CN104794164B - Method based on the social parking demand of data identification settlement parking stall matching of increasing income - Google Patents
Method based on the social parking demand of data identification settlement parking stall matching of increasing income Download PDFInfo
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Abstract
The invention discloses a kind of method based on the social parking demand of data identification settlement parking stall matching of increasing income, comprise the following steps：The excavation and arrangement of S1, data；S2, using Baidu's LBS open platforms to employment data carry out address resolution；S3, once judge：Various types of data is subjected to space dropping place by generalized information system in the base map of traffic zone respectively, and coordinate correction is carried out to it and is collected；S4, secondary judgement：Live to be higher than than the space cell higher than setting value and number of employees distribution density the space cell of whole district's averag density by screening duty respectively, obtain meeting the potentiality location that matching demand is lived in duty；S5, the settlement parking stall distribution for identifying the social parking demand of matching and its coverage.The present invention is identified by data of increasing income, matching location potential to city, and social parking demand is matched with settlement parking stall, saves the social cost that information publisher searches for potential target settlement, and auxiliary realizes the space resources optimization for parking of staggering the time.
Description
Technical field
It is more particularly to a kind of based on data identification settlement parking stall matching of increasing income the present invention relates to matched research field of stopping
Social parking demand, save the method that information publisher searches for the social cost of potential target settlement.
Background technology
Under the ever-increasing background of car, the high density employment area in city is generally faced with parking stall deficiency in the daytime
The problem of, and relatively vacant parking stall on human Settlements daytimes then provides possibility to alleviate city parking problem.It is existing to stop
Main problem existing for car APP is the deficiency in parking space information source, and the supply and demand that potential target community stops in the daytime with employment area is closed
System can not be resolved so that information publisher is difficult to search for potential target settlement, constrains the possibility that parking stall timesharing is shared
Property.
With the high speed development of information-intensive society, the parking matching of employment-inhabitation is expected to give reality by multiple technologies means
It is existing.Urban employment spacial analytical method and Employment network index system of the Wu Xiao (2014) based on Employment network visual angle, are constructed
A kind of simply and intuitively Employment network correlation model, analyzes urban employment space, this is the present invention provides inspiration
The thinking of property.Zhao Nan (2008) carries out the velocity amplitude in GPS traffic flow data samplings prediction section by Floating Car.Zhang Jianfeng
(2014) a kind of parking method and system of intelligent and high-efficiency rate are proposed based on cloud computing and big data.Ji Liju (2007) is based on nothing
The multilevel city parking inducible system of line transmission, using GPRS cordless communication networks, gathers parking position information of park in city, leads to
The three-level system that releases news is crossed intuitively to guide parking behavior.Ji Li (2013) to Parking position querying method into
Row research, and propose a kind of real time inquiry system.In intelligent society, carry out Urban Traffic Planning auxiliary using big data and set
The technical solution feasible in respect of proposition science is helped.But above-mentioned technical proposal ignores and the potential settlement in parking space information source is searched
Rope, is not fully solved the problem of parking resource makes full use of yet.
Since there is the supply that certain probability realizes parking stall in the daytime in potential settlement, as long as having foot in high density area
Enough settlements are it is ensured that certain supply, and there is absolute parking demand in highdensity employment area, therefore, passes through
The location of employment-inhabitation differentiates, can reduce the searching cost that potential target settlement is identified similar to the information such as APP publisher,
What raising parking stall timesharing was shared realizes probability.
The content of the invention
The shortcomings that it is a primary object of the present invention to overcome the prior art and deficiency, there is provided one kind is based on data identification of increasing income
The method of the social parking demand of settlement parking stall matching, matching location potential to city are identified, and society is matched with settlement parking stall
Parking demand, saves the social cost that information publisher searches for potential target settlement, and final auxiliary realizes the space for parking of staggering the time
Resource optimization.
In order to achieve the above object, the present invention uses following technical scheme：
Based on the method for the social parking demand of data identification settlement parking stall matching of increasing income, comprise the following steps：
The excavation and arrangement of S1, data：To increase income, network data is combined work with business data, resident population's data
For key data, it is aided with the network data crawl of cell and parking facility；
S2, using Baidu's LBS open platforms carry out business data address resolution, and by GIS software by network data
Space dropping place and coordinates correction are uniformly carried out with enterprise, resident population's data, weighted superposition multidimensional data is potential with Comprehensive Assessment
Match location；
S3, once judge：After the collection and the processing of working base map for completing basic data, Various types of data is passed through
Generalized information system carries out space dropping place in the base map of traffic zone respectively, and carries out coordinate correction to it and collect；
S4, secondary judgement：Lived respectively by screening duty than the space cell and number of employees distribution density higher than setting value
Higher than the space cell of whole district's averag density, obtain meeting the potentiality location that matching demand is lived in duty；
S5, be oriented to parking area judgement：Potential matching location is selected, obtains the dependency number in residential quarter and parking lot in region
According to then identifying the distribution of settlement parking stall and its coverage of the social parking demand of matching.
Preferably, in step S1, the network data of increasing income includes Sina weibo data, and the Sina weibo data pass through
Python instruments capture, and concretely comprise the following steps：
S1.1.1, the public open platform account of application Sina, create and apply and obtain App Key and App Secret；
S1.1.2, calculate the dynamic beginning and ending time stamp of search microblogging using Python instruments；
S1.1.3, in the Config files of Python instruments, configure center point coordinate, the beginning and ending time of search range
Stamp, search radius, save file；
S1.1.4, utilize IDLE tool open weibo with pois files, operation acquisition microblog data, by acquisition
Data carry out obtaining temporal information, the geography information of microblogging after coded format conversion.
Preferably, in step S2, it is to the step of business data progress address resolution and coordinates correction：
S2.1.1, submit application, obtains the api interface key of Baidu's LBS open platforms；
S2.1.2, the interface secret key obtained according to relevant parameter requirement and previous step, writing URL for address resolution please
Ask, enterprise address is converted into corresponding web site；
S2.1.3, the address resolution URL request network address write according to previous step by enterprise's address information, batch import
Into Locoy Spider softwares, and after setting collection label task, text template, by Locoy Spider instruments to enterprise
Industry address carries out the address resolution of batch, so as to get the geographic coordinate information of each enterprise.
Preferably, in step S2, resident population's data processing is concretely comprised the following steps：
S2.2.1, after rejecting non-constructive land in each street, by the resident population's data counted in the unit of street divided by
Each street area, calculates the density of population in each street of institute's survey region；
The area for each traffic zone space cell that S2.2.2, computation partition come out；
S2.2.3, by each traffic zone space cell area with its residing for the street density of population be multiplied, estimation obtains each
Resident population's quantity of traffic zone.
Preferably, in step S2, the network data excavation based on LBS open platforms concretely comprises the following steps：
S2.3.1, the API keys for obtaining Baidu's open platform；
S2.3.2, the URL request parameter that object search is set；
Whether S2.3.3, open the URL request parameter set in a browser, examine URL request parameter qualified, if
The quantity of object search exceedes setting bar number, then needs to reduce search range again；
S2.3.4, will spell in qualified URL batch importing Locoy Spider softwares, set collection label and text mould
Plate, operation obtain related data.
Preferably, in step S3, the specific method once judged is：
Various types of data key element, be standardized by S3.1
For the different consideration of the dimension to Various types of data key element, the order of magnitude, the specific method of standardization is as follows, if
Quantity of the jth item data key element in space cell i is αij, define first：
Wherein, the n in formula (1) represents that studying city shares n traffic zone unit, then i=1,2 ..., n, data
Key element j=1,2,3,4；
It should be noted that work as eijWhen=0, ln (eij) meaningless, therefore calculating standard value PijWhen, need to be to eijCarry out
Correct, thus in each unit redefined different key elements standard value Pij：
Pij=eij·ln(eij) (2)
The duty of S3.2, calculating each unit live to compare
After Various types of data is standardized, further, respectively by microblogging day data and night data, enterprise
Industry number of employees and resident population's data carry out duty and live to match, so that the two class duties that each traffic zone unit is calculated live to compare
βij, calculation procedure is as follows：
Or
I=1 in formula, 2 ..., m, j=1,2；In addition, WBetween i daysThe microblogging day of traffic zone unit i after expression standardization
Between data, WI nightsThe microblogging night data of traffic zone unit i after expression standardization；QiTraffic after expression standardization
Enterprise's number of employees of cell unit i, RiResident population's data of traffic zone unit i after expression standardization；
S3.3, calculate the objective weight that ratio is lived in all kinds of duties using average variance method
The average value of two class key elements is calculated first：
Then, the standard deviation that ratio is lived in two class duties is calculated：
I=1 in formula, 2 ..., m, j=1,2；
Finally, the objective weight of two class key elements is calculated：
S3.4, calculate the subjective weight that ratio is lived in all kinds of duties using Te Feierfa；
The data source accuracy of ratio is lived according to two class duties and the degree of association is lived in duty, and two class duties are lived than carrying out using Delphi
Importance is judged, and obtains the subjective weight that ratio is lived in two class duties, calculation formula is：
I=1 in formula, 2 ..., n, j=1,2；Wherein KijThe weight that ratio is lived for jth class duty for i-th of people is judged；
S3.5, calculate the comprehensive weight that ratio is lived in two class duties；
On the basis of the above, by calculating the subjective comprehensive weight relation for being worth to two class key elements with objective weight：
The synthesis duty of S3.6, calculating each unit live to compare；
The synthesis duty that two class duties are lived to obtain than according to its weight relationship, carrying out integrating superposition to each space cell lives to compare：
I=1 in formula, 2 ..., m, j=1,2.
Preferably, in step S4, the specific method of secondary judgement is：
S4.1, obtain each space cell duty live compare on the basis of, by γi>=0.8 space cell, which is considered as, meets duty
Firmly match the potentiality location of demand；
S4.2, the number of employees distribution density (ρ by calculating each unit in business datai) and whole region distribution density
Average value (ρ), the potentiality that duty lives matching demand are identified as conforming to by number of employees distribution density higher than the space cell of average value
Location：
I=1 in formula, 2 ..., m, in addition, PiFor the number of employees of space cell, SiFor the area of space cell；
S4.3, screening duty live to compare γi>=0.8 and number of employees distribution density ρiThe space cell of >=ρ, so as to be matched
The potentiality space cell of demand.
Preferably, in step S5, being oriented to the specific method that parking area judges is：
S5.1, using Baidu open platform obtain the related data in residential quarter and parking lot in region, by two class data
Space dropping place and coordinate correction are carried out in generalized information system, and intersects this with the residential estate in matching parking demand unit
The parking facility of unit and residential quarter distribution；
S5.2, filter out by network inquiry the built-in adult in area on behalf of later cell in 2000, and accordingly filters out this
The parking lot of class residential quarter, so as to obtain the residential quarter parking lot with matching parking demand potentiality；
S5.3,300 meters and 500 meters of service radiuses for calculating the residential quarter parking lot for meeting matching parking demand respectively,
Obtain satisfactory I grade of potential Parking in residential area matching location and II grade of potential Parking in residential area matching location in area.
Preferably, before step S1, the step of obtaining working base map is further included, its specific method is：
Administrative division border, traffic network, land character, the physical features border of institute's survey region are obtained, is painted using CAD
System divides the base map of traffic zone, and the base map file of shp forms is converted in GIS software, and whole region is divided,
Finally obtain the working base map behind institute's survey region division traffic zone.
Preferably, in step S4, the setting value is 0.8.
Compared with prior art, the present invention having the following advantages that and beneficial effect：
1st, the present invention using the space cell of urban transportation cell as working base map, spatially overlap Sina weibo in the daytime,
The data at night and the working demographic data of enterprise, resident population's data, calculate duty and live matching relationship respectively, and by square
After poor method and Delphi calculating weight, Comprehensive Assessment is carried out to potential matching location, identifies that comprehensive duty is lived than being higher than 0.8
Duty lives to mix plot.Further, block of the social parking demand higher than average value is identified, so as to screen to obtain highly dense
Potential matching location is lived in the duty of degree, and settlement therein is screened, and will be built up age newer Parking in residential area field and fallen
Position, realizes that the facility supply that society's parking is matched with settlement parking stall is suggested.
2nd, the settlement in the potential matching location of present invention identification, and based on the stronger social parking demand in these locations and
Relation is lived in duty, propose the social parking demand of settlement parking stall matching, save information publisher search for the society of potential target settlement into
This method, so as to fulfill the resource distribution of parking facility.
Brief description of the drawings
Fig. 1 is the specific implementation flow chart of the present invention；
Fig. 2 is division figure in traffic zone of the present invention；
Fig. 3 is that ratio distribution map is lived in the duty of the invention based on data of increasing income；
Fig. 4 is the number of employees density profile of the invention based on business data；
Fig. 5 is the potentiality space cell distribution map of Parking in residential area matching demand of the present invention based on data of increasing income；
Fig. 6 is I grade and II grade potential Parking in residential area matching location distribution map of the present invention based on data of increasing income.
Embodiment
With reference to embodiment and attached drawing, the present invention is described in further detail, but embodiments of the present invention are unlimited
In this.
Embodiment
As shown in Figure 1, method of the present embodiment based on the social parking demand of data identification settlement parking stall matching of increasing income, including
Following step：
(1) preparation base map and traffic zone is divided；
The present invention is superimposed the center line of present situation road network using the border in city (or region) as working base map in CAD,
And traffic zone is further divided according to technical standard.
With reference to the criteria for classifying of domestic and international traffic zone, general urban central zone traffic zone area is 1-3 squares of public affairs
In, the area of Urban Marginal Areas traffic zone is then 5-15 square kilometres, in addition the higher density of population of Chinese city and
More complicated land character, each block is combined and actually divided in this research method, and the traffic of local center built-up areas is small
Area's area can take smaller value according to city road network, and urban periphal defence then generally takes higher value.In addition, this patent traffic zone divides
Border is boundary according to road axis, and substantially based on major trunk roads road network, region limit is carried out supplemented by the subsidiary road road network of some areas
It is fixed, while consider the factors such as natural boundary and geological location, and the principle not across administrative line is followed substantially.
The administrative division border of institute survey region, traffic network, land character, physical features border etc. are obtained, utilizes CAD
The base map of division traffic zone is drawn, and the base map file of shp forms is converted in GIS software, according to the original of previous step
Then whole region is divided, finally obtains the working base map behind institute's survey region division traffic zone.
(2) excavation and arrangement of data；
This research method network data that will increase income is combined as key data with business data, resident population's data, auxiliary
Captured with cell and the network data of parking facility, abundant data source.
In addition, address resolution is carried out to business data using Baidu's LBS open platforms, and by GIS software by network number
Space dropping place and coordinates correction are uniformly carried out according to enterprise, resident population's data, weighted superposition multidimensional data is dived with Comprehensive Assessment
In matching location.
1. capture Sina weibo text data with python instruments；
The present invention is by using the microblogging api interface in Sina's development platform and geography information interface (http://
Open.weibo.com/wiki/2/place/nearby_timeline), the microblogging dynamic in a period of time inner region is obtained,
Including the time of microblogging renewal, geography information, text message etc..It is special by the spatial distribution round the clock for analyzing microblogging text
Sign, obtain in the range of duty live distribution situation.Concrete operation step is：
(1) apply for the public open platform account of Sina, create and apply and obtain App Key and App Secret；
(2) the Python instruments write using author calculate the dynamic beginning and ending time stamp of search microblogging, it is necessary to specified otherwise
, limited by microblogging api interface, it is proposed that the microblogging dynamic of search nearly one week or so；
(3) in the Config files of Python instruments, the center point coordinate of search range is configured, the beginning and ending time stabs, searches
The parameters such as rope radius (being not more than 11132 meters), save file；
(4) the weibo with pois files write using IDLE tool open authors, operation obtain microblog data, will
The data of acquisition carry out obtaining temporal information, geography information of microblogging etc. after coded format conversion；Need to illustrate herein
, by the Interface limits of Sina weibo, each account can only obtain 1000 microblog datas for one hour, and each hour empties one
It is secondary.
2. carrying out address resolution to business data, the number of employees data of enterprise are arranged；
According to the business directory of industrial and commercial bureau's registration statistics, the business data of institute's survey region is filtered out, wherein paying close attention to
One item data of number of employees of each enterprise.
The address resolution of business data can be got each based on the Geocoding API functions in Baidu's open platform
The geographic coordinate information of enterprise.Due to from the coordinate information acquired in Baidu's LBS open platforms there are certain error, in the later stage
, it is necessary to carry out certain screening and rectification work to geographical coordinate in data processing and space dropping place.Enterprise's address resolution and seat
Mark correction comprises the following steps that：
(1) application is submitted, obtains the api interface key of Baidu's LBS open platforms；
(2) interface secret key obtained according to relevant parameter requirement and previous step, URL request is write for address resolution,
Enterprise address is converted into corresponding web site；
(3) the address resolution URL request network address write according to previous step by enterprise's address information, imported into batches
In Locoy Spider softwares, and after setting collection label task, text template etc., by Locoy Spider instruments to enterprise
Industry address carries out the address resolution of batch, so as to get the geographic coordinate information of each enterprise.
3. arrange resident population's data；
The statistics of national resident population is often counted by space cell of street, and in this method then with accordance with
The traffic zone space cell of standard division is as working base map, and the division of traffic zone is followed and avoided across street substantially
The principle in Administrative boundaries boundary line.Therefore, exclude the natural farmland in street, after massif, resident population's data are approx seen
Work is uniformly distributed in each street unit, and the inhabitation people of each traffic zone in institute's survey region is estimated with the density of population divided equally
Mouth quantity, comprises the following steps that：
(1) after rejecting the non-constructive lands such as natural farmland in each street, massif, the inhabitation people that will be counted in the unit of street
Mouth data divided by each street area, calculate the density of population in each street of institute's survey region；
(2) area for each traffic zone space cell that computation partition comes out；
(3) by each traffic zone space cell area with its residing for the street density of population be multiplied, estimation obtain each traffic
Resident population's quantity of cell.
4. the network data excavation based on LBS open platforms；
The present invention utilizes Baidu's open platform, obtains the geography information in a certain range of " parking lot " and " cell ", leads to
Cross the residential quarter for screening and analyzing potential matching location and the spatial distribution in community parking lot and year is built up in residential quarter
For distribution situation, integrated judgment is oriented to the distribution of parking settlement.Wherein the network data excavation step based on LBS open platforms is such as
Under：
(1) the API keys of Baidu's open platform are obtained；
(2) the URL request parameter of object search is set, it is main to include search key, object search title (parking lot and small
Area), search range (latitude and longitude coordinates of rectangular extent) etc.；
(3) the URL request parameter set is opened in a browser, examines URL request parameter whether qualified, if search
The quantity of object then needs to reduce search range again more than 760；
(4) qualified URL batches will be spelt to import in Locoy Spider softwares, collection label and text template are set,
Operation obtains related data.The main information of data includes facility name, latitude and longitude coordinates, facility address etc..
(3) potential matching location judges
1. once judge
After completing the collection of basic data and the processing of working base map again, Various types of data is existed respectively by generalized information system
Space dropping place is carried out in the base map of traffic zone, and coordinate correction is carried out to it and is collected.According to microblog data, business data and residence
Firmly demographic data, by 08:00-18:00 microblogging day data and enterprise's number of employees is classified as employed population distributed data, will
18:00-08:00 microblogging night data and census data are classified as the distribution of resident population, utilize average variance method and Te Fei
Ratio distribution situation is lived in the synthesis duty that each unit is calculated in your method, so as to complete to judge the first time in potential matching location.
Comprise the following steps that：
(1) Various types of data key element is standardized
For the different consideration of the dimension to Various types of data key element, the order of magnitude.The specific method of standardization is as follows, if
Quantity of the jth item data key element in space cell i is αij, define first：
Wherein, the n in formula (1) represents that studying city (or region) shares n traffic zone unit, then i=1,
2 ..., n, Data Elements j=1,2,3,4.
It should be noted that work as eijWhen=0, ln (eij) meaningless, therefore calculating standard value PijWhen, need to be to eijCarry out
Correct, thus in each unit redefined different key elements standard value Pij：
Pij=eij·ln(eij) (2)
(2) duty for calculating each unit lives to compare
After Various types of data is standardized, further, respectively by microblogging day data and night data, enterprise
Industry number of employees and resident population's data carry out duty and live to match, so that the two class duties that each traffic zone unit is calculated live to compare
βij, calculation procedure is as follows：
Or
I=1 in formula, 2 ..., m, j=1,2.In addition, WBetween i daysThe microblogging day of traffic zone unit i after expression standardization
Between data, WI nightsThe microblogging night data of traffic zone unit i after expression standardization；QiTraffic after expression standardization
Enterprise's number of employees of cell unit i, RiResident population's data of traffic zone unit i after expression standardization.
(3) objective weight of ratio is lived using all kinds of duties of average variance method calculating
The average value of two class key elements is calculated first：
Then, the standard deviation that ratio is lived in two class duties is calculated：
I=1 in formula, 2 ..., m, j=1,2.
Finally, the objective weight of two class key elements is calculated：
(4) the subjective weight of ratio is lived using all kinds of duties of Te Feierfa calculating.
The data source accuracy of ratio is lived according to two class duties and the degree of association is lived in duty, and two class duties are lived than carrying out using Delphi
Importance is judged, and obtains the subjective weight that ratio is lived in two class duties, calculation formula is：
I=1 in formula, 2 ..., n, j=1,2.Wherein KijThe weight that ratio is lived for jth class duty for i-th of people is judged.
(5) comprehensive weight that ratio is lived in two class duties is calculated.
On the basis of the above, by calculating the subjective comprehensive weight relation for being worth to two class key elements with objective weight：
(6) the synthesis duty for calculating each unit lives to compare.
The synthesis duty that two class duties are lived to obtain than according to its weight relationship, carrying out integrating superposition to each space cell lives to compare：
I=1 in formula, 2 ..., m, j=1,2.
2. secondary judgement
It is higher than whole district's averag density than the space cell higher than 0.8 and number of employees distribution density by screening duty and living respectively
Space cell, obtain meeting the potentiality location that matching demand is lived in duty.Comprise the following steps that：
First, on the basis of ratio is lived in the duty for obtaining each space cell, by γi>=0.8 space cell, which is considered as, meets duty
Firmly match the potentiality location of demand.
Then, by calculating the number of employees distribution density (ρ of each unit in business datai) and whole region distribution density
Average value (ρ), the potentiality that duty lives matching demand are identified as conforming to by number of employees distribution density higher than the space cell of average value
Location：
I=1,2 in formula ..., m.In addition, PiFor the number of employees of space cell, SiFor the area of space cell.
Finally, duty is screened to live to compare γi>=0.8 and number of employees distribution density ρiThe space cell of >=ρ, so as to be matched
The potentiality space cell of demand.
(4) parking settlement is oriented to judge
Herein on basis, the related data in residential quarter and parking lot in region is obtained using Baidu's open platform, will
Two class data carry out space dropping place and coordinate correction in generalized information system, and with matching the residential estate phase in parking demand unit
Hand over the parking facility for obtaining the unit and residential quarter distribution.
Then, the built-in adult in area is filtered out on behalf of later cell in 2000 by network inquiry, and accordingly filters out this
The parking lot of class residential quarter, so as to obtain the residential quarter parking lot with matching parking demand potentiality.
Finally, 300 meters and 500 meters of service radiuses in the residential quarter parking lot for meeting matching parking demand are calculated respectively,
Obtain satisfactory I grade of potential Parking in residential area matching location and II grade of potential Parking in residential area matching location in area.
(5) example operation
It is example operation scope that the present invention, which have chosen reported in Tianhe district of Guangzhou, by analyzing the business data in Tianhe District, occupying
Firmly demographic data and microblogging text data etc., utilize mean square deviation, special fell's method and the data in related residential quarter and its parking lot
Having obtained potential Parking in residential area matching location, whole process in the range of Tianhe District includes preparation, the potential matching of basic data
The judgement in location and three parts of judgement for being oriented to parking settlement.
5.1st, basic data prepares；
According to the present situation road network and Administrative boundaries of Tianhe District, according to the general division principle of traffic zone by Milky Way zoning
81 space cells are divided into, the working base map as follow-up study analysis.Herein it should be strongly noted that due to the present invention
Mainly research employment and the parking demand lived, so the non-constructive land such as stove mountain, South China Botanical Garden is not originally being ground in area
In the range of studying carefully.
46217 business data in the range of Tianhe District are obtained by screening, using LBS open platforms to it into row address solution
Data, are carried out space dropping place and coordinate correction by analysis in generalized information system.
By calculating the quantity of each street resident population and the ratio of each street area, the resident population point in each street is obtained
Cloth density, resident population's quantity of each space cell is obtained further according to resident population's distribution density of each space cell.Need herein
It should be particularly noted that, in view of the situation of population distribution inequality, non-build is not used when calculating resident population's distribution density
Take into account on ground.
Using Python instruments, by continuously searching for obtain the microblogging text data in the range of Tianhe District, Tianhe District is obtained
Interior 107859 microblogging text datas, and space dropping place and coordinate correction are carried out in generalized information system.By 08:00-18:00 it is micro-
Rich day data and enterprise's number of employees are classified as employed population distributed data, by 18:00-08:00 microblogging night data and people
Mouth census data is classified as the distribution of resident population.
5.2nd, the judgement in potential matching location；
Herein on basis, using formula (1) and formula (2) by the microblog data in Tianhe District, business data and inhabitation
Demographic data is standardized respectively, recycle formula (3) can obtain duty of the Tianhe District based on microblog data live than and
Ratio is lived in duty based on enterprise, resident population's data.
Then, using average variance method, the visitor that two class duties live ratio is calculated according to formula (4), formula (5) and formula (6)
See weight.Using special fell's method, the subjective weight that two class duties live ratio can be calculated according to formula (7), weighed using formula (8)
The superposition of weight, finally obtains the comprehensive weight relation that ratio is lived in two class duties：ω1=0.733 and ω2=0.267.
On this basis, two class duties are lived than being overlapped according to comprehensive weight relation, so as to obtain day using formula (9)
River reach duty is lived than distribution map (as shown in Figure 2).
According to the number of employees distribution situation of business data in each space cell and the area of each space cell, according to formula
(10) Density Distribution (as shown in Figure 3) that the ratio of the two is worth to number of employees is calculated, and unit number of employees density is higher than
The be averaged space cell of number of employees density of the whole district screens, the potential matching location intensive as employed population.
In addition, being lived obtaining Tianhe District duty in the distributed basis of ratio, duty is lived and is filtered out than the space cell more than 0.8
Come, overlapped with space cell of the number of employees density higher than average value, finally obtained and met Parking in residential area in Tianhe District
The potentiality space cell (as shown in Figure 4) of matching demand.
5.3rd, it is oriented to the judgement of parking settlement；
After obtaining meeting in Tianhe District the potentiality space cell of Parking in residential area matching demand, the inhabitation in unit is used
Ground screens, the analyst coverage as analysis parking settlement.In addition, obtained using Baidu's open platform in the range of Tianhe District
Residential quarter and the distribution situation in residential quarter parking lot, will build up after 2000 in potentiality space cell in residential estate
Cell screen, make the 300 meters of service radiuses and 500 meters of service radiuses of such community parking field respectively, finally obtain day
The potential matching settlement in I grade of river reach and II grade of potential matching settlement (as shown in Figure 5, Figure 6).
Above-described embodiment is the preferable embodiment of the present invention, but embodiments of the present invention and from above-described embodiment
Limitation, other any Spirit Essences without departing from the present invention with made under principle change, modification, replacement, combine, simplification,
Equivalent substitute mode is should be, is included within protection scope of the present invention.
Claims (10)
1. the method based on the social parking demand of data identification settlement parking stall matching of increasing income, it is characterised in that comprise the following steps：
The excavation and arrangement of S1, data：It is combined using network data of increasing income with business data, resident population's data and is used as master
Data are wanted, are aided with the network data crawl of cell and parking facility；
S2, using Baidu's LBS open platforms carry out business data address resolution, and by GIS software by network data and enterprise
Industry, resident population's data uniformly carry out space dropping place and coordinates correction, and weighted superposition multidimensional data is with the potential matching of Comprehensive Assessment
Location；
S3, once judge：After the collection and the processing of working base map for completing basic data, Various types of data is passed through into GIS systems
System carries out space dropping place in the base map of traffic zone respectively, and carries out coordinate correction to it and collect；
S4, secondary judgement：It is higher than respectively by screening duty and living than the space cell higher than setting value and number of employees distribution density
The space cell of whole district's averag density, obtains meeting the potentiality location that matching demand is lived in duty；
S5, be oriented to parking area judgement：Potential matching location is selected, obtains the related data in residential quarter and parking lot in region,
Then the distribution of settlement parking stall and its coverage of the social parking demand of matching are identified.
2. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S1, the network data of increasing income includes Sina weibo data, and the Sina weibo data pass through python works
Tool crawl, concretely comprises the following steps：
S1.1.1, the public open platform account of application Sina, create and apply and obtain App Key and App Secret；
S1.1.2, calculate the dynamic beginning and ending time stamp of search microblogging using Python instruments；
S1.1.3, in the Config files of Python instruments, configure search range center point coordinate, the beginning and ending time stamp, search
Rope radius, save file；
S1.1.4, utilize IDLE tool open weibo with pois files, operation acquisition microblog data, by the data of acquisition
Carry out obtaining temporal information, the geography information of microblogging after coded format conversion.
3. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S2, is to the step of business data progress address resolution and coordinates correction：
S2.1.1, submit application, obtains the api interface key of Baidu's LBS open platforms；
S2.1.2, the interface secret key obtained according to relevant parameter requirement and previous step, URL request is write for address resolution,
Enterprise address is converted into corresponding web site；
S2.1.3, the address resolution URL request network address write according to previous step by enterprise's address information, imported into batches
In Locoy Spider softwares, and after setting collection label task, text template, by Locoy Spider instruments to enterprise
Address carries out the address resolution of batch, so as to get the geographic coordinate information of each enterprise.
4. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S2, resident population's data processing is concretely comprised the following steps：
After non-constructive land in S2.2.1, each street of rejecting, by the resident population's data counted in the unit of street divided by each street
Road area, calculates the density of population in each street of institute's survey region；
The area for each traffic zone space cell that S2.2.2, computation partition come out；
S2.2.3, by each traffic zone space cell area with its residing for the street density of population be multiplied, estimation obtain each traffic
Resident population's quantity of cell.
5. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S2, the network data excavation based on LBS open platforms concretely comprises the following steps：
S2.3.1, the API keys for obtaining Baidu's open platform；
S2.3.2, the URL request parameter that object search is set；
Whether S2.3.3, open the URL request parameter set in a browser, examine URL request parameter qualified, if search
The quantity of object exceedes setting bar number, then needs to reduce search range again；
S2.3.4, will spell in qualified URL batch importing Locoy Spider softwares, set collection label and text template,
Operation obtains related data.
6. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S3, the specific method once judged is：
Various types of data key element, be standardized by S3.1
For the different consideration of the dimension to Various types of data key element, the order of magnitude, the specific method of standardization is as follows, if jth
Quantity of the item data key element in space cell i is αij, define first：
<mrow>
<msub>
<mi>e</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>&alpha;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>/</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>&alpha;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, the n in formula (1) represents that studying city shares n traffic zone unit, then i=1,2 ..., n, Data Elements
J=1,2,3,4；
It should be noted that work as eijWhen=0, ln (eij) meaningless, therefore calculating standard value PijWhen, need to be to eijIt is modified,
So as to the standard value P of different key elements in each unit that is redefinedij：
Pij=eij·ln(eij) (2)
The duty of S3.2, calculating each unit live to compare
After Various types of data is standardized, further, respectively by microblogging day data and night data, enterprise from
Industry number and resident population's data carry out duty and live to match, so that the two class duties that each traffic zone unit is calculated live to compare βij, meter
It is as follows to calculate step：
I=1 in formula, 2 ..., n, j=1,2；In addition, WBetween i daysThe microblogging of traffic zone unit i counts in the daytime after expression standardization
According to WI nightsThe microblogging night data of traffic zone unit i after expression standardization；QiTraffic zone after expression standardization
Enterprise's number of employees of unit i, RiResident population's data of traffic zone unit i after expression standardization；
S3.3, calculate the objective weight that ratio is lived in all kinds of duties using average variance method
The average value of two class key elements is calculated first：
<mrow>
<msub>
<mi>Q</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>&beta;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mi>n</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Then, the standard deviation that ratio is lived in two class duties is calculated：
<mrow>
<msub>
<mi>&delta;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<msqrt>
<mrow>
<mfrac>
<mn>1</mn>
<mi>n</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>&beta;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>Q</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
I=1 in formula, 2 ..., n, j=1,2；
Finally, the objective weight of two class key elements is calculated：
<mrow>
<msub>
<mi>&theta;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>&delta;</mi>
<mi>j</mi>
</msub>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<msub>
<mi>&delta;</mi>
<mi>j</mi>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
S3.4, calculate the subjective weight that ratio is lived in all kinds of duties using Te Feierfa；
The data source accuracy of ratio is lived according to two class duties and the degree of association is lived in duty, two class duties is lived using Delphi more important than carrying out
Property judge, obtain the subjective weight that ratio is lived in two class duties, calculation formula is：
<mrow>
<msub>
<mi>&mu;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>K</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mi>n</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
I=1 in formula, 2 ..., n, j=1,2；Wherein KijThe weight that ratio is lived for jth class duty for i-th of people is judged；
S3.5, calculate the comprehensive weight that ratio is lived in two class duties；
On the basis of the above, by calculating the subjective comprehensive weight relation for being worth to two class key elements with objective weight：
<mrow>
<msub>
<mi>&omega;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>&theta;</mi>
<mi>j</mi>
</msub>
<mo>+</mo>
<msub>
<mi>&mu;</mi>
<mi>j</mi>
</msub>
</mrow>
<mn>2</mn>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
The synthesis duty of S3.6, calculating each unit live to compare；
The synthesis duty that two class duties are lived to obtain than according to its weight relationship, carrying out integrating superposition to each space cell lives to compare：
<mrow>
<msub>
<mi>&gamma;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>&beta;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>&omega;</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
I=1 in formula, 2 ..., n, j=1,2.
7. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S4, the specific method of secondary judgement is：
S4.1, obtain each space cell duty live compare on the basis of, by γi>=0.8 space cell, which is considered as, meets duty firmly matching
The potentiality location of demand；
S4.2, the number of employees distribution density ρ by calculating each unit in business dataiWith whole region distribution density average value
ρ, the potentiality location that duty lives matching demand is identified as conforming to by number of employees distribution density higher than the space cell of average value：
<mrow>
<msub>
<mi>&rho;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>&rho;</mi>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>&rho;</mi>
<mi>i</mi>
</msub>
</mrow>
<mi>n</mi>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
I=1 in formula, 2 ..., n, in addition, PiFor the number of employees of space cell, SiFor the area of space cell；
S4.3, screening duty live to compare γi>=0.8 and number of employees distribution density ρiThe space cell of >=ρ, so as to obtain matching demand
Potentiality space cell.
8. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, step
In S5, being oriented to the specific method that parking area judges is：
S5.1, using Baidu open platform obtain the related data in residential quarter and parking lot in region, by two class data in GIS
Space dropping place and coordinate correction are carried out in system, and intersects to obtain the unit with the residential estate in matching parking demand unit
Parking facility and residential quarter distribution；
S5.2, filter out by network inquiry the built-in adult in area on behalf of later cell in 2000, and accordingly filters out such residence
The firmly parking lot of cell, so as to obtain the residential quarter parking lot with matching parking demand potentiality；
S5.3,300 meters and 500 meters of service radiuses for calculating the residential quarter parking lot for meeting matching parking demand respectively, obtain
Satisfactory I grade of potential Parking in residential area matching location and II grade of potential Parking in residential area matching location in area.
9. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, before step S1, further includes the step of obtaining working base map, its specific method is：
Administrative division border, traffic network, land character, the physical features border of institute's survey region are obtained, is drawn and drawn using CAD
Divide the base map of traffic zone, and the base map file of shp forms is converted in GIS software, whole region is divided, finally
Obtain the working base map behind institute's survey region division traffic zone.
10. the method according to claim 1 based on the social parking demand of data identification settlement parking stall matching of increasing income, it is special
Sign is, in step S4, the setting value is 0.8.
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| CN107239756A (en) * | 2017-05-23 | 2017-10-10 | 国家地理空间信息中心 | Density of population analysis system based on high score satellite remote sensing date combination type of ground objects |
| CN110610250B (en) * | 2018-06-15 | 2023-07-07 | 阿里巴巴集团控股有限公司 | Method, device and equipment for recommending and prompting idle parking spaces and vehicle searching route |
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