CN106203681A - The constant due-date type urban area of a kind of data-driven divides and method for digging - Google Patents

Abstract

The constant due-date type urban area that the invention discloses a kind of data-driven divides and method for digging, network is set up in geographical position first with public traffic station, and the different connected components therefrom extracted in network set up point set, each point set is checked, undesirably adjust, then the central point of the point set after adjusting is calculated as regional center, afterwards the point in region is distributed to closest regional center, by route data, limit is built in region again, form Local Area Network, finally according to Local Area Network zoning ranking, output top n region is guidance type (TOD) region as public transport.The present invention can utilize bus station data to divide urban area, excavates TOD region, provides effective information for urban development, has the features such as feasibility is strong, applied widely, Result is accurate.

Description

The constant due-date type urban area of a kind of data-driven divides and method for digging

Technical field

The present invention relates to (TOD) urban development pattern field with public transport as guidance type, particularly relate to a kind of based on city The TOD urban area of city's data divides and method for digging.

Background technology

TOD urban development pattern has become as the possible scheme of one solving urban sustainable development, this kind of developmental pattern The form of urban development can be reinvented, promote its quality, alleviate the problems that city faces.Want to carry out building of TOD type city If primary problem is classifying rationally region, then it is intended to select the region of most worthy to carry out accordingly as TOD region Exploitation.The present invention will utilize public transport station point data to determine regional center and boundary method, utilize public transport afterwards Route excavates TOD region.But in existing technology, directly region is divided according to longitude and latitude often, or according to Road net data carries out region division, and these can not well embody public transport center, is unfavorable for that city is around public Traffic develops.

Summary of the invention

In place of the purpose of the present invention some shortcomings mainly for above-mentioned existing research, the public of a kind of data-driven is proposed Traffic guide type urban area divide and method for digging, by public transport station point data process, determine regional center and Zone boundary, utilizes public transit route data, excavates TOD region, provides effective information for urban development planning.

The present invention is to reach above-mentioned purpose by the following technical programs, the constant due-date type city of a kind of data-driven Region divides and method for digging, and it comprises the steps:

1) for the distance public traffic station less than d, setting up a limit, obtain public traffic network, d is for pre-setting Threshold value.

2) according to 1) in public traffic network, extract the point set S in each connected component, join the collection of point set S Close in T.

3) each point set is checked whether meet expectation.Do not meet and adjust.

4) calculate the center of each concentration, obtain regional center.

5) according to 4) in regional center, in region point belong to closest regional center, obtain zone boundary.

6) utilize public transit route data to build limit between zones, obtain Local Area Network.

7) according to 6) in Local Area Network, carry out zoning ranking.

8) output top n region is as TOD region.

Step 1) concrete steps:

Using each website as a node, a limit is set up for any two distance website less than d, the most whole Public traffic network can be formed multiple connected component.

Step 2) concrete steps:

Each connected component in traversing graph, the when of the connected component that every time traversal is new, generates null set S, will be all over The node gone through joins set S, the when that this connected component traversal terminating, is added by set S in the set T of set.

Step 3) each point set checked whether meet expectation.Do not meet and adjust.Comprise the following steps:

3.1) for the node in each set, if there is the distance between two nodes more than 2d, then it is believed that be somebody's turn to do Node in set, at least can be divided into two set, then perform 3.2)；If do not existed, then it is believed that in each set All nodes are no more than d from its center, then point set meets expectation, perform step 4)

3.2) using two farthest for distance in this set nodes as initialized barycenter in k-means algorithm, then exist This set performs k-means algorithm, forms two bunches.

3.3) delete original some set, two bunches are added in T as newly-generated point set, continues checking for new collection Close.

Step 4) calculate the formula at each some concentration center:

$\ln g\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^n\ln g\left(i\right);lat\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^{n}lat\left(i\right);$

Number of nodes during wherein n is region, lat (i) is the latitude of i-th node, and lng (i) is the warp of i-th node Degree.Lng (center) and lat (center) is the position at the center obtained.

Step 5) obtain the formula of zone boundary:

R polymerization website (i.e. candidate centers) P={p that we will obtain from clustering algorithm based on connected component₁, p₂,...,p_RAs seed, whole survey region X is carried out Voronoi division, thus it is many to form R disjoint Voronoi Limit shape, polygon be exactly one to be polymerized website p_iCentered by TOD candidate Development area r_i, formal definition such as formula institute Under.

r_i=x ∈ X | D (x, p_i)≤D(x,p_j), i, j={1,2 ..., R}, i ≠ j}

In above formula formula, we use euclidean metric as distance function D therein.

Step 6) utilize public transit route data to build limit between zones, obtain Local Area Network concrete steps:

We give the quality of every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and numerical value reduces successively, then by the region at continuous two stations of all routes, as two nodes, set up one corresponding The limit of weights, if continuous the two of route stand in the same area, then ignores.

Step 7) zoning ranking concrete steps:

PageRank algorithm based on random walk model is applied in regional relation network, as zoning by we The basis of importance.

7.1) RandomWalk value (the being called for short RW value) RW in given all R regions (i.e. node)_i, and meet (0)

7.2) iteration calculates, and the formula calculating kth step is as follows:

${\mathrm{RW}}_i\left(k\right)=d\underset{j=1}{\overset{R}{\Σ}}{s}_{i,j}\×{\mathrm{RW}}_j(k-1)+\frac{1-d}{R}$

Wherein, d is damped coefficient, typically takes 0.85.By s_i,jThe matrix S of the R row R row formed commonly referred to as shifts square Battle array.

7.3) when RW (k) RW (k-1) absolute value and less than threshold value W, stop iterative computation.

7.2) being specifically defined of transfer matrix in:

7.2.1) importance is connected:

We give the quality of every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and numerical value reduces successively.So, in region connects network, node v_iWith node v_jConnection importance w (v_i,v_j) can Calculated by equation below.

w(v_i,v_j)=m_i,jw_m+n_i,jw_n+k_i,jw_k

7.2.2) transfer matrix:

After determining connection importance, account for destination node according to each limit importance and go out the ratio of the total importance in limit and carry out generally Rate shifts, the element s in transfer matrix S after i.e. improving_i,jCalculate according to equation below,

Wherein, w (v_i,v_j) represent node v_iWith node v_jBetween connection importance, can be by 7.2.1) in formula calculate Go out, gather N (v_j) it is node v_jThe set of neighbor node, i.e. with node v_jThe node being connected.

Accompanying drawing explanation

The constant due-date type urban area of a kind of data-driven that Fig. 1 provides for case study on implementation of the present invention divides and digs The flow chart of pick method；

Fig. 2 is the flow chart that case study on implementation of the present invention extracts regional center；

Fig. 3 is that the constant due-date type urban area division of a kind of data-driven utilizes Hangzhoupro with method for digging case study on implementation The region division result figure that state city public transport station point data obtains；

Fig. 4 is that the constant due-date type urban area division of a kind of data-driven utilizes Hangzhoupro with method for digging case study on implementation The region Result figure that state city public transit route data obtain；

Detailed description of the invention

The constant due-date type urban area embodiments providing a kind of data-driven divides and method for digging, As it is shown in figure 1, the method includes:

Step 1: utilizing Hangzhou public transport station point data to set up public traffic network, d value takes 800 here, for often One point is to (u, v), if (u, v) less than d, then (u v), ultimately forms public transport links to distance distance to add limit edge Network.

Public transport line and station data are as follows.

Have studied public traffic station and the route of three types herein: common public transport, bus rapid transit and subway.At us In the data set obtained, directly contain 8255 websites of 641 the common public bus networks in February, 2015 Hangzhou, and Article 14,446 site information of bus rapid transit circuit, its form is as shown in table 1.

Table 1 public bus network and station data form

Tab.1Format of bus dataset

Owing to this data set lacking the relevant information of subway, and the subway work that performer is indispensable in TOD develops With, so we are manually added subway line totally 127 subway stations, Hangzhou 1,2,4,5 and 6, and relevant important information, Such as, longitude and latitude.

Step 2: according to Hangzhou public traffic network, form the set T of point set.

Each connected component in traversing graph, the when of the connected component that every time traversal is new, generates null set S, will be all over The node gone through joins set S, the when that this connected component traversal terminating, is added by set S in the set T of set.

Step 3: each point set is checked whether and meets expectation.Do not meet and adjust.Comprise the following steps:

3.1) for the node in each set, if there is the distance between two nodes more than 2d, then it is believed that be somebody's turn to do Node in set, at least can be divided into two set, then perform 3.2)；If do not existed, then it is believed that in each set All nodes are no more than d from its center, then point set meets expectation, perform step 4)

3.2) using two farthest for distance in this set nodes as initialized barycenter in k-means algorithm, then exist This set performs k-means algorithm, forms two bunches.

3.3) delete original some set, two bunches are added in T as newly-generated point set, continues checking for new collection Close.

Step 4: according to the formula at each some concentration center:

$\ln g\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^n\ln g\left(i\right);lat\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^{n}lat\left(i\right);$

Extract the center of each point set.

Step 5: according to formula r_i=x ∈ X | D (x, p_i)≤D(x,p_j), i, j={1,2 ..., R}, i ≠ j}, dividing regions Border, territory.

Step 6: utilize public transit route data to build limit between zones, obtains Local Area Network concrete steps:

We give the quality of every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and numerical value reduces successively, then by the region at continuous two stations of all routes, as two nodes, set up one corresponding The limit of weights, if continuous the two of route stand in the same area, then ignores.Here w is taken_k=1, w_n=2, w_m=3.

Step 7: zoning ranking concrete steps:

PageRank algorithm based on random walk model is applied in regional relation network, as zoning by we The basis of importance.

7.1) RandomWalk value (the being called for short RW value) RW in given all R regions (i.e. node)_i, and meet (0)

7.2) iteration calculates, and the formula calculating kth step is as follows:

${\mathrm{RW}}_i\left(k\right)=d\underset{j=1}{\overset{R}{\Σ}}{s}_{i,j}\×{\mathrm{RW}}_j(k-1)+\frac{1-d}{R}$

Wherein, d is damped coefficient, typically takes 0.85.By s_i,jThe matrix S of the R row R row formed commonly referred to as shifts square Battle array.

7.3) when RW (k) RW (k-1) absolute value and less than threshold value W, stop iterative computation, what W took here is All and less than 10^-16。

7.2) being specifically defined of transfer matrix in:

7.2.1) importance is connected:

We give the quality of every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and numerical value reduces successively.So, in region connects network, node v_iWith node v_jConnection importance w (v_i,v_j) can Calculated by equation below.

w(v_i,v_j)=m_i,jw_m+n_i,jw_n+k_i,jw_k

7.2.2) transfer matrix:

After determining connection importance, account for destination node according to each limit importance and go out the ratio of the total importance in limit and carry out generally Rate shifts, the element s in transfer matrix S after i.e. improving_i,jCalculate according to equation below,

Wherein, w (v_i,v_j) represent node v_iWith node v_jBetween connection importance, can be by 7.2.1) in formula calculate Go out, gather N (v_j) it is node v_jThe set of neighbor node, i.e. with node v_jThe node being connected.

Step 8: output ranking top n region is as TOD region, and N takes 50 here.

Fig. 3 is the region division result obtained according to Hangzhou public transport station point data.645 marked off in Fig. 3 Area size is moderate, and the region area being positioned at bustling and remote location is more or less the same, and comes from polygonal center and border See, also comply with the zone boundary in actual geographic situation, such as figure and meet the trend in the Qiantang River.Knowable to experimental results, It is proposed that the region partitioning method problem that can effectively solve redundancy station based on connected component cluster, it is possible to mark off Reasonably region, and it also avoid the problem such as determination and unstable result of K value in original clustering algorithm.

Fig. 4 is the region Result obtained according to Hangzhou public transit route data.Figure 4, it is seen that base The most new cities at different levels in urban planning can be identified, in entirety in the Random Walk Algorithm connecting importance guiding The scattered effect simultaneously having reached localized clusters, is more consistent with reality.

Claims (9)

1. the constant due-date type urban area of a data-driven divides and method for digging, it is characterised in that: include as follows Step:

1) for the distance public traffic station less than d, setting up a limit, obtain public traffic network, d is the threshold pre-set Value；

2) according to 1) in public traffic network, extract the point set S in each connected component, join the set T of point set S In；

3) each point set is checked whether meet expectation；Do not meet and adjust；

4) calculate the center of each concentration, obtain regional center；

5) according to 4) in regional center, in region point belong to closest regional center, obtain zone boundary；

6) utilize public transit route data to build limit between zones, obtain Local Area Network；

7) according to 6) in Local Area Network, carry out zoning ranking；

8) output top n region is as TOD region.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 1 divides and method for digging, its It is characterised by: described step 1) concrete steps:

Using each website as a node, a limit is set up for any two distance website less than d, the most whole public Transportation network can be formed multiple connected component.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 1 or 2 divides and method for digging, It is characterized in that: described step 2) concrete steps:

Each connected component in traversing graph, the when of the connected component that traversal is new every time, generates null set S, by traversal Node joins set S, the when that this connected component traversal terminating, is added by set S in the set T of set.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 3 divides and method for digging, its It is characterised by: described step 3) each point set is checked whether meet expectation；Do not meet and adjust；Comprise the following steps:

3.1) for the node in each set, if there is the distance between two nodes more than 2d, then it is believed that this set In node, at least can be divided into two set, then perform 3.2)；If do not existed, then it is believed that all in each set Node is no more than d from its center, then point set meets expectation, performs step 4)

3.2) using two farthest for distance in this set nodes as initialized barycenter in k-means algorithm, then at this collection Conjunction performs k-means algorithm, forms two bunches；

3.3) delete original some set, two bunches are added in T as newly-generated point set, continues checking for new set.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 4 divides and method for digging, its It is characterised by: described step 4) calculate the formula at each some concentration center:

$\ln g\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^n\ln g\left(i\right);lat\left(center\right)=\frac{1}{n}{\mathrm{\Σ}}_i^{n}lat\left(i\right);$

Number of nodes during wherein n is region, lat (i) is the latitude of i-th node, and lng (i) is the longitude of i-th node； Lng (center) and lat (center) is the position at the center obtained.

6. the constant due-date type urban area of a kind of data-driven as described in claim 4 or 5 divides and method for digging, It is characterized in that: described step 5) obtain the formula of zone boundary:

R polymerization website (i.e. candidate centers) P={p that we will obtain from clustering algorithm based on connected component₁,p₂,…, p_RAs seed, whole survey region X is carried out Voronoi division, thus forms R disjoint Voronoi polygon, One polygon be exactly one to be polymerized website p_iCentered by TOD candidate Development area r_i, under formal definition such as formula institute；

r_i=x ∈ X | D (x, p_i)≤D(x,p_j), i, j={1,2 ..., R}, i ≠ j}

In above formula formula, we use euclidean metric as distance function D therein.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 6 divides and method for digging, its It is characterised by: described step 6) utilize public transit route data to build limit between zones, obtain Local Area Network concrete steps:

The quality giving every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and number Value reduces successively, then by the region at continuous two stations of all routes, as two nodes, sets up the limit of a corresponding weight value, If continuous the two of route stand in the same area, then ignore.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 7 divides and method for digging, its It is characterised by: described step 7) zoning ranking concrete steps:

PageRank algorithm based on random walk model is applied in regional relation network by we, important as zoning The basis of property；

7.1) RandomWalk value RW in given all R regions (i.e. node)_i, and meet (0)7.2) repeatedly In generation, calculates, and the formula calculating kth step is as follows:

${\mathrm{RW}}_i\left(k\right)=d\underset{j=1}{\overset{R}{\Σ}}{s}_{i,j}\×{\mathrm{RW}}_j(k-1)+\frac{1-d}{R}$

Wherein, d is damped coefficient, typically takes 0.85；By s_i,jThe matrix S of the R row R row formed is referred to as transfer matrix；

7.3) when RW (k) RW (k-1) absolute value and less than threshold value W, stop iterative computation.

The constant due-date type urban area of a kind of data-driven the most as claimed in claim 7 or 8 divides and method for digging, It is characterized in that: described 7.2) in being specifically defined of transfer matrix:

7.2.1) importance is connected:

The quality giving every subway line is w_k, the quality of bus rapid transit line is w_n, the quality of common public transport line is w_m, and number Value reduces successively；So, in region connects network, node v_iWith node v_jConnection importance w (v_i,v_j) by equation below Calculate；

w(v_i,v_j)=m_i,jw_m+n_i,jw_n+k_i,jw_k

7.2.2) transfer matrix:

After determining connection importance, account for destination node according to each limit importance and go out the ratio of the total importance in limit and carry out probability and turn Move, the element s in transfer matrix S after i.e. improving_i,jCalculate according to equation below,

Wherein, w (v_i,v_j) represent node v_iWith node v_jBetween connection importance, by 7.2.1) in formula calculate, set N(v_j) it is node v_jThe set of neighbor node, i.e. with node v_jThe node being connected.