CN110866051A - System for representing space-time evolution of regional logistics enterprise - Google Patents
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Abstract
The invention discloses a system for representing regional logistics enterprise space-time evolution, which is used for acquiring attribute information of logistics enterprises, converting the attribute information of the enterprises into space-time data information, visualizing the space-time evolution characteristics of the logistics enterprises in a certain time period and a certain region by means of a GIS visualization technology, and predicting the evolution direction of the regional logistics industry in a certain time in the future by a regional space-time evolution analysis module. The method can reasonably predict the future development trend of the logistics enterprise site selection, provide reference and basis for the establishment and site selection of the logistics enterprise, and improve the transportation efficiency and the distribution efficiency of logistics.
Description
Technical Field
The invention relates to the field of positioning of the Internet of things and the field of spatial analysis, in particular to a system for representing space-time evolution of regional logistics enterprises.
Background
With the continuous development of the globalization of economy and the continuous increase of the global freight volume, China, as the most developing country in the world, has a huge increase in freight traffic, and with the increase of freight traffic, the spatial locations of urban spatial structures and logistics facilities are gradually changed. The position of the logistics facility has great influence on the cost and the efficiency of freight transportation, and simultaneously influences the reasonable allocation of logistics resources, so how to make urban logistics space recombination agree with urban space recombination has profound influence on sustainable development vigorously advocated in the society at present. However, the problems of inaccurate positioning, lack of informatization strategy, disordered construction thought and the like exist in the traditional logistics site selection process. By visualizing the space-time evolution of the logistics enterprises in a certain area, the space-time evolution characteristics of the urban logistics enterprises in a certain time period are discovered, visual signs and strategies are provided for future establishment, site selection and development of the logistics enterprises, decision basis is provided for site selection of logistics nodes, and the transportation and distribution efficiency is improved.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a system for representing the space-time evolution of regional logistics enterprises, which can reduce the logistics cost and improve the transportation and distribution efficiency.
The technical scheme adopted by the invention is as follows:
a system for representing the space-time evolution of regional logistics enterprises comprises a data collection module a, a data conversion and processing module b, a regional overall spatial distribution characteristic analysis module c, a regional spatial aggregation analysis module d and a regional spatial distribution cold and hot point analysis module e which are sequentially connected, wherein the regional overall spatial distribution characteristic analysis module c, the regional spatial aggregation analysis module d and the regional spatial distribution cold and hot point analysis module e are in signal connection with a regional space-time evolution analysis module f;
the data collection module a acquires attribute information of the logistics enterprise;
the data conversion and processing module b converts the attribute information of the logistics enterprise into space-time data information and generates a state report for all states of the space-time data information;
the regional overall spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprise in the research region;
the region space gathering analysis module d analyzes the evolution characteristics of the logistics enterprises;
the region space distribution cold and hot point analysis module e is used for identifying hot point regions and cold point regions which are distributed on different spatial positions by the logistics enterprises;
and the region space-time evolution analysis module f is combined with the region overall spatial distribution characteristic analysis module c, the region space aggregation analysis module d and the region space distribution cold and hot spot analysis module e to predict the evolution direction of the region logistics industry in a certain time in the future.
In the above technical solution, the attribute information includes a detailed address, registration time and deregistration time, registration capital, enterprise property and operating range of the logistics enterprise.
In the technical scheme, the time-space data information refers to that detailed addresses of all logistics enterprises are converted into corresponding longitude and latitude coordinates on a geographic space through a map attribute library standard, and classification on a time scale is established according to different registration times and cancellation times of the logistics enterprises.
In the above technical solution, the regional total spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprise in the research region through standard deviation ellipse analysis and center analysis.
In the technical scheme, the region space gathering analysis module d visualizes the evolution characteristics of the logistics enterprises by adopting nuclear density analysis, and the nuclear density analysis is based on the self-adaptive bandwidth h under the condition of average gravity center constraintiThe kernel density function is:
wherein:
for the kernel density value of point x, K () is a weight function, (x-x)i) Points x and x representing the desired density estimateiThe direct distance between the two, omega is the bandwidth parameter;
setting average center of gravity
X and x areiThe direct distance between x and
and xiTo determine, namely:
in the formula:
denotes x and xiThrough the mean center of gravity
The degree of distance correlation of the connections,
represents x and
the degree of correlation of the distance between them,
to represent
And xiThe degree of distance correlation between;
the degree of association of all points in the region is:
the average degree of association is:
comparison of x-xiAnd q, the smaller of the two being x-xiIs combined with a given value of h and substituted into the formula
In the method, the nuclear density value is calculated and then is subject to the formula
Obtaining adaptive bandwidth hi。
In the technical scheme, the space-time evolution of the logistics enterprise is visualized on a map by the regional total spatial distribution characteristic analysis module c, the regional spatial aggregation analysis module d and the regional spatial distribution cold-hot spot analysis module e.
In the above technical solution, the process of acquiring the hot spot area and the cold spot area is as follows: hot spot values of the attention area unit and peripheral units of the attention area unit are calculated through hot spot analysis, and the relevance between adjacent unit values is defined according to the distance D, so that hot spot areas and cold spot areas in the research area are identified, and the hot spot areas and the cold spot areas which are distributed on different spatial positions of the logistics enterprise are determined.
The invention has the beneficial effects that: the invention is based on the information positioning system of the Internet of things, obtains the attribute information of the logistics enterprises, establishes the enterprise attribute database, converts the attribute information in the enterprise attribute database into corresponding time-space data information, visualizes the time-space evolution characteristics of the logistics enterprises in a certain time period and a certain area by means of the GIS visualization technology, and provides reference and reference for future governments and enterprises in the establishment and site selection of the logistics facilities, thereby reducing the transportation cost and improving the distribution service efficiency and quality.
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The invention will now be described by way of example and with reference to the accompanying drawings; (to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise)
FIG. 1 is a block diagram of a system for characterizing the spatiotemporal evolution of a regional logistics enterprise in accordance with the present invention.
Detailed Description
The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited thereto.
And taking logistics enterprises with corresponding standards in a certain area as research objects to research the space-time evolution characteristics of the logistics enterprises in a certain time period.
As shown in fig. 1, a system for representing space-time evolution of regional logistics enterprises includes a data collection module a, a data conversion and processing module b, a regional overall spatial distribution characteristic analysis module c, a regional spatial aggregation analysis module d, and a regional spatial distribution cold and hot spot analysis module e, which are connected in sequence, wherein the regional overall spatial distribution characteristic analysis module c, the regional spatial aggregation analysis module d, and the regional spatial distribution cold and hot spot analysis module e are all in signal connection with a regional space-time evolution analysis module f, and the data conversion and processing module b includes a data conversion unit and a data processing unit.
The method comprises the steps that a logistics enterprise list is obtained through an official website, a data collection module a obtains attribute information of a detailed address, registration time, logout time, registration capital, enterprise properties, an operation range and the like of a logistics enterprise based on an internet of things positioning system (IPS), and a logistics enterprise attribute database is established according to the attribute information.
The data conversion unit in the data conversion and processing module b converts attribute information in the attribute database of the logistics enterprise into space-time data information which can be identified by GIS software through xgeocording software, wherein the space-time data information refers to converting detailed addresses of various logistics enterprises into longitude and latitude coordinates on a corresponding geographic space through map attribute library standards (Baidu, Google, Gord or other map standards), and then classification on a time scale is established according to different registration time and logout time of the logistics enterprises; then, the time-space data information is monitored in a timing mode and maintained in real time through the data processing unit, and whether error data influence later-stage operation is detected; and the data processing unit generates a status report from all the statuses of the spatiotemporal data information through the xgeocoding software.
And (3) importing the geographic longitude and latitude coordinates and other attribute information of each logistics enterprise in different years in a certain time period into GIS software, and displaying the logistics enterprises on a map in a point mode. And then visualizing the space-time evolution of the logistics enterprises on a map by utilizing the visualization function of GIS software through a regional overall spatial distribution characteristic analysis module c, a regional spatial aggregation analysis module d and a regional spatial distribution cold and hot point analysis module e.
The regional overall spatial distribution characteristic analysis module c determines the development trend (concentration, diffusion or non-significance) of the logistics enterprise in the research region through standard deviation ellipse analysis and center analysis. The standard deviation ellipse analysis unit is a common method for measuring a certain tendency of a group of points in an area, and the tendency can be made more definite by drawing an element on a map to feel the same as the directionality of the element and calculating the standard deviation ellipse. The general dispersion trend of the logistics enterprises over time can be clearly found by comparing the standard deviation ellipses obtained by the logistics enterprises of different years. The center analysis is to measure the geometric characteristics of the elements by using the average gravity center of all the elements, wherein the average gravity center is the average x coordinate, y coordinate and z coordinate (if available) of all the elements in the research area, and the x coordinate and the y coordinate correspond to the longitude and latitude on a map. The average gravity center tracks the distribution change of the research elements by calculating the dispersion degree of the dispersion points (here, each logistics enterprise), and is very intuitively displayed when the distribution of different types of elements is compared. The distances between the corresponding logistics enterprises in different years and the corresponding average center can be obtained through center analysis, and by comparing the numerical values, the trend that the logistics enterprises develop on the geographical distribution along with the time is gathered, diffused or has no obvious change can be verified on the data.
The regional total spatial distribution characteristic analysis module c can preliminarily verify the spatial dynamic change of the logistics enterprises in the geographical distribution, but the gathering condition of the logistics enterprises in the spatial distribution cannot be intuitively displayed, so that the regional spatial gathering analysis module d (nuclear density analysis) is used for further analyzing the evolution characteristics of the logistics enterprises. Nuclear density analysis is a visual tool, and is a common method for exploring hot spot areas of punctual data. The magnitude of the point elements in a unit area is calculated by kernel density analysis and modeled as a smooth pyramidal surface, which is used to calculate the density of points around each output pixel, and the search radius of the pyramidal surface determines the scale of the analysis. The formula is as follows:
in the formula:
for the kernel density value of point x, K () is a weight function, (x-x)i) Points x and x representing the desired density estimateiThe direct distance between h and h is the bandwidth, i.e. the search radius (set by the GIS software), the choice of which value affects the smoothness of the density estimate.
In the spatial analysis of the selected point elements, the setting of the bandwidth h is mainly related to the analysis scale and the geographic phenomenon characteristics. Smaller bandwidths may allow more high-value or low-value regions to appear in the density distribution result, which is suitable for revealing local features of the density distribution, while larger bandwidths may allow hot spot regions to be more apparent on a global scale. In general, the bandwidth should be directly related to the discrete degree of the spatial elements, a larger bandwidth should be used for sparse spatial element distribution, and a smaller bandwidth should be considered for dense spatial elements.
In general, the value of the bandwidth h is fixed, and some errors may exist in analysis for some spatial elements with large density differences. Therefore, the invention constructs a self-adaptive bandwidth h based on the average gravity center constraint conditioniThe nuclear density analysis of (2) is used for adopting different analysis scales for different positions, so that errors caused by uneven distribution of spatial elements in the nuclear density analysis process are reduced.
On the basis of a kernel density function with fixed bandwidth, correcting bandwidth parameters; the kernel density function uses the following formula:
in the formula: omega is a bandwidth parameter and is set by GIS software.
By setting the mean centre of gravity
X and x areiThe direct distance between x and
and xiTo decide. Namely:
in the formula:
denotes x and xiThrough the mean center of gravity
The degree of distance correlation of the connections,
represents x and
the degree of correlation of the distance between them,
to represent
And xiThe degree of distance correlation between them.
The degree of association of all points in the region is:
the average degree of association is:
comparison of x-xiAnd q, the smaller of the two being
Value of (i.e., x-x)iThe value of (3) is combined with the set h and is substituted into the formula (1) to obtain a specific nuclear density value, and then the formula (2) is used for obtaining the adaptive bandwidth hi。
For the obtained nuclear density value
And dividing the physical distribution enterprise into different grades according to a natural break point method and the year with the maximum nuclear density value as a basis so as to generate physical distribution enterprise nuclear density distribution maps on different time nodes, and visually displaying the aggregation condition of the physical distribution enterprise space distribution in the region and the geographical dynamic change of the physical distribution enterprise along with the time through longitudinally comparing the space nuclear density distribution under the different time nodes.
The nuclear density analysis reveals the spatial distribution and aggregation condition of the logistics enterprises in the regional range, and hot spot areas and cold spot areas which are distributed on different spatial positions of the logistics enterprises are further identified through a regional spatial distribution cold and hot spot analysis module e (hot spot analysis). Hotspot analysis, also known as local spatial autocorrelation, calculates hotspot values for the cells in the region of interest and their surrounding cells, with distance D defining the correlation between neighboring cell values. In spatial clustering analysis, local clusters with high hot spot values are called hot spots, local clusters with low hot spot values and close to each other are called cold spots, and the local G statistic can identify two different spatial clustering modes existing in the research area. The hot spot analysis can be used for identifying high-value clusters (H-H patterns) and low-value clusters (L-L patterns) of the distribution quantity of the logistics enterprises at different spatial positions in a specific time range, and hot spot and cold spot areas of quantity increment.
Wherein
(hot-point value) is defined as:
wherein r isijRepresenting a spatial weight matrix, xjAnd representing the value of the space variable of the j area, namely the number of enterprises in the area.
To pair
After normalization, the above formula is converted to:
in the formula:
and
are respectively
The expected value and variance of the statistic. When the obtained standardized value Z is positive and remarkable, the number of enterprises in the position i and the surrounding area is increased in the time period, the aggregation intensity is strong, and the position i and the surrounding area are hot spot areas of enterprise tendency layout; on the contrary, when the Z value is negative and significant, it indicates that the number of enterprises at the position i and the surrounding area thereof is negative and increased in the time period, the aggregation strength is weak, and the enterprise is a cold spot layout area for avoiding or escaping.
And dividing the hot point value of each time interval into different grades from high to low by adopting a natural crack point method, and generating a hot point diagram of the spatial layout of the logistics enterprises in the area range. The areas with high heat indexes represent hot spots where logistics enterprises tend to gather and distribute, and the areas with low heat indexes represent cold spots in spatial layout where the logistics enterprises escape or avoid. By comparing the heat point diagrams under different time nodes, the forming mechanism of the spatial layout of the regional logistics enterprises can be further identified, and the future development trend can be reasonably predicted.
The region space-time evolution analysis module f is combined with a region overall spatial distribution characteristic analysis module c, a region space aggregation analysis module d, a region space distribution cold and hot point analysis module e, and corresponding regional policy guidance in the year and regional geographical environment quality, and starts from the aspects of economics, logistics, urban planning, geography and the like, so as to analyze and predict the evolution direction of the region logistics industry in a certain time in the future, and provide reference and basis for the establishment and site selection of logistics enterprises.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that several modifications in advance without departing from the principle of the invention should be considered as the protection scope of the invention for the designer skilled in the art.
Claims (7)
1. A system for characterizing the spatial-temporal evolution of a regional logistics enterprise, characterized by: the system comprises a data collection module a, a data conversion and processing module b, a regional overall spatial distribution characteristic analysis module c, a regional space aggregation analysis module d and a regional space distribution cold and hot point analysis module e which are sequentially connected, wherein the regional overall spatial distribution characteristic analysis module c, the regional space aggregation analysis module d and the regional space distribution cold and hot point analysis module e are in signal connection with a regional space-time evolution analysis module f;
the data collection module a acquires attribute information of the logistics enterprise;
the data conversion and processing module b converts the attribute information of the logistics enterprise into space-time data information and generates a state report for all states of the space-time data information;
the regional overall spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprise in the research region;
the region space gathering analysis module d analyzes the evolution characteristics of the logistics enterprises;
the region space distribution cold and hot point analysis module e is used for identifying hot point regions and cold point regions which are distributed on different spatial positions by the logistics enterprises;
and the region space-time evolution analysis module f is combined with the region overall spatial distribution characteristic analysis module c, the region space aggregation analysis module d and the region space distribution cold and hot spot analysis module e to predict the evolution direction of the region logistics industry in a certain time in the future.
2. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 1, wherein: the attribute information comprises detailed addresses, registration time and logout time, registration capital, enterprise properties and operating range of the logistics enterprises.
3. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 2, wherein: the time-space data information refers to the steps that detailed addresses of all logistics enterprises are converted into longitude and latitude coordinates on the corresponding geographic space through map attribute library standards, and classification on time scales is established according to different registration time and cancellation time of the logistics enterprises.
4. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 1, wherein: and the regional overall spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprise in the research region through standard deviation ellipse analysis and center analysis.
5. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 1, wherein: the region space gathering analysis module d visualizes the evolution characteristics of the logistics enterprises by adopting nuclear density analysis, and the nuclear density analysis is based on the self-adaptive bandwidth h under the condition of average gravity center constraintiThe kernel density function is:
wherein:
is the kernel density value of point x, K () is the weightFunction, (x-x)i) Points x and x representing the desired density estimateiThe direct distance between the two, omega is the bandwidth parameter;
setting average center of gravity
X and x areiThe direct distance between x and
and xiTo determine, namely:
in the formula:
denotes x and xiThrough the mean center of gravity
The degree of distance correlation of the connections,
represents x and
the degree of correlation of the distance between them,
to represent
And xiThe degree of distance correlation between;
the degree of association of all points in the region is:
the average degree of association is:
comparison of x-xiAnd q, the smaller of the two being x-xiIs combined with a given value of h and substituted into the formula
In the method, the nuclear density value is calculated and then is subject to the formula
Obtaining adaptive bandwidth hi。
6. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 1, wherein: the region overall spatial distribution characteristic analysis module c, the region spatial gathering analysis module d and the region spatial distribution cold and hot point analysis module e visualize the space-time evolution of the logistics enterprises on a map.
7. The system for characterizing the spatiotemporal evolution of a regional logistics enterprise of claim 1, wherein: the acquisition process of the hot spot area and the cold spot area comprises the following steps: hot spot values of the attention area unit and peripheral units of the attention area unit are calculated through hot spot analysis, and the relevance between adjacent unit values is defined according to the distance D, so that hot spot areas and cold spot areas in the research area are identified, and the hot spot areas and the cold spot areas which are distributed on different spatial positions of the logistics enterprise are determined.
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