CN110866051B - System for representing regional logistics enterprise space-time evolution - Google Patents
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Abstract
The invention discloses a system for representing the space-time evolution of regional logistics enterprises, which is used for acquiring attribute information of the logistics enterprises, converting the enterprise attribute information 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 (geographic information system) visualization technology, and predicting the evolution direction of the regional logistics enterprises in a certain time in the future by a regional space-time evolution analysis module. The invention can reasonably predict the future development trend of the logistics enterprise site selection, provides reference and basis for the establishment and site selection of the logistics enterprise, and improves 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 economic globalization, the global freight traffic is increasing, china is the largest developing country in the world, the freight traffic is also greatly increasing, and with the increase of freight traffic, the spatial location of urban space structures and logistics facilities is gradually changed. The position of the logistics facility has great influence on the cost and efficiency of cargo transportation, and meanwhile, the reasonable configuration of logistics resources can be influenced, so that how to enable urban logistics space recombination to be matched with urban space recombination has profound influence on sustainable development advocated by the current society. However, the problem of inaccurate positioning exists in the conventional 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 found, visual indication is provided for future establishment, site selection and development of the logistics enterprises, decision basis is provided for site selection of logistics nodes, and 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 logistics cost and improve transportation and distribution efficiency.
The invention adopts the following technical scheme:
a system for representing regional logistics enterprise space-time evolution 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 concentration 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 concentration analysis module d and the regional spatial distribution cold and hot point analysis module e are all in signal connection with a regional space-time evolution analysis module f;
the data collection module a acquires attribute information of a logistics enterprise;
the data conversion and processing module b converts attribute information of the logistics enterprises into space-time data information and generates a status report of all states of the space-time data information;
the regional overall spatial distribution characteristic analysis module c determines the overall development trend of logistics enterprises in the research region;
the regional space aggregation analysis module d analyzes the evolution characteristics of the logistics enterprise;
the regional space distribution cold and hot spot analysis module e is used for identifying hot spot areas and cold spot areas which are distributed on different spatial positions of a logistics enterprise;
the regional space-time evolution analysis module f is combined with 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 to predict the evolution direction of regional logistics in a certain time in the future.
In the above technical solution, the attribute information includes a detailed address, a registration time and a cancellation time of the logistics enterprise, a registration capital, an enterprise property and an operation range.
In the above technical solution, the spatio-temporal data information refers to converting detailed addresses of each logistics enterprise into longitude and latitude coordinates on a corresponding geographic space through a map attribute library standard, and then establishing classification on time scales according to different registration time and cancellation time of the logistics enterprise.
In the above technical scheme, the regional overall spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprises in the research region through standard deviation ellipse analysis and center analysis.
In the above technical solution, the regional space aggregation analysis module d visualizes evolution characteristics of the logistics enterprise by adopting nuclear density analysis, where the nuclear density analysis is based on an adaptive bandwidth h under an average gravity center constraint condition i The 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 required density estimates i The direct distance between the two, omega is the bandwidth parameter;
setting the average center of gravityX is equal to x i The direct distance between them is defined by x and +.>And x i To decide, namely:
wherein:representing x and x i Through the average center of gravity->Distance association degree of connection,/->Represents x and->Degree of distance correlation between->Representation->And x i The degree of distance association between them;
the degree of association of all points within the region is:
the average degree of association is:
comparison of x-x i And q, the smaller of the two being x-x i Is combined with a given h, and is brought into the formulaIn (2), obtaining the nuclear density value, and then passing through the formula +.>Obtaining self-adaptive bandwidth h i 。
In the above technical solution, the regional overall spatial distribution feature analysis module c, the regional spatial concentration analysis module d, and the regional spatial distribution cold and hot point analysis module e visualize the space-time evolution of the logistics enterprise on a map.
In the above technical solution, the acquiring process of the hot spot area and the cold spot area is: and (3) calculating hot spot values of the concerned area unit and peripheral units thereof through hot spot analysis, and defining the relevance between adjacent unit values by using the distance D so as to identify hot spot areas and cold spot areas in the research area and determine the hot spot areas and the cold spot areas which are distributed on different spatial positions of the logistics enterprise.
The beneficial effects of the invention are as follows: according to the invention, based on the information positioning system of the Internet of things, the attribute information of the logistics enterprises is obtained, an enterprise attribute database is established, then the attribute information in the enterprise attribute database is converted into corresponding space-time data information, and the space-time evolution characteristics of the logistics enterprises in a certain time period and a certain area are visualized by means of a GIS (geographic information system) visualization technology, so that references and references are provided for the establishment and site selection of the logistics facilities of the enterprises in the future, the transportation cost is reduced, and the distribution service efficiency and quality are improved.
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The invention will be illustrated by way of example and with reference to the accompanying drawings; (for the sake of more clear explanation of the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be obvious that the drawings in the description below are only some embodiments of the present invention, and that other drawings can be obtained from these drawings without inventive effort for a person skilled in the art)
FIG. 1 is a block diagram of a system for characterizing the temporal and spatial evolution of regional logistics enterprises in accordance with the present invention.
Detailed Description
The following describes the technical scheme of the present invention in detail with reference to the accompanying drawings, but the scope of the present invention is not limited thereto.
Taking logistics enterprises with corresponding standards in a certain area as research objects, and researching the time-space evolution characteristics of the logistics enterprises in a certain time period.
As shown in fig. 1, the 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 concentration 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 concentration 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, and the data conversion and processing module b comprises 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 such as a detailed address, registration time and cancellation time, registration capital, enterprise properties and operation range of a logistics enterprise based on an Internet of things positioning system (IPS), and a logistics enterprise attribute database is built according to the attribute information.
The data conversion unit in the data conversion and processing module b converts attribute information in a logistics enterprise attribute database into spatial-temporal data information which can be identified by GIS software through xgeocoding software, wherein the spatial-temporal data information refers to that detailed addresses of all logistics enterprises are converted into longitude and latitude coordinates on corresponding geographic spaces through map attribute library standards (hundred degrees, google, high-altitude or other map standards), and classification on time scales is established according to different registration time and cancellation time of the logistics enterprises; the data processing unit is used for carrying out timing monitoring and real-time maintenance on the space-time data information and detecting whether error data influence the later operation; the data processing unit generates a status report of all the states of the space-time data information through xgeocoding software.
And importing the geographic longitude and latitude coordinates and other attribute information of each logistics enterprise in different years within a certain period of time into GIS software, and displaying the logistics enterprises on a map in a point mode. And then, the space-time evolution of the logistics enterprise is visualized on a map through a regional overall spatial distribution characteristic analysis module c, a regional spatial concentration analysis module d and a regional spatial distribution cold and hot point analysis module e by utilizing the visualization function of GIS software.
The regional overall spatial distribution characteristic analysis module c determines the overall development trend (aggregation, diffusion or insignificant) of the logistics enterprises 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 trend of a set of points in a region, and the direction of the elements can be perceived as the same by drawing the elements on a map, and the trend can be made more clear by calculating the standard deviation ellipse. The standard deviation ellipses obtained by comparing logistics enterprises in different years can clearly find the approximate dispersion trend of the logistics enterprises with time. The center analysis refers to measuring the geometric characteristics of the elements using the average center of gravity of all elements, which refers to the average x-coordinate, y-coordinate and z-coordinate (if available) of all elements in the study area, the x-coordinate and y-coordinate corresponding to the latitude and longitude on the map. The average gravity center tracks the distribution change of the research elements by calculating the dispersion degree of the scattered points (each logistics enterprise, and the distribution of different types of elements is intuitively displayed. The distances from the corresponding average centers of the logistics enterprises in different years can be obtained through center analysis, and the data can be compared to verify that the trend of the logistics enterprises in the geographic distribution along with the time is aggregation, diffusion or no obvious change.
The regional overall spatial distribution characteristic analysis module c can preliminarily verify the spatial dynamic change of the logistics enterprises in geographical distribution, but can not intuitively display the aggregation condition of the logistics enterprises in spatial distribution, so that the evolution characteristics of the logistics enterprises are further analyzed by using the regional spatial aggregation analysis module d (nuclear density analysis). Nuclear density analysis is a visual tool and is a common method for exploring punctiform data hot spot areas. The magnitude of the dot elements in a unit area is calculated by nuclear density analysis and the magnitude is modeled as a smooth conical surface, which is used to calculate the density of the dots around each output pixel, the search radius of the conical surface determining the scale of the analysis. The formula is as follows:
wherein:for the kernel density value of point x, K () is a weight function, (x-x) i ) Points x and x representing the required density estimates i The direct distance between the two is h, the bandwidth, i.e. the searching radius (set by GIS software), and the selection of the value affects the smoothness of the density estimation.
In the spatial analysis of the selected point elements, the bandwidth h is set mainly in relation to the analysis scale and the geographic phenomenon characteristics. The smaller bandwidth may allow more regions of high or low values to appear in the density distribution result, suitable for revealing local features of the density distribution, while the larger bandwidth may allow hot spot regions to be more apparent at the global scale. In general, bandwidth should be positively correlated with the degree of dispersion of spatial elements, with a larger bandwidth being employed for sparse spatial element distributions and a smaller bandwidth being considered for dense spatial elements.
Typically, the bandwidth h is fixed, and there are some errors in analysis for some spatial elements with large density differences. Therefore, the invention constructs the self-adaptive bandwidth h based on the constraint condition of the average gravity center i Different analysis scales are adopted for different positions, so that errors caused by uneven distribution of spatial elements in the nuclear density analysis process are reduced.
Correcting bandwidth parameters on the basis of a kernel density function with fixed bandwidth; the kernel density function uses the following formula:
wherein: omega is a bandwidth parameter and is set by GIS software.
By setting the average gravity centerX is equal to x i The direct distance between them is defined by x and +.>And x i To determine. Namely:
wherein:representing x and x i Through the average center of gravity->Distance association degree of connection,/->Represents x and->Degree of distance correlation between->Representation->And x i The degree of distance association between them.
The degree of association of all points within the region is:
the average degree of association is:
comparison of x-x i And q, the smaller of them beingThe value of (i.e. x-x i The value of (2) is combined with a given h and is brought into a formula (1) to obtain a specific nuclear density value, and then the adaptive bandwidth h is obtained through a formula (2) i 。
For the obtained nuclear density valueAccording to the natural split point method and based on the year with the maximum nuclear density value, different grades are divided, so that nuclear density distribution diagrams of logistics enterprises on different time nodes are generated, and the aggregation condition of the spatial distribution of the logistics enterprises in the area and the geographic dynamic change of the logistics enterprises along with time can be intuitively displayed through longitudinal comparison of the spatial nuclear density distribution under different time nodes.
Nuclear density analysis reveals the spatial distribution and aggregation of logistics enterprises in a regional range through regional spatial divisionThe distribution hot spot analysis module e (hot spot analysis) further identifies hot spot areas and cold spot areas distributed on different spatial positions of the logistics enterprises. Hot spot analysis, also known as local spatial autocorrelation, calculates hot spot values for the region of interest cells and their surrounding cells, defining the correlation between adjacent cell values with distance D. In spatial aggregation analysis, local aggregates with higher values of hot spots are referred to as hot spots, while local aggregates with lower values of hot spots and close to each other are referred to as cold spots, and local G statistics can identify these two different spatial aggregation patterns present in the investigation region. The hot spot analysis can be used for identifying high-value clusters (H-H mode) and low-value clusters (L-L mode) of distribution quantity of logistics enterprises at different spatial positions within a specific time range, and hot spots and cold spot areas with quantity increment. Wherein the method comprises the steps of(hotspot value) is defined as:
wherein r is ij Representing a spatial weight matrix, x j Representing the spatial variable value of the j region, i.e., the number of businesses within the region.
For a pair ofAfter normalization, the above formula is converted into:
wherein:and->Are respectively->The expected value and variance of the statistics. The normalized value Z obtained is positive and significant, indicatingThe number of enterprises in the position i and the surrounding areas thereof is increased in the time period, the gathering strength is strong, and the position i and the surrounding areas are hot spot areas of enterprise tendency layout; on the contrary, when the Z value is negative and obvious, the position i and the surrounding areas thereof are indicated to be negative and increased in enterprise quantity in the time period, and the concentration strength is weak, so that the position i and the surrounding areas are cold spot layout areas for avoiding or escaping of enterprises.
And dividing the hot spot value of each period into different grades from high to low by adopting a natural split point method, and generating a hot spot diagram of the spatial layout of the logistics enterprise in the region range. The areas with high heat index represent hot spots where logistics enterprises tend to gather and distribute, while the areas with low heat index represent spatially laid cold spots where logistics enterprises "escape" or "avoid". By comparing the hot spot diagrams under different time nodes, the formation mechanism of the spatial layout of regional logistics enterprises can be further identified, and the future development trend can be reasonably predicted.
The regional space-time evolution analysis module f is combined with the regional overall spatial distribution characteristic analysis module c, the regional spatial aggregation analysis module d, the regional spatial distribution cold and hot spot analysis module e and the regional geographic environment advantages and disadvantages in the corresponding year, and the evolution direction of regional logistics industry in a certain time in the future is analyzed and predicted from the aspects of economics, logistics, urban planning, geography and the like, so that references and bases are provided for building 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 examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that several modifications, which do not depart from the principles of the invention, should be deemed to be the scope of the invention as set forth in the appended claims.
Claims (6)
1. A system for characterizing the temporal-spatial evolution of regional logistics enterprises, 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 spatial concentration 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 concentration analysis module d and the regional spatial distribution cold and hot point analysis module e are all in signal connection with a regional space-time evolution analysis module f;
the data collection module a acquires attribute information of a logistics enterprise;
the data conversion and processing module b converts attribute information of the logistics enterprises into space-time data information and generates a status report of all states of the space-time data information;
the regional overall spatial distribution characteristic analysis module c determines the overall development trend of logistics enterprises in the research region;
the regional space aggregation analysis module d analyzes the evolution characteristics of the logistics enterprise;
the regional space distribution cold and hot spot analysis module e is used for identifying hot spot areas and cold spot areas which are distributed on different spatial positions of a logistics enterprise;
the regional space-time evolution analysis module f is combined with 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 to predict the evolution direction of regional logistics in a certain time in the future;
the regional space aggregation analysis module d visualizes evolution characteristics of logistics enterprises by adopting nuclear density analysis, and the nuclear density analysis is based on self-adaptive bandwidth h under the constraint condition of average gravity center i The 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 required density estimates i The direct distance between the two, omega is the bandwidth parameter;
setting the average center of gravityX is equal to x i The direct distance between them is defined by x and +.>And x i To decide, namely:
wherein:representing x and x i Through the average center of gravity->Distance association degree of connection,/->Represents x and->Degree of distance correlation between->Representation->And x i The degree of distance association between them;
the degree of association of all points within the region is:
the average degree of association is:
comparison of x-x i And q, the smaller of the two being x-x i Is combined with the value of (2)A given h, brought into the formulaIn (2), obtaining the nuclear density value, and then passing through the formula +.>Obtaining self-adaptive bandwidth h i 。
2. The system for characterizing regional logistics enterprise temporal and spatial evolution of claim 1, wherein: the attribute information includes the detailed address, registration time and deregistration time, registration capital, enterprise nature and business scope of the logistics enterprise.
3. The system for characterizing regional logistics enterprise temporal and spatial evolution of claim 2, wherein: the time-space data information refers to that detailed addresses of all logistics enterprises are converted into longitude and latitude coordinates on corresponding geographic space through a map attribute library standard, and classification on time scales is built according to different registration time and cancellation time of the logistics enterprises.
4. The system for characterizing regional logistics enterprise temporal and spatial evolution of claim 1, wherein: and the regional overall spatial distribution characteristic analysis module c determines the overall development trend of the logistics enterprises in the research region through standard deviation ellipse analysis and center analysis.
5. The system for characterizing regional logistics enterprise temporal and spatial evolution of claim 1, wherein: and the regional overall spatial distribution characteristic analysis module c, the regional spatial concentration analysis module d and the regional spatial distribution cold and hot point analysis module e visualize the space-time evolution of the logistics enterprise on a map.
6. The system for characterizing regional logistics enterprise temporal and spatial evolution of claim 1, wherein: the hot spot area and the cold spot area are obtained by the following steps: and (3) calculating hot spot values of the concerned area unit and peripheral units thereof through hot spot analysis, and defining the relevance between adjacent unit values by using the distance D so as to identify hot spot areas and cold spot areas in the research area and determine the hot spot areas and the cold spot areas which are distributed on different spatial positions of the logistics enterprise.
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