of the population distribution and change space-time mode are facilitated, and the fine management of urban population is further supported.
Description
Technical Field
The application belongs to the field of urban geographic intelligent calculation, and designs a refined population analysis unit division method based on urban morphology and population convergence mode.
Background
The population analysis unit is a basic space unit for space-time operation, rule mining and analysis result visualization of urban population analysis. Static population distribution or dynamic population change information is displayed through the population analysis unit, space-time analysis is carried out on the basis of the population analysis unit, and the fineness of population information expression and the accuracy of population analysis results are directly determined by the shape, the area size, the space continuity and other attributes of the population analysis unit. Therefore, the suitable population analysis unit division method is crucial to effectively expressing urban population activity information, analyzing population change spatiotemporal patterns and further supporting urban population related application.
In long-term urban population studies, a variety of demographic analysis units have been used or divided. Based on the expression of population distribution and variation information, in order to meet the condition and application requirement of population analysis unit division, the main division method is divided into 3 directions.
Demographic analysis unit partitioning based on spatial scale requirements. The common macro and mesoscopic population analysis units in cities are traditional administrative division units comprising urban districts and streets, and the space units can be directly matched with census data and other types of government statistical data, so that the space units not only are standard verification units of population distribution data, but also provide intuitive scientific basis data feedback for government management and policy planning. According to the micro scale, the building is a natural micro element in a city, and part of researchers use the building vector surface element directly as a population analysis unit, and can express population distribution information with fine spatial granularity or carry out refined population distribution pattern analysis. A population analysis unit dividing method based on application scene requirements. Considering dynamic change and flow of population, a typical application scenario is urban traffic, and related researchers divide a Traffic Analysis Zone (TAZ) by using main roads in an urban road network and analyze traffic travel characteristics of urban population on the basis of conforming to the form of a city of China. On the basis, the relevant data of the transportation sites and the transportation vehicles are further combined to analyze the interaction mode of urban population and transportation facilities or the operation mode of the transportation vehicles. The unit pertinently expresses population distribution and change characteristics based on the requirements of traffic scenes, and provides effective population information for planning or optimizing urban traffic. A population analysis unit partitioning method based on research data. The geography grids are currently the most popular and convenient demographic analysis units divided for matching data quality. The expression of the population information under different spatial resolutions is realized by manually establishing regular polygons with different sizes. The method can be directly matched with grid data such as land utilization data, night light data and the like popular in urban population research. With the development of the novel sensor, part of research is carried out on dividing Thiessen polygons into population analysis units, such as smart card swiping data and mobile phone signaling data, by taking individual space-time mark data and related urban service facility data as cores and combining mathematical theory, and the units realize scientific statistics and space expression on the human mouth space-time mark data.
Generally, the current population analysis unit division method is mainly driven by research data and application requirements, and the division method of the human unit is not considered from the perspective of population distribution and variation characteristic expression and analysis accuracy. Particularly, on a microscopic scale, the results of the 3 types of human mouth analysis units are different from urban forms, low in spatial unit continuity, insufficient in spatial resolution and the like, so that population information expression is inaccurate, the scene universality is poor, and fine and accurate urban population analysis and analysis result application cannot be supported for a long time.
Therefore, according to the characteristics of urban Population flow convergence, the invention combines with urban morphological elements to construct FPAZ (Fine Population Analysis Zone) suitable for characterizing and analyzing Population distribution and variation characteristics at a microscopic scale.
Disclosure of Invention
The invention provides a refined population analysis unit dividing method based on city morphology and population convergence mode, aiming at the problem that a microscopic population analysis unit suitable for expressing population distribution and change characteristics is absent at present. According to the characteristics of urban population flow convergence, the FPAZ suitable for characterizing and analyzing population distribution and variation characteristics under the micro scale is constructed by combining urban morphological elements.
The invention provides a refined population analysis unit dividing method based on city morphology and population convergence mode, which comprises the following steps:
s1, taking the space structure and population analysis fine granularity requirements of cities into consideration, extracting the elements of the urban trunk roads and the water system which are divided and form the urban space area, fitting the urban natural form facing the functional heterogeneity of the micro-scale population activity area, extracting the polygons of the roads and the water system based on the geometric attributes and the spatial topological features, and dividing the rest form elements into basic analysis units through the polygonization topological processing;
s2, considering the spatial distribution characteristics of urban population flow in local convergence and the long-term stable characteristics depending on entrance dynamic variation, constructing a population convergence preference model taking local entrance elements as a space division core, and extracting main entrance elements having a key effect on population convergence by utilizing a semantic dictionary matching and population flow simulation method;
and S3, dividing FPAZ suitable for expressing and analyzing population distribution and variation characteristics, namely a fine population analysis area, by using the microstructure elements in the basic analysis unit and the main entrance and exit elements extracted by the comprehensive population convergence preference model by using a spatial clustering method.
Further, the concrete implementation manner of extracting the road polygon in step S1 is as follows;
s11, single-level road polygon extraction
In the divided region R, the trunk road set L with the highest grade is selectediI ═ 1,2,3 … r, the larger r, the lower the rank; merging the region boundary B, and constructing a spatial unit set U by spatial topological processing of converting the line element into the plane elementi＝{uti1，uti2， uti3……utinGet UiThe areas uta of all the cells in the tree are used for constructing an area value distribution histogram, a minimum area threshold value minArea is determined according to the mutation points of the histogram group number, and any ut is treatedip(p＝[1,2…n]) If area utaipLess than minArea and no other elements other than asset type are contained within the cell, the cell is labeled as a road polygon; on the contrary, if utaipGreater than the threshold minArea, but only including asset-related spatial elements, then the cell is also labeled as a road polygon;
s12, multilevel road polygon extraction
At UiFiltering the road polygons on the basis of the first level and selecting the main road L of the second leveli+1Merging into a set of spatial units U by spatial topology operationsi+1＝{ut(i+1)1，ut(i+1)2，ut(i+1)1……ut(i+1)m}; then at Ui+1And repeating the step S11 on the basis of the road polygon information until all the main roads of all the levels are merged to finish the extraction of the road polygon.
Further, the specific implementation manner of extracting the main entrance and exit elements having a key effect on population convergence by using the semantic dictionary matching and the population flow simulation method in the step S2 is as follows;
s21 population flow path network construction
First, one place is selected inside or outside the divided region R in consideration of the geographical features of population flow convergenceThe physical orientation assumes a virtual population movement starting point OpjJ is a starting point selected according to the geographic orientation; all entrance and exit element sets in the region are used as target sites, and the urban trunk road network is used as a main network for population flow; meanwhile, only the influence of geographic distance factors on population movement is considered, and a population flow path network set EP is constructed by utilizing a Dijkstra shortest path algorithmj＝{epj1,epj2…epjk}；
S22, entrance/exit flow path similarity calculation
Extracting road intersections of a main road network, expressing a population flow path network as an ID sequence of elements of the path intersections, calculating the path similarity rs of simulation of each entrance and exit in the same basic analysis unit by using a difflib algorithm, developing a main entrance and exit mark by combining a similarity threshold tv, and setting tv according to the walking sensitivity distance value of urban population to the elements of the infrastructure points and the average length of the population flow path network point sequence;
s23, main entrance and exit mark based on path similarity
Calculating the corresponding flow paths ep for any two ports in any elementary analysis unitjvAnd epjwDegree of similarity rs, v ═ 1,2 … k],w＝[1,2…k]V ≠ w, if rs is smaller than the threshold value tv, the two access elements are respectively marked as main accesses; if rs is larger than the threshold value tv, firstly marking the two entrance and exit elements as the same cluster; the above operation is sequentially performed on each gateway element to obtain a cluster set CTR ═ { ct ═ ct1，ct2，ct3……ctxThen for any cluster ctgG ═ 1,2 … x as the gateway element in (1)]Calculating a cluster ct by considering the characteristic that the center of the space cluster has representativenessgObtaining a distance set DS (distance set) of the geographical distance dis of the path corresponding to each access elementg1，disg2，disg3……disgyGet sequence SQ { s by ordering the DSsg1， sg2，sg3……sgyAnd selecting an access element corresponding to the median value of the sequence SQ as a main accessAnd (4) a mouth.
Further, the difflib algorithm in step S22 is based on the LCS problem, as shown in formula (1), combined with the dynamic programming concept, as shown in formula (2), and the improved sequence variance calculation method of the perfect matching algorithm; in the formula (1), XmIs a sequence X, Y of length mnIs a sequence of length n Y, LCS (X)m,Yn) Is the longest common subsequence of sequences X and Y, in equation (2), c [ b][d]Recording the length of the longest common subsequence of sequence X and sequence Y, b and d being the length of sequence X and sequence Y, respectively;
further, the dividing step of FPAZ in step S3 is as follows:
1) microstructural element extraction
Extracting microstructure elements in a basic analysis unit, including an internal road and an artificial lake, and converting the microstructure elements and boundary combination line elements of the basic analysis unit into a spatial topology processing of surface elements to obtain a unit set M;
2) unit classification based on main access elements
According to the main entrance element set E ═ { E ═ E in the basic analysis unit1,e2,e3……ehSetting an entrance classification as EntryC for each unit of M, and marking the unit type EntryC as the ID attribute of a unique main entrance element if the element is contained in a certain unit; otherwise, if a certain unit contains more than one or no main entrance elements, calculating Euclidean distances from the center of mass of the unit to each main entrance element, and acquiring an entrance element ID attribute marking unit type entry of the shortest distance minEntry;
3) FPAZ partitioning
And based on the classification result of the main entrance, performing spatial fusion on the units with the same type EntryC in the M, and merging all fused units to obtain the FPAZ in the basic analysis unit.
Further, tv is 0.95.
The invention has the following beneficial effects: the FPAZ suitable for expressing population distribution and variation characteristics under the microscopic scale is divided by combining city morphological elements with a population convergence mode. The method comprises the steps of taking a basic dividing range defining a space scale and a population analysis unit as an entry point, dividing a basic analysis unit by using city morphological elements with large and medium scales, further obtaining access elements which have main influence on population aggregation change by using a population aggregation preference model, and further dividing the basic analysis unit by integrating microstructure elements to obtain FPAZ. The method not only considers the influence of the urban morphological characteristics on population distribution and change, but also considers the distribution change characteristics and long-term stability characteristics of urban population flow convergence, so that the FPAZ is more suitable for analyzing the spatio-temporal patterns of population distribution and change.
Drawings
FIG. 1 is a technical flow diagram of an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further explained with reference to the drawings and the embodiments.
The technical scheme and the detailed modeling flow of the invention are explained below by referring to the accompanying drawings and embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for dividing a refined population analysis unit based on city morphology and population convergence pattern in the embodiment of the present invention includes the following steps:
s1, taking the space structure and population analysis fine granularity requirements of cities into consideration, extracting elements for dividing and forming city space areas such as city trunk roads and water systems, fitting city natural forms facing to the functional heterogeneity of the micro-scale population activity areas, extracting polygons of the roads and the water systems based on geometric attributes and space topological features, and dividing the remaining form elements into basic analysis units through polygonal topological processing.
S2, considering the spatial distribution characteristics of urban population flow in local convergence and the long-term stable characteristics depending on entrance dynamic variation, constructing a population convergence preference model taking local entrance elements as a spatial division core, and extracting main entrance elements having a key effect on population convergence by utilizing a semantic dictionary matching and population flow simulation method.
And S3, dividing FPAZ suitable for expressing and analyzing population distribution and variation characteristics by using the microstructure elements in the basic analysis unit and the main entrance and exit elements extracted by the comprehensive population convergence preference model by using a spatial clustering method.
The basic idea of the invention is as follows: based on an understanding of the population distribution and the course of change in cities, the pattern of population movement and convergence in cities is similar to the nature of the process of water flow to lakes. The population moves under the drive of different activities through a multi-level traffic network, finally gathers in local space areas through fixed entrances and exits, and the gathering areas form irregular space units through the restriction of urban form elements such as roads, water networks and the like. Therefore, the method takes the basic division range of the defined space scale and the population analysis unit as an entry point, divides the basic analysis unit by using the urban morphological elements with large and medium scales, further obtains the entrance and exit elements which have main influence on population aggregation change by using the population aggregation preference model, and further divides the basic analysis unit by integrating the microstructure elements to obtain the FPAZ.
Compared with other population analysis unit dividing methods, the key creation point of the invention is to consider the characteristics of population distribution and change and consider the urban morphology and the requirement of fine-grained space, and the invention provides the population analysis unit dividing method which is suitable for analyzing the population distribution and the change mode on a micro scale.
In another embodiment of the invention:
the invention provides a refined population analysis unit dividing method based on city morphology and population convergence mode, which is applied to a certain city and comprises the following specific steps:
step 1, taking into account basic analysis unit division of city morphology
Morphological elements in cities have a direct limiting effect on dividing urban space areas to form space units with finer granularity, and further changing the flowing and gathering modes of population. The urban road is the most basic urban natural form factor as a transportation facility for movement and passage of urban population, and has important influence on dividing building plots and forming urban resident life boundaries.
1) Morphological element extraction and spatial topological unit construction for macroscopic and medium city
Firstly, defining a space range and a scale by using city form elements, and dividing a basic analysis unit. According to city-to-road network data and water network data, about 250000 main road route elements and about 800 main water system polygonal elements are extracted from a city together, and about 50000 space topological units are constructed in the city together.
2) Multi-level road polygon extraction and filtering
And (3) attaching the natural forms of cities to the micro-scale population activity gathering areas, and filtering multi-level road polygons, water system polygons and greening polygons. Considering the geometric attribute characteristics of the road polygon, because the TAZ is suitable for mesoscopic or macroscopic spatial analysis, the area of the road polygon is generally smaller than that of the TAZ; considering the spatial topological characteristics of a road polygon, the road is an urban basic traffic element, and only spatial elements related to road facilities are contained in the road polygon. The filtering step of the road polygon is thus as follows:
(1) single-stage road polygon extraction
In the divided region R, the trunk road set L with the highest grade is selectedi(the larger the i { (1, 2,3 … r } r, the lower the rank) is), the region boundary B is merged, and the spatial unit set U is constructed by the spatial topology processing in which the line element is converted into the plane elementi＝{uti1，uti2， uti3……utinH, where n is the set UiMaximum number of spatial units in, then obtain UiAnd (4) constructing an area value distribution histogram of the areas uta of all the cells, and determining a minimum area threshold value minArea according to the mutation points of the histogram group number. For arbitrary utip(p＝[1,2…n]) If area utaipLess than minArea and no other elements than asset type are contained within the cell, the cell is labeled as a road polygon. On the contrary, if utaipAbove the threshold minArea, but only contains asset-related spatial elements, the cell is also marked as a road polygon. According to the city road facility directory, road facility-related elements are extracted from the traffic and warehouse type POI data as shown in table 1.
TABLE 1 traffic and warehouse type POI data for filtering micro-scale road polygons
(2) Multi-level road polygon extraction
At UiFiltering the road polygons on the basis of the first level and selecting the main road L of the second leveli+1Merging into a set of spatial units U by spatial topology operationsi+1＝{ut(i+1)1，ut(i+1)2，ut(i+1)1……ut(i+1)mIn which m is the set Ui+1Maximum number of spatial cells. Then at Ui+1And (3) repeating the step (1) on the basis until all the main roads of all the levels are combined to finish the extraction of the road polygons. About 30800 road polygons and water system polygons are filtered in a certain city based on the steps, and finally, about 19000 basic analysis units are obtained.
Step 2, a population convergence preference model
At the microscopic scale of cities, the stationarity of population distribution changes is not only influenced by city morphological elements but also associated with population behaviors. The distribution and change of the population in the local area are mainly dependent on the entrance for flowing under the restriction of urban form factors. On the basis, the city population selects a preferred entrance under the drive of factors influencing the movement behaviors such as activity purpose, distance and the like, and then a long-term stable space convergence phenomenon is generated due to behavior similarity, so that a space unit with stable population distribution change is formed by taking the entrance as a core.
1) Access and exit element definition and extraction
The definition of the gateway is a fixed channel for the inflow and outflow of the human mouth under the local area, and the gateway elements corresponding to the attribute description are obtained by constructing a semantic dictionary according to the attribute characteristics, wherein the semantic dictionary mainly comprises the directory of directions, levels, sequence numbers, gateway description and the like as shown in a table 2.
Table 2 Access component extraction semantic dictionary
And taking the spatial characteristics into consideration, and performing spatial superposition on the internal road directly connected with the boundary to acquire the corresponding entrance and exit elements on the spatial characteristics. 45000 inlet elements are matched from POI data of a certain city according to a semantic dictionary; meanwhile, the attributes of 'parking lot connecting roads' and 'POI connecting roads' in the road network classification directory are combined to extract the intersection points of the internal roads and the boundary of the basic analysis unit, and finally, about 53000 entrance elements are obtained in total.
2) Population convergence preference model construction
The entrance and exit elements with different grades and spatial positions have difference on the flowing and gathering of the population by considering the preference characteristics of the population flowing on the entrance and exit selection. The population convergence mode is that the main entrance and exit with large population flow and large spatial feature difference with other entrance and exit elements have obvious influence on the population convergence of local areas.
Firstly, considering the acquisition of the inlet and outlet elements of the semantic attribute characteristic part of the inlet and outlet elements from the matching result with the semantic dictionary, the main inlet and outlet elements can be extracted according to the semantic dictionary directory, and the steps are as follows:
(1) the method considers the low flow characteristic of the walk entrance of the population, is not a main node of population flow and local area convergence, and only allows the entrance elements of people to walk through and is filtered according to the 'pedestrian' vocabulary of the 'classification' directory of the semantic dictionary. About 8000 inlet and outlet elements are filtered by the method in a certain market, and about 39000 residual inlet and outlet elements are remained.
(2) And (4) taking the positions and the classifications of the access elements into consideration, extracting the access elements with main positions in the spatial positions according to similar semantic words such as 'main' and 'front' in the 'direction and classification' directory of the semantic dictionary, and marking the access elements as main accesses.
(3) And considering the level similarity characteristics of the access elements, the access elements with the same name and different serial numbers have similar population preference influence. Therefore, the gateway elements with the same gateway name are extracted according to the specific serial number in the "number" directory of the semantic dictionary and marked as the main gateway. About 160 major entrances and exits are marked in a city based on the above steps.
And secondly, marking the main entrance element by using a population flow simulation method in consideration of the shape and the area of the basic analysis unit and the spatial distribution characteristics of the entrance element. The labeling steps based on the population simulation method are as follows:
(1) population flow path network construction
Firstly, considering the geographic characteristics of population flow convergence, a geographic azimuth is selected inside or outside the divided region R to assume a virtual population flow starting point Opj(j is a starting point selected according to the geographical direction, and at most 4 simulation points are needed to complete the simulation, so that j in this embodiment is 1,2,3,4), all entrance and exit element sets in the area are used as target locations, and the urban main road network is used as a main network for population flow. Meanwhile, only the influence of geographic distance factors on population movement is considered, and a population flow path network set EP is constructed by utilizing Dijkstra shortest path algorithmj＝{epj1,epj2…epjkWhere k is the set EPjMaximum number of flow paths in (2).
In this embodiment, two traffic stations, namely a train station and an airport, located in two directions of east and west are respectively selected in a certain city as starting points of population flow simulation, 39000 entrance and exit elements are used as end points, roads above the county level are used as a simulation network for population flow, and about 78000 population flow paths are simulated and constructed by a shortest path search algorithm.
(2) Inlet and outlet flow path similarity calculation
The method comprises the steps of extracting road intersections of a main road network, expressing a population flow path network as an ID sequence of elements of the path intersections, and calculating the path similarity rs of each entrance/exit simulation in the same basic analysis unit by using a difflib algorithm, wherein the difflib algorithm is a sequence difference calculation method based on LCS (LCS) (shown in a formula (1)) and combined with a dynamic planning idea (shown in a formula (2)) and an integer matching algorithm. In the formula (1), XmIs a sequence X, Y of length mnIs a sequence of length n Y, LCS (X)m,Yn) Max represents taking the maximum value as the longest common subsequence of sequences X and Y. In formula (2), c [ b ]][d]The length of the longest common subsequence for recording sequence X and sequence Y, and b and d are the length of sequence X and sequence Y, respectively.
And then developing a main entrance mark by combining a similarity threshold value tv, wherein tv is set to be 0.95 according to a walking sensitivity distance value of the city population to the infrastructure point elements of 400m and the average length of the population flow path network point sequence.
(3) Main entrance and exit mark based on path similarity
Calculating the corresponding flow paths ep for any two ports in any elementary analysis unitjvAnd epjw(v＝[1,2…k],w＝[1,2…k]V ≠ w), if rs is smaller than a threshold value tv, marking the two access elements as main accesses respectively; if rs is larger than threshold value tv, firstly marking two entrance and exit elements as the same cluster. The above operation is sequentially performed on each gateway element to obtain a cluster set CTR ═ { ct ═ ct1，ct2，ct3……ctxWhere x is the maximum number of clusters in the set CTR. Then for any cluster ctg(g＝[1,2…x]) The cluster ct is calculated by considering the characteristic that the center of the space cluster has the representativenessgObtaining a distance set DS (distance set) of the geographical distance dis of the path corresponding to each access elementg1，disg2， disg3……disgySequence SQ ═ s obtained by sorting the DSsg1，sg2，sg3……sgyAnd y is the maximum number of the distance set DS and the sequencing sequence SQ, and an access element corresponding to the median of the sequence SQ is selected and marked as a main access. In the present embodiment, based on the above steps, a total of about 6000 basic analysis units including 2 or more main entrances are obtained.
Step 3, FPAZ division based on microscopic elements
And further dividing the basic analysis unit based on main entrance and exit elements obtained by the population convergence preference model to obtain FPAZ suitable for expressing population distribution and change information under a micro scale. Considering the influence of the microstructure elements in the basic analysis unit on the spatial range of population convergence, the FPAZ dividing steps are as follows:
1) microstructural element extraction
And extracting microstructure elements such as internal roads, artificial lakes and the like in the basic analysis unit, and converting the microstructure elements and boundary combination line elements of the basic analysis unit into space topology processing of surface elements to obtain a unit set M.
2) Unit classification based on main access elements
According to the main entrance element set E ═ { E ═ E in the basic analysis unit1,e2,e3……ehH is the maximum number of main entrance elements in the set E, entrance classification is set for each unit of M as entry, and if a unit contains a unique main entrance element, the unit type entry is marked as the ID attribute of the element; conversely, if a unit includes more than one or no main access elementAnd then, calculating Euclidean distances from the centroid of the unit to each main entrance element, and acquiring the entrance element ID attribute marking unit type entry C of the shortest distance minEntry.
3) FPAZ partitioning
And based on the classification result of the main entrance, performing spatial fusion on the units with the same type EntryC in the M, and merging all fused units to obtain the FPAZ in the basic analysis unit.
In this embodiment, based on the result of the calculation of the population convergence preference model in a certain city, 6000 basic analysis units are further divided, and 39000 FPAZ units are obtained in total by combining the original basic analysis units.
The method is based on the characteristics of urban population flow convergence, defines the space scale and the space division range by the macroscopic mesoscopic morphological elements of the city, filters multi-level road polygons facing to the micro-scale requirement, and constructs a basic analysis unit. And marking access factors which have main influence on population convergence by using the population convergence preference model, and further dividing the FPAZ suitable for expressing population distribution and variation characteristics by combining with microstructure factors in the basic analysis unit. The research on the micro-scale population analysis unit division method is beneficial to the analysis and mining of population distribution and change spatiotemporal patterns, so that the fine management of urban population is further supported.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (5)
1. A population analysis unit division method based on city morphology and convergence mode is characterized by comprising the following steps:
s1, extracting, dividing and forming elements of urban space areas including urban trunk roads and water systems according to the space structure and population analysis fine-grained requirements of cities, attaching urban natural forms facing to functional heterogeneity of micro-scale population activity areas, extracting polygons of roads and water systems based on geometric attributes and spatial topological features, and dividing the remaining morphological elements into basic analysis units through polygonal topological processing;
s2, considering the spatial distribution characteristics of urban population flow in local convergence and the long-term stable characteristics depending on entrance dynamic variation, constructing a population convergence preference model taking local entrance elements as a space division core, and extracting main entrance elements having a key effect on population convergence by utilizing a semantic dictionary matching and population flow simulation method;
the specific implementation manner of extracting the main entrance and exit elements having a key effect on population convergence by using the semantic dictionary matching and the population flow simulation method in the step S2 is as follows;
s21 population flow path network construction
Firstly, considering the geographic characteristics of population flow convergence, a geographic azimuth is selected inside or outside the divided region R to assume a virtual population flow starting point OpjJ is a starting point selected according to the geographic orientation; all entrance and exit element sets in the region are used as target sites, and the urban trunk road network is used as a main network for population flow; meanwhile, only the influence of geographic distance factors on population movement is considered, and a population flow path network set EP is constructed by utilizing Dijkstra shortest path algorithmj＝{epj1,epj2…epjkWhere k is the set EPjMaximum number of flow paths in (a);
s22, entrance/exit flow path similarity calculation
Extracting road intersections of a main road network, expressing a population flow path network as an ID sequence of elements of the path intersections, calculating the path similarity rs of simulation of each entrance and exit in the same basic analysis unit by using a difflib algorithm, developing a main entrance and exit mark by combining a similarity threshold tv, and setting tv according to the walking sensitivity distance value of urban population to the elements of the infrastructure points and the average length of the population flow path network point sequence;
s23, main entrance and exit mark based on path similarity
Calculating the corresponding flow paths ep for any two ports in any elementary analysis unitjvAnd epjwDegree of similarity rs, v ═ 1,2 … k],w＝[1,2…k]V ≠ w, if rs is smaller than the threshold value tv, the two access elements are respectively marked as main accesses; if rs is larger than the threshold value tv, firstly marking the two entrance and exit elements as the same cluster; sequentially executing the above operation on each gateway element to obtain a cluster set CTR ═ ct1，ct2，ct3……ctxX is the maximum number of clusters in the set CTR; then for any cluster ctgG ═ 1,2 … x as the gateway element in (1)]Calculating a cluster ct by considering the characteristic that the center of the space cluster has representativenessgObtaining a distance set DS (distance set) of the geographical distance dis of the path corresponding to each access elementg1，disg2，disg3……disgyGet sequence SQ { s by ordering the DSsg1，sg2，sg3……sgyY is the maximum number of the distance set DS and the sequencing sequence SQ, and an access element corresponding to the median of the sequence SQ is selected and marked as a main access;
and S3, dividing FPAZ suitable for expressing and analyzing population distribution and variation characteristics, namely a fine population analysis area, by using the microstructure elements in the basic analysis unit and the main entrance and exit elements extracted by the comprehensive population convergence preference model by using a spatial clustering method.
2. The population analysis unit partitioning method based on urban morphology and convergence patterns as claimed in claim 1, wherein: the concrete implementation manner of extracting the road polygon in step S1 is as follows;
s11, single-level road polygon extraction
In the divided region R, the trunk road set L with the highest grade is selectediI ═ 1,2,3 … r, the larger r, the lower the rank; merging the region boundary B, and constructing a spatial unit set U by spatial topological processing of converting the line element into the plane elementi＝{uti1，uti2，uti3……utinH, where n is the set UiMaximum number of spatial units in, then obtain UiThe areas uta of all the cells in the histogram are constructed, an area value distribution histogram is constructed, a minimum area threshold value minArea is determined according to the catastrophe points of the histogram group number, and for any utip，p＝[1,2…n]If area utaipLess than minArea and no other elements other than asset type are contained within the cell, the cell is labeled as a road polygon; otherwise, if utaipGreater than the threshold minArea, but only including asset-related spatial elements, then the cell is also labeled as a road polygon;
s12, multilevel road polygon extraction
At UiFiltering the road polygons on the basis of the first level and selecting the main road L of the second leveli+1Merging into a set of spatial units U by spatial topology operationsi+1＝{ut(i+1)1，ut(i+1)2，ut(i+1)1……ut(i+1)mWhere m is the set Ui+1Maximum number of spatial cells; then at Ui+1And repeating the step S11 on the basis of the road polygon information until all the main roads of all the levels are merged to finish the extraction of the road polygon.
3. The population analysis unit partitioning method based on city morphology and convergence pattern as claimed in claim 1, wherein: the difflib algorithm in step S22 is based on LCS problem, as shown in formula (1), combined with dynamic programming concept, as shown in formula (2), and a sequence variance calculation method improved by the full match algorithm; in the formula (1), XmIs a sequence X, Y of length mnIs a sequence of length n Y, LCS (X)m,Yn) Max is the maximum value for the longest common subsequence of sequences X and Y, in equation (2), c [ b ]][d]Recording the length of the longest common subsequence of sequence X and sequence Y, b and d being the length of sequence X and sequence Y, respectively;
4. the population analysis unit partitioning method based on urban morphology and convergence patterns as claimed in claim 1, wherein: the dividing step of FPAZ in step S3 is as follows;
1) microstructural element extraction
Extracting microstructure elements in a basic analysis unit, including an internal road and an artificial lake, and converting the microstructure elements and boundary combination line elements of the basic analysis unit into a spatial topology processing of surface elements to obtain a unit set M;
2) unit classification based on main access elements
According to the main entrance element set E ═ { E ═ E in the basic analysis unit1,e2,e3……ehH is the maximum number of main entrance elements in the set E, entrance classification is set for each unit of M as entry, and if a unit contains a unique main entrance element, the unit type entry is marked as the ID attribute of the element; on the contrary, if a certain unit contains more than one or no main entrance elements, calculating Euclidean distances from the centroid of the unit to each main entrance element, and acquiring an entrance element ID attribute marking unit type entry C of the shortest distance minEntry;
3) FPAZ partitioning
And based on the classification result of the main entrance, performing spatial fusion on the units with the same type EntryC in the M, and merging all fused units to obtain the FPAZ in the basic analysis unit.
5. The population analysis unit partitioning method based on urban morphology and convergence patterns as claimed in claim 1, wherein: tv is 0.95.
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