CN114863272A - Method and system for determining influence strength of urban vegetation on urban comprehensive vitality - Google Patents
Method and system for determining influence strength of urban vegetation on urban comprehensive vitality Download PDFInfo
- Publication number
- CN114863272A CN114863272A CN202210416740.8A CN202210416740A CN114863272A CN 114863272 A CN114863272 A CN 114863272A CN 202210416740 A CN202210416740 A CN 202210416740A CN 114863272 A CN114863272 A CN 114863272A
- Authority
- CN
- China
- Prior art keywords
- urban
- data
- influence
- strength
- vegetation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
- Y02A30/60—Planning or developing urban green infrastructure
Abstract
The invention provides a method and a system for determining the influence strength of urban vegetation on urban comprehensive vitality, wherein the method mainly comprises the following steps: acquiring vector boundary data of a target area, and creating a cellular graph of the target area according to the vector boundary data, wherein the cellular graph comprises a plurality of cellular units; acquiring thermodynamic diagram data of a target area, and determining crowd gathering strength in a honeycomb unit according to the thermodynamic diagram data; obtaining social network text data in the target area, and determining emotion intensity in the cellular unit according to the social network text data; calculating to obtain the urban comprehensive activity according to the crowd gathering strength and the emotion strength; the method comprises the steps of obtaining multi-source big data of a target area, extracting to obtain influence factors, determining the influence intensity change value of urban vegetation on urban comprehensive vitality according to detection results of all the influence factors, wherein the influence factors comprise vegetation coverage and non-vegetation influence factors, and the scheme can be widely applied to the field of urban planning.
Description
Technical Field
The invention relates to the technical field of urban planning design, in particular to a method and a system for determining the influence strength of urban vegetation on urban comprehensive vitality.
Background
Urban vitality refers to the ability of a city to provide diverse lives of citizens, characterized by human activities and their interaction with space. The urban comprehensive activity measure refers to the comprehensive evaluation of urban activity by adopting urban activity evaluation index dimensions in multiple aspects. The urban vitality evaluation indexes are calculated by adopting multi-source urban data and utilizing various evaluation methods.
The existing city vitality research has provided a plurality of evaluation systems and a plurality of influence factors, and makes important contribution to the current city planning and development. In the research of the intensity of the urban vitality influence factors, predecessors mine the influence of various indexes on urban vitality from the aspects of urban areas, social environments, economic environments, cultural environments and the like, wherein the factors of positive correlation and the factors of negative correlation exist.
In the technical field of urban planning and design, the following significant technical problems generally exist in the related technical solutions:
(1) the existing urban vitality research mainly focuses on urban space planning and design, the urban planning which excessively pursues the construction efficiency easily ignores the happiness of residents, people serve as main bodies of urban life, the urban vitality is built through activities and exchanges in the urban space, but the research of urban vitality evaluation from the psychological perspective of the residents is relatively less.
(2) In the research on the influence factors of the urban vitality, predecessors pay attention to the physical environment and the social and economic environment in the city, and the ecological environment is less excavated; the vegetation coverage is one of important factors in urban ecological environment, and the influence of the vegetation coverage on urban vitality is not uniformly fixed.
(3) The factors influencing the urban vitality are various, and the relationship between the factors is extremely complex, and the factors can be promoted pairwise or offset mutually, but the research on the combined action relationship and the action strength of the multiple factors in the prior art is relatively less.
Disclosure of Invention
In view of the above, to at least partially solve one of the above technical problems, an object of the embodiments of the present invention is to provide a method for measuring the comprehensive urban activity more comprehensively and more accurately; in addition, the scheme also provides a corresponding system, a device and a storage medium to realize the method.
On one hand, the technical scheme of the application provides a method for determining the influence strength of urban vegetation on urban comprehensive vitality, which comprises the following steps:
acquiring vector boundary data of a target area, and creating a cellular graph of the target area according to the vector boundary data, wherein the cellular graph comprises a plurality of cellular units;
acquiring thermodynamic diagram data of a target area, and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data;
obtaining social network text data in the target region, and determining emotional intensity in the cellular unit according to the social network text data;
calculating to obtain urban comprehensive activity according to the crowd gathering strength and the emotion strength;
obtaining multi-source big data of the target area, extracting to obtain an influence factor, and determining the change value of the influence intensity of the influence factor on the urban comprehensive activity, wherein the influence factor comprises vegetation coverage and non-vegetation influence factors.
In a feasible embodiment of the scheme of the application, the obtaining of the multi-source big data of the target area to extract an influence factor and the determining of the change value of the influence strength of the influence factor on the urban comprehensive vitality include at least one of the following steps:
calculating to obtain first influence strength of each factor according to the vegetation coverage or the single-factor state of the non-vegetation influence factors;
obtaining a second influence intensity according to the vegetation coverage and the interaction state of the non-vegetation influence factors, and calculating to obtain the influence intensity change value according to the first influence intensity of the vegetation coverage and the second influence intensity of the interaction of the vegetation coverage and the non-vegetation influence factors:
in a possible embodiment of the present disclosure, the step of calculating the urban comprehensive vitality according to the crowd gathering strength and the emotional strength includes:
carrying out data standardization processing on the crowd gathering strength and the emotion strength;
constructing an initial matrix according to the data after standardization processing, and carrying out standardization processing on the initial matrix to obtain a standardized matrix;
calculating to obtain a first distance according to the normalized matrix and a positive ideal solution, and calculating to obtain a second distance according to the normalized matrix and a negative ideal solution;
and calculating to obtain the urban comprehensive vitality according to the first distance and the second distance.
In a feasible embodiment of the scheme of the application, the step of obtaining the multi-source big data of the target area and extracting to obtain an influence factor and determining an influence intensity change value of the influence factor on the urban comprehensive vitality includes:
determining the action state of the second influence strength;
the action state comprises at least one of: a non-linear attenuation state, a single-factor non-linear attenuation state, a two-factor enhancement state, an independent action state, and a non-linear enhancement state.
In one possible embodiment of the present solution, the non-vegetation affecting factors include at least one of: road accessibility, land use mix, point of interest density, building density, night light intensity, salary level, and house price level.
In a possible embodiment of the present application, if it is determined that the influence factor is vegetation coverage, in the embodiment, the step of obtaining the multi-source big data of the target area and extracting the obtained influence factor, and determining the change value of the influence strength of the influence factor on the urban comprehensive activity further includes a quantization process of the vegetation coverage, where the quantization process includes:
acquiring a remote sensing image;
performing orthorectification on the remote sensing image;
carrying out image fusion on multispectral data and panchromatic data of the remote sensing image after orthorectification to obtain remote sensing image data;
and performing atmospheric correction on multispectral data of the remote sensing image data, and performing band operation on multispectral bands of the remote sensing image data after atmospheric correction to obtain the vegetation coverage.
In a feasible embodiment of the present application, the step of constructing and obtaining an initial matrix according to the data after the normalization processing, and performing the normalization processing on the initial matrix to obtain a normalized matrix includes:
dividing the crowd gathering intensity into a working day crowd gathering intensity and a resting day crowd gathering intensity;
dividing the emotional intensity into a working day emotional intensity and a resting day emotional intensity;
and constructing the initial matrix according to a secondary index, wherein a row vector of the initial matrix represents the number of the honeycomb units, a column vector of the initial matrix represents a secondary index, and the secondary index comprises the working day crowd gathering strength, the rest day crowd gathering strength, the working day emotion strength and the rest day emotion strength.
On the other hand, this application technical scheme still provides a system for confirming urban vegetation is to city comprehensive vigor influence intensity, and this system includes:
the multi-source big data acquisition unit is used for acquiring vector boundary data of a target area and creating a cellular map of the target area according to the vector boundary data, wherein the cellular map comprises a plurality of cellular units;
the crowd gathering strength calculation unit is used for acquiring thermodynamic diagram data of a target area and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data;
the emotion intensity calculation unit is used for acquiring social network text data in the target region and determining emotion intensity in the cellular unit according to the social network text data;
the comprehensive vitality calculating unit is used for calculating urban comprehensive vitality according to the crowd gathering strength and the emotion strength;
and the influence intensity measurement unit is used for acquiring multi-source big data of the target area, extracting to obtain influence factors, and determining the change value of the influence intensity of the influence factors on the urban comprehensive vitality according to the detection result of the influence factors on the urban comprehensive vitality, wherein the influence factors comprise vegetation coverage and non-vegetation influence factors.
On the other hand, this application technical scheme still provides the equipment of confirming urban vegetation to city comprehensive vitality influence intensity, and this equipment includes:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to perform a method of determining the intensity of impact of urban vegetation on urban integrated vigor as described in any one of the first aspects.
In another aspect, the present technical solution further provides a storage medium, in which a processor-executable program is stored, and when the processor-executable program is executed by a processor, the processor is configured to perform the method for determining the influence strength of urban vegetation on urban comprehensive vitality, as described in any one of the first aspect.
Advantages and benefits of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
according to the technical scheme, the urban comprehensive vitality is evaluated by combining the crowd gathering strength and the emotion strength, objective crowd activities and subjective resident psychological conditions are considered, the subjective and objective consistency is achieved, and the usability of the measurement result is higher due to more comprehensive factor consideration; according to the scheme, the influence strength of the vegetation coverage factor on the urban vitality and the interaction mode of the vegetation coverage factor and other factors can be mined through comprehensively analyzing various possible influence factors, the value of urban vegetation on urban vitality construction can be explored, and therefore a more accurate measure of urban comprehensive vitality can be realized, the evaluation index system of urban comprehensive vitality can be improved, and a reference is provided for urban planning research.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating steps of a method for determining an influence strength of urban vegetation on urban comprehensive vitality according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a visualization result of the crowd gathering strength according to the embodiment of the present disclosure;
FIG. 3 is a diagram illustrating a visualization result of emotional intensity in the technical solution of the present application;
fig. 4 is a schematic diagram of a city comprehensive vitality calculation result and region selection verification in the technical scheme of the application;
fig. 5 is a schematic view of a visualization result of vegetation coverage in the technical scheme of the present application;
fig. 6 is a schematic diagram illustrating a road reachability result in the technical solution of the present application;
FIG. 7 is a schematic diagram of results of land utilization mixedness in the technical solution of the present application;
FIG. 8 is a schematic diagram of a POI density result in the present disclosure;
FIG. 9 is a graph illustrating building density results according to aspects of the present disclosure;
FIG. 10 is a diagram illustrating a night light intensity result according to an embodiment of the present disclosure;
FIG. 11 is a diagram illustrating salary level results according to an embodiment of the present disclosure;
FIG. 12 is a schematic representation of the results of the rate level in the embodiment of the present application;
FIG. 13 is a graph of single factor explanatory force intensity in the solution of the present application;
FIG. 14 is a graph illustrating the strength of an interpretive force of vegetation coverage interacting with other factors according to aspects of the present disclosure;
FIG. 15 is a graph of the variation of intensity for the two-factor interaction in the present embodiment.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
The vegetation plays an important role in a land ecosystem, is a natural tie for connecting elements such as soil, atmosphere and moisture, and can improve natural ecological elements such as regional air, water quality and the like, so that the vegetation is more beneficial to human survival. The importance of good ecological environment landscape for the modeling of modern urban space is self-evident, the waterfront spaces such as greenbelt parks, lake water systems and the like are important components of urban public spaces, and the spaces in the high-quality landscape environment are full of abundant crowd activities, and the overflow effect of space activity can bring the crowd activities to peripheral spaces. Urban vegetation coverage and its dynamic changing conditions have been a topic of interest to scholars and urban planners. However, with the expansion of urbanization and urban construction, natural landscapes mainly based on urban vegetation are gradually replaced by impervious surfaces such as cement and buildings, so that a series of natural problems such as biological diversity reduction and water and soil loss occur, and a series of urban ecological problems such as heat island effect and haze also occur.
Although the corresponding technical solutions for the urban comprehensive vitality measure have been proposed in the related art, the determined influencing factors for the urban comprehensive vitality are generally single, or influence analysis of the interaction of the influencing factors is lacked. For this reason, in a first aspect, the present application provides a method for determining an influence strength of urban vegetation on urban comprehensive vitality, and as shown in fig. 1, the method may include steps S100 to S500:
s100, vector boundary data of the target area are obtained, and a cellular graph of the target area is created according to the vector boundary data, wherein the cellular graph comprises a plurality of cellular units.
Specifically, in the embodiment, firstly, an urban area or a city needing city comprehensive activity measurement is taken as a target area through a GIS platform, a regular hexagon cellular map is created through the GIS platform on the basis of vector boundary data of the target area, the cellular map comprises a plurality of regular hexagon cellular units, and the crowd gathering strength, the emotion strength and the like calculated in the subsequent steps all take a single cellular unit as a unit.
S200, obtaining thermodynamic diagram data of the target area, and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data.
In particular, in an embodiment, the crowd concentration strength for each cell is calculated. Firstly, data acquisition is carried out, namely thermodynamic diagram data of a target area are acquired. Then data cleaning is carried out, and data cleaning is carried out on thermodynamic diagram data; the method aims to carry out examination and verification on data, delete repeated information, correct existing errors and ensure data consistency. And further performing space coordinate conversion, performing geographical registration on one cleaned thermodynamic diagram by using a geographical registration tool by using a high-definition satellite remote sensing map with space coordinates, and storing a registration link in the geographical registration process. And then projection conversion is carried out, the embodiment utilizes the geographic registration link stored in the space coordinate conversion to respectively carry out batch geographic registration on the rest cleaned thermodynamic diagrams to obtain tif data with coordinates and overlapped space positions, and then data cutting is carried out on the tif data, namely the cleaned thermodynamic diagrams are subjected to data cutting to be cut into a range matched with the target region. And finally, calculating to obtain the crowd gathering strength, namely performing mean value calculation on the clipped thermodynamic diagram data, wherein the calculation formula is as follows:
wherein, V int Representing the crowd concentration intensity; i represents different time, i is 1,2,3, … n, n is the total number of time; v i Indicating the thermodynamic value at time i. Finally, as shown in fig. 2, the crowd concentration strength within the honeycomb cell is calculated: the crowd concentration degree in each honeycomb unit is the crowd concentration intensity V of all the crowd falling into the honeycomb unit i Is measured. In fig. 2, the left side is a visualization result of the crowd gathering intensity on weekdays, and the right side is a visualization result of the crowd gathering intensity on holidays.
S300, social network text data in the target area are obtained, and emotion intensity in the cellular unit is determined according to the social network text data.
In particular, in an embodiment, relevant social network text data of the platform or software in the target region, including but not limited to topic content, location, release time, gender information, is obtained through a data analysis tool, and is updated and collected once every certain period of time (1 hour or 2 hours). All the data are acquired and then merged into a data set document. Then, data cleaning treatment is carried out, for example, positioning place values which are not in the range of the research area in the data set document are screened out firstly, and the whole row is deleted; secondly, Chinese extraction is carried out on the topic content, characters such as symbols, numbers, expressions and the like which cannot be used for emotion analysis are removed, and the last location positioning character in each line of topic content text is deleted; and finally, deleting repeated values and null value data of all the data. Performing coordinate conversion on the cleaned data, for example, copying the location point list information into a newly-built second document, converting the text location into longitude and latitude coordinates by calling an API (application programming interface) of the map open platform, and writing the longitude and latitude coordinates into the second document, wherein the longitude and latitude information is a mars coordinate system; and the Mars coordinate system positioning data is converted into a WGS-1984 geographical coordinate system which can be finally applied to geographical mapping by utilizing GIS software. For social network text data, performing emotion analysis and assignment, for example, copying topic content into a newly-created third document, performing emotion analysis and emotion assignment on the text through a natural language processing technology, and writing emotion values (the interval is [0-1], the more the interval is closer to 1, the more positive the interval is), emotion tendencies and credibility into the third document; copying the data and the converted coordinate data into a data set document together to enable each row of data information in the file to correspond to each other; and finally, deleting repeated values and null values of all the data again. Finally, introducing the emotion data into GIS software, selecting longitude and latitude data columns in the data by XY coordinates respectively, and selecting an emotion intensity value by a Z value; then, setting a projection coordinate system for the data points, and screening out the data points falling into a research area; the emotional intensity in each cell was finally calculated as the mean of the emotional intensities of all data points falling within the cell, as shown in fig. 3. In fig. 3, the left side is the visualization of the mood intensity on weekdays, and the right side is the visualization of the mood intensity on weekdays.
S400, calculating to obtain urban comprehensive vitality according to the crowd gathering strength and the emotion strength;
in the embodiment, the urban comprehensive vitality is calculated according to the crowd gathering strength and the emotion strength by adopting a TOPSIS-based evaluation method. In an embodiment, the step S400 of calculating the city comprehensive vitality according to the crowd gathering strength and the emotional strength may include steps S410 to S440:
and S410, carrying out data standardization processing on the clustering strength and the emotion strength of the human.
Specifically, in the embodiment, the data obtained in step S200 and step S300 are first normalized, where the normalization formula is:
and S420, constructing to obtain an initial matrix according to the data after the standardization processing, and carrying out standardization processing on the initial matrix to obtain a standardized matrix.
The embodiment firstly divides the crowd gathering strength into the crowd gathering strength in working days and the crowd gathering strength in rest days; dividing the emotional intensity into working day emotional intensity and resting day emotional intensity; and then constructing according to the secondary indexes to obtain an initial matrix, wherein the row vector of the initial matrix represents the number of the honeycomb units, the column vector of the initial matrix represents the secondary indexes, and the secondary indexes comprise the working day crowd gathering strength, the resting day crowd gathering strength, the working day emotion strength and the resting day emotion strength.
Specifically, in the embodiment, for a plurality of cellular units obtained by division, the embodiment is represented by row vectors of a matrix, four secondary indexes such as the working day crowd gathering strength, the resting day crowd gathering strength, the working day emotional strength, the resting day emotional strength and the like are respectively represented by column vectors of the matrix, and the working day crowd gathering strength, the resting day crowd gathering strength, the working day emotional strength and the resting day emotional strength are all very large indexes, so that an initial matrix is formed:
in the initial matrix, x ij And j is a j-th index representing the ith cellular unit, wherein i is 1,2,3 and … m, m is the total number of the cellular units in the research area, and j is 1,2,3 and 4. Then, carrying out normalization processing on the initial matrix X:
further, the normalized matrix is:
and S430, calculating to obtain a first distance according to the normalized matrix and the positive ideal solution, and calculating to obtain a second distance according to the normalized matrix and the negative ideal solution.
In particular, in an embodiment, a positive ideal solution is determined
Sum negative ideal solution
In an embodiment, a positive ideal solution refers to a solution that maximizes the benefit of outcome and minimizes the cost, and a negative ideal solution refers to a solution that maximizes the cost and minimizes the benefit of outcome. Get the positive ideal solution
Negative ideal solution
Best mode
Worst scheme
Wherein m is 4.
Then, the embodiment carries out scoring index construction aiming at the positive ideal solution and the negative ideal solution, and calculates the object and the optimal scheme of the cellular unit
(first distance) and worst case
(second distance). The calculation formula is as follows:
where i is an evaluation target and represents a cell, j is an evaluation index, and j is 1,2,3, 4.
And S440, calculating according to the first distance and the second distance to obtain the urban comprehensive vitality.
In particular, in the examples, the urban comprehensive vitality is measured as C i Value measure, C i A higher value is closer to 1, which indicates a higher urban integrated activity, and a higher value is closer to 0, which indicates a lower urban integrated activity. The calculation formula is as follows:
it should be noted that, if there is no social network emotion data in the ith cell on both working days and rest days, the emotion value of the cell is assigned to 0, that is: x is the number of i3 ＝0，x i4 When the initial matrix is 0, the initial matrix may be simplified to obtain a matrix:
finally, as shown in fig. 4, the embodiment may further perform visualization processing on the city comprehensive vitality calculation result.
S500, obtaining multi-source big data of the target area, extracting to obtain influence factors, and determining the change value of the influence intensity of the urban vegetation on the urban comprehensive vitality according to the influence intensity of the influence factors on the urban comprehensive vitality, wherein the influence factors comprise vegetation coverage and non-vegetation influence factors.
In an embodiment, the non-vegetation affecting factors include at least one of: road accessibility, land use mix, point of interest density, building density, night light intensity, salary level, and house price level.
Therefore, in performing the acquisition of the impact factors, embodiments include, but are not limited to, steps S501-S508:
s501, obtaining vegetation coverage.
Specifically, in the embodiment, a high-resolution second remote sensing image of a target area is obtained firstly; and then performing orthorectification on the multispectral and panchromatic data of the high-resolution second-grade remote sensing image. And then carrying out image fusion on the multispectral and panchromatic data of the high-resolution No. two remote sensing image to obtain the high-resolution No. two remote sensing image data with the resolution of 1 m. And then, performing atmospheric correction on the multispectral data of the high-resolution second remote sensing image, wherein the aim is to obtain a true reflection value of the ground object. Further, performing band operation on the multispectral bands of the corrected high-resolution No. two remote sensing images to obtain vegetation coverage, wherein the calculation formula is as follows:
NDVI represents the vegetation coverage, NIR represents the reflection value of a near infrared band, and R is the reflection value of a visible light red band. And finally, calculating the vegetation coverage in each honeycomb unit as the mean value of all the vegetation coverage falling into the honeycomb unit, normalizing the mean value of the vegetation coverage, and performing visualization processing on the vegetation coverage calculation result as shown in fig. 5.
And S502, acquiring the road accessibility.
In the embodiment, firstly, the road line data of a target area is obtained through an open source map downloading platform; because the obtained road network data may have problems of broken roads, repeated roads, partial roads missing and the like, the road data needs to be subjected to processing such as deleting repeated roads, prolonging broken roads, supplementing missing road segments and the like, and the road data of the target area is screened out. The method is characterized in that topology correction is carried out on cleaned road network data, wherein topology is a method for abstracting entities into points irrelevant to size and shape of the points, lines connecting the entities are abstracted into lines, and then the relationship between the points and the lines is represented in a graph form, and the purpose is to extract the connection relationship between the points and the lines. Then, the road network data after the topology correction is split, and the aim is to refine the road network into single objects for analysis. According to urban reachability calculation requirements of different scales, specific network radiuses (such as 800 meters, 1000 meters, 2000 meters and the like) are selected to calculate the traffic centrality and reachability of the local network. Different analysis radii mean the adaptability of the street organization structure to travel at corresponding distances, 500m is often considered to be a walking comfortable distance, and a large-scale radius is more suitable for commuting traffic such as vehicle driving. The selectivity reflects the potential of a certain street as the shortest path between any group of line segments in a street network, represents the accessibility of the road line segment as a necessary path for traversing traffic or traveling, and the high-selectivity road is often developed into a main traffic trunk system of a city; the calculation formula is as follows:
wherein NQPDA (x) is the degree of integration, P (y) is the weight of the node y in the search radius R, and P (y) is the element [0, 1] in the continuous space analysis]In discrete space analysis, the value of P (y) is 0 or 1; d M (x, y) is the shortest topological distance from the node x to the node y; w (y) is a custom weight. Further, the road accessibility in each cell is calculated as the average of all degrees of integration that fall within the cell, as shown in fig. 6, and visualized after normalization processing.
And S503, acquiring the land utilization mixing degree.
In an embodiment, the data analysis tool is used to extract the POI data of the map open platform, where the POI data includes attribute information such as name, type, location, and the like. The obtained POI data categories have the problems of errors, intersection, repetition and the like, so that the cleaning work of deleting error data and removing duplication is carried out on the original data, and then the original longitude and latitude of the cleaned POI data is required to be converted into a WGS-1984 geographic coordinate system as the original longitude and latitude is a Mars coordinate system. The POI data after coordinate conversion is re-classified, and POI types having similar attributes, for example, residential sites, commercial service sites, industrial sites, scientific and educational sites, medical service sites, transportation facility sites, travel sites, etc., are classified into a large group, with the purpose of avoiding the occurrence of redundant and abnormal values in the land use mixture calculation process due to excessive POI categories. Further, the degree of land use mixing in the cell is calculated: and calculating land utilization mixing degree aiming at the classified POI data, wherein the calculation formula is as follows:
H＝∑(Pi)(㏑Pi)
wherein, H represents the land use mixing degree, i represents the category number of POIs in a certain cellular unit, and Pi represents the ratio of the number of the ith category POIs in a certain cellular unit to the total number of the POIs in the cell. Finally, as shown in fig. 7, after normalization processing is performed on the land use mixture degree, visualization is performed.
And S504, obtaining the POI density.
Specifically, in the embodiment, in the process of obtaining the POI density, the processes of data obtaining, data cleaning and coordinate conversion are the same as those in step S503, which are not described herein, and the POI density is calculated by performing a kernel density estimation method on the POI data after coordinate conversion. The calculation formula is as follows:
where f (x) is the density estimate of the element at x, h is the window width or bandwidth, (x-x) i ) And k (—) is a weight function of the kernel, which is the distance between the ith element and the estimated element x. The POI density in each cell is then calculated as the mean of all the estimated nuclear densities falling within the cell, as shown at 8, and is visualized after normalization.
And S505, acquiring the building density.
Specifically, in the embodiment, building vector data in a map are acquired through a data analysis tool, and for the crawled building vector data, the GIS software is used for correcting and deleting error data to obtain the building vector data with geographic coordinate information. And calculating the building density aiming at the preprocessed building vector data, wherein the calculation formula is as follows:
wherein LD represents the building line density, n represents the number of building lines within the circumference; l represents a line length; v represents the weight of the line; a represents the circle area. The building density in each cell is further calculated as the average of the densities of all the building data lines falling into the cell. As shown in fig. 9, after normalization processing is performed on the building data line density, visualization is performed.
And S506, obtaining the light intensity at night.
Specifically, in the embodiment, the night light remote sensing image of the target area is acquired, and in order to ensure the projection consistency of various data, projection conversion is performed on the acquired night light remote sensing image. And then, cutting the projection-converted night light remote sensing image to obtain night light data of the target area. And then resampling the night light data: and performing grid resampling on the clipped night light data, so as to ensure that the night light data is unified with the resolution of other factors. Calculating the night light intensity in the honeycomb unit: the night light intensity in each honeycomb unit is all the night light intensity L falling into the honeycomb unit i The calculation formula is as follows:
wherein L is i Indicating the night light intensity of the ith cell; n is a radical of i Representing the total number of cells in the ith cell; l ij Representing the luminance value of the jth cell element falling within the ith cell. As shown in fig. 10, after the luminance values are normalized, visualization is performed.
And S507, acquiring salary level.
In particular embodiments, the recruitment data of the recruitment website is collected via a data analysis tool, including but not limited to recruitment company positioning, recruitment post, salary information, and the like. And then, performing coordinate transformation, wherein the step of coordinate transformation is the same as the coordinate transformation in step 300, and is not described herein again. Importing salary horizontal data into GIS software, selecting longitude and latitude data columns in the data by XY coordinates respectively, selecting room value by Z value, setting a projection coordinate system for data points, and screening out the data points falling into a research area; calculating to obtain the salary level of each cellular unit:
wherein X i Indicating the salary level of the ith cell; n is a radical of i Representing the total number of data points within the ith cell; x is the number of ij Indicating the salary level that falls within the jth data point in the ith cell. After the normalization process, the salary level is visualized as shown in fig. 11.
And S508, acquiring the room price level.
In particular, in the embodiment, the data collector is used to obtain the rate data of the research area in the house finding network, including but not limited to the house location, the rate, and the area size information. And then, performing coordinate transformation, wherein the step of coordinate transformation is the same as the coordinate transformation in step 300, and is not described herein again. Importing the room price data into GIS software, selecting longitude and latitude data columns in the data respectively by XY coordinates, selecting room value by Z value, setting a projection coordinate system for data points, and screening out the data points falling into a research area; calculate the rate level of each cell:
wherein F i Representing the rate level of the ith cell; n is a radical of i Representing the total number of data points within the ith cell; f. of ij Indicating the room price for the jth data point in the ith cell. After the normalization process, the level of the vacation is visualized as shown in fig. 12. In an embodiment, the step S500 of obtaining multi-source big data of a target area and extracting an influence factor, and determining the influence of urban vegetation on urban comprehensive vitality according to the influence strength of the influence factor on the urban comprehensive vitality includes the following steps:
and S510, calculating to obtain first influence strength according to vegetation coverage or non-vegetation influence factors.
Specifically, in the embodiment, the 8 impact factor data X1, X2, … and X8 extracted in steps S501 to S508 are acquired. Calculating the influence strength of 8 influence factors on the urban comprehensive activity, and measuring by using a q value, wherein the q value calculation formula is as follows:
wherein h represents the city unit in which the research area is located; h-1, 2,3, …, L representing the number of influencing factors; n is a radical of h Discrete variances representing the integrated activity of cities within the target zone,
SST＝Nσ 2 SWW and SST are the sum of the intra-layer variance and the total variance of the whole area, respectively. The q value represents the explanatory power of each factor to the city vitality difference, and the value range is [0,1]The larger the q value is, the better the factor explains the city vitality, and the weaker the factor explains the city vitality. Exemplary, the results of factor probing q values are shown in table 1:
TABLE 1
As shown in fig. 13, the 8 influencing factors have the lowest explanatory power of the urban comprehensive vitality under the action of a single factor, wherein the vegetation coverage (q ═ 0.0195) is the lowest explanatory power of the urban comprehensive vitality. The strongest explanatory power for the spatial differentiation of the urban comprehensive vitality in the 8 influence factors is POI density distribution, namely urban functional point density, and the explanatory power of the vegetation coverage is the weakest. The magnitude of the explanatory forces for all factors are ordered as: POI density > road accessibility > room price level > salary level > building density > night light intensity > land use mixing degree > vegetation coverage.
S520, calculating to obtain second influence strength according to the interaction influence of the vegetation coverage and the non-vegetation influence factors, and calculating to obtain the influence strength change value according to the first influence strength of the vegetation coverage and the second influence strength of the interaction of the vegetation coverage and the non-vegetation influence factors.
In the embodiment, the influence strength (q value) of two influence factors, such as X1 and X2, on the urban comprehensive vitality is calculated respectively, namely the explanatory power of the single-factor action on the urban vitality: q (X1) and q (X2), and then calculating the influence strength of the interaction on the urban comprehensive vitality: q (X1:. andX 2). The numerical results of the interaction are shown in table 2:
TABLE 2
Wherein, B represents double-factor enhancement, N represents nonlinear enhancement, the interaction q values of different factors are all larger than the q value of single-factor action, and are all in double-factor enhancement or nonlinear enhancement interaction relation, and are not mutually independent or weakened, which shows that the interaction of the factors can increase the explanatory power to the urban comprehensive vitality.
In an embodiment, the impact strength calculation results q (X1), q (X2) and q (X1 ≠ X2) of pairs are compared and an enhanced or reduced state is output. The two-factor interaction has the following five states:
1. when q (X1 ≠ X2) < Min (q (X1), q (X2)), the nonlinear attenuation state is set;
2. when Min (q (X1), q (X2)) < q (X1 ≠ X2) < Max (q (X1), q (X2)), it is the single-factor nonlinear attenuation state;
3. when q (X1 ^ X2) > Max (q (X1), q (X2)), the double-factor enhanced state is obtained;
q (X1 ═ X2) ═ q (X1) + q (X2), then the state of independent action;
q (X1:. n.x 2) > q (X1) + q (X2), which is the nonlinear enhancement state;
the enhancement status indicates that the interpretive power of the two factors on the urban comprehensive vitality is increased when the two factors interact compared with the single factor; the weakened state represents a weakening of the explanation for the overall vitality of the city when two factors interact compared to the single factor effect. When the interaction of the two factors is in an enhanced state, the two factors are jointly optimized, so that the comprehensive urban vitality can be better built; and when the interaction of the two factors is in a weakening state, the comprehensive vitality of the city can be better improved only by improving and optimizing the single factor.
Further, in the embodiment, the intensity of the interpretative force under the interaction of the vegetation coverage and other factors is calculated and visualized, as shown in fig. 14, the interpretative force of the urban vitality is obviously improved under the interaction of the vegetation coverage factor (X8) and other factors, so that an enhanced amplitude curve of the two-factor interaction needs to be further made to represent the difference between the interaction result of the factor and other factors and the single interaction result of the factor.
As shown in fig. 15, is an enhanced amplitude curve for two-factor interaction. The calculation formula of the intensity variation is as follows:
Δq i ＝q(Xi∩Xj)-q(Xi),(i＝1,2,...,n,j＝1,2,...,n,i≠j，n＝8)
wherein Xi and Xj each represent 8 factors, and when i is 1, j is 2,3 1 Q (X1 ≠ Xj) -q (X1); when i is 2, j is 1,3,4,5,6,7,8, Δ q 2 Q (X2 ≠ Xj) -q (X2); when i is 3, j is 1,2,4,5,6,7,8, Δ q 3 Q (X3 ≠ Xj) -q (X3); when i is 8, j is 1,2, 7, then Δ q 8 (Vegetation) Q (X8 n Xj) -q (X8) represents the intensity variation of the explanatory force of the urban comprehensive vitality under the interaction of the vegetation coverage factor and other influence factors.
The red lines in FIG. 15 indicate the following meanings: Δ q of 8 (Vegetation) Q (X8 ═ Xj) — q (X8), the other curves have similar meanings. As can be seen from comparison of the curves in fig. 15, the interpretive force of vegetation coverage with other factors has the highest pairwise interaction enhancement amplitude compared with other factors, and as shown in table 1, the value of single-factor effect of vegetation coverage is only 0.0195, which indicates that the single-factor effect of vegetation coverage has a small influence on urban vitality, but the influence on spatial heterogeneity of urban vitality is most remarkably enhanced under the interaction with other factors,the method reflects the spatial heterogeneity that the vegetation coverage does not directly act on the urban vitality, but indirectly influences the urban comprehensive vitality by coupling the influence factors such as spatial accessibility, POI density and building density. This enriches prior research efforts on the correlation of urban vigor with vegetation coverage. For example, in areas where building density is high, POI density is high, and green lanes and parks are built at the same time, vitality level is high; therefore, in the process of city construction, the urban greening should be emphasized, so as to stimulate the urban vitality. However, it is worth noting that in areas with dispersed buildings and inconvenient traffic, the farmland is distributed more, and the vitality basic value is lower, so the vegetation coverage degree needs to be balanced with the urban physical environment according to local conditions, and the urban vitality is favorably stimulated.
On the other hand, this application technical scheme still provides a system for confirming urban vegetation is to city comprehensive vigor influence intensity, and this system includes:
the multi-source big data acquisition unit is used for acquiring vector boundary data of a target area, and creating a cellular map of the target area according to the vector boundary data, wherein the cellular map comprises a plurality of cellular units;
the crowd gathering strength calculation unit is used for acquiring thermodynamic diagram data of a target area and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data;
the emotion intensity calculation unit is used for acquiring social network text data in the target region and determining emotion intensity in the cellular unit according to the social network text data;
the comprehensive vitality calculating unit is used for calculating urban comprehensive vitality according to the crowd gathering strength and the emotion strength;
and the influence intensity measurement unit is used for acquiring multi-source big data of the target area, extracting to obtain influence factors, and determining the change value of the influence intensity of the influence factors on the urban comprehensive vitality according to the detection result of the influence factors on the urban comprehensive vitality, wherein the influence factors comprise vegetation coverage and non-vegetation influence factors.
On the other hand, the technical scheme of the application also provides equipment for determining the influence strength of urban vegetation on the urban comprehensive vitality; it includes:
at least one processor; at least one memory for storing at least one program; when the at least one program is executed by the at least one processor, the at least one processor is caused to perform a method of determining the intensity of impact of urban vegetation on urban integrated vigor as in the first aspect.
The embodiment of the invention also provides a storage medium, which stores a corresponding execution program, and the program is executed by a processor, so that the method for determining the influence strength of urban vegetation on the urban comprehensive vitality is realized in the first aspect.
From the above specific implementation process, it can be concluded that the technical solution provided by the present invention has the following advantages or advantages compared to the prior art:
(1) the angle is novel: the emotion intensity of urban residents is considered, and the existing urban vitality evaluation research is perfected. The value of urban vegetation for urban vitality is focused on, and the quality of urban living environment is focused on.
(2) High aging: and acquiring thermodynamic diagram data and text data in real time and processing the thermodynamic diagram data and the text data in real time.
(3) High efficiency: based on the existing mathematical model and the GIS platform, the big data is analyzed and processed, complex repeated work is reduced, and the method is strong in operability and high in efficiency.
In alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flow charts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.
Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and/or features may be integrated in a single physical device and/or software module, or one or more of the functions and/or features may be implemented in a separate physical device or software module. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary for an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be understood within the ordinary skill of an engineer given the nature, function, and interrelationships of the modules. Accordingly, those skilled in the art can, using ordinary skill, practice the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative of and not intended to limit the scope of the invention, which is defined by the appended claims and their full scope of equivalents.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for determining influence strength of urban vegetation on urban comprehensive vitality is characterized by comprising the following steps:
acquiring vector boundary data of a target area, and creating a cellular graph of the target area according to the vector boundary data, wherein the cellular graph comprises a plurality of cellular units;
acquiring thermodynamic diagram data of the target region, and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data;
obtaining social network text data in the target region, and determining emotional intensity in the cellular unit according to the social network text data;
calculating according to the crowd gathering strength and the emotion strength to obtain city comprehensive activity;
obtaining an influence factor obtained by extracting multi-source big data of the target area, and determining the change value of the influence intensity of the influence factor on the urban comprehensive activity;
the influencing factors include vegetation coverage and non-vegetation influencing factors.
2. The method for determining the influence strength of urban vegetation on urban comprehensive vitality according to claim 1, wherein the step of obtaining the influence factor extracted from the multi-source big data of the target area and determining the change value of the influence factor on the influence strength of urban comprehensive vitality comprises at least one of the following steps:
calculating to obtain first influence strength of each factor according to the vegetation coverage or the single factor state of the non-vegetation influence factor;
and obtaining a second influence intensity according to the interaction state of the vegetation coverage and the non-vegetation influence factors, and calculating to obtain the influence intensity change value according to the first influence intensity of the vegetation coverage and the second influence intensity of the interaction of the vegetation coverage and the non-vegetation influence factors.
3. The method of claim 1, wherein the step of calculating the integrated urban vitality based on the crowd gathering intensity and the emotional intensity comprises:
carrying out data standardization processing on the crowd gathering strength and the emotion strength;
constructing an initial matrix according to a result after standardization processing, and carrying out standardization processing on the initial matrix to obtain a standardized matrix;
calculating to obtain a first distance according to the normalized matrix and a positive ideal solution, and calculating to obtain a second distance according to the normalized matrix and a negative ideal solution;
and calculating to obtain the urban comprehensive vitality according to the first distance and the second distance.
4. The method of claim 2, wherein the step of obtaining the multi-source big data of the target area, extracting an influence factor, and determining the change value of the influence factor on the urban comprehensive vitality comprises the following steps:
determining the action state of the second influence strength;
the action state comprises at least one of: a non-linear decrease state, a single-factor non-linear decrease state, a two-factor increase state, an independent action state, and a non-linear increase state.
5. The method of claim 1, wherein the non-vegetation affecting factors comprise at least one of: road accessibility, land use mix, point of interest density, building density, night light intensity, salary level, and house price level.
6. The method of claim 1, wherein the influence factor is vegetation coverage, the step of obtaining multi-source big data of the target area and extracting the influence factor to determine a change value of the influence factor on the urban comprehensive vitality comprises:
acquiring a remote sensing image;
performing orthorectification on the remote sensing image;
carrying out image fusion on multispectral and panchromatic data of the remote sensing image after orthorectification to obtain remote sensing image data;
and performing atmospheric correction on multispectral data of the remote sensing image data, and performing band operation on multispectral bands of the remote sensing image data after atmospheric correction to obtain the vegetation coverage.
7. The method of claim 3, wherein the step of constructing an initial matrix according to the normalized data and normalizing the initial matrix to obtain a normalized matrix comprises:
dividing the crowd gathering intensity into a working day crowd gathering intensity and a resting day crowd gathering intensity;
dividing the emotional intensity into a working day emotional intensity and a resting day emotional intensity;
and constructing the initial matrix according to a secondary index, wherein a row vector of the initial matrix represents the number of the honeycomb units, a column vector of the initial matrix represents a secondary index, and the secondary index comprises the working day crowd gathering strength, the rest day crowd gathering strength, the working day emotion strength and the rest day emotion strength.
8. A system for determining the intensity of impact of urban vegetation on the overall vitality of a city, the system comprising:
the multi-source big data acquisition unit is used for acquiring vector boundary data of a target area, and creating a cellular map of the target area according to the vector boundary data, wherein the cellular map comprises a plurality of cellular units;
the crowd gathering strength calculation unit is used for acquiring thermodynamic diagram data of a target area and determining crowd gathering strength in the honeycomb unit according to the thermodynamic diagram data;
the emotion intensity calculation unit is used for acquiring social network text data in the target region and determining emotion intensity in the cellular unit according to the social network text data;
the comprehensive vitality calculating unit is used for calculating urban comprehensive vitality according to the crowd gathering strength and the emotion strength;
and the influence intensity measurement unit is used for acquiring multi-source big data of the target area, extracting to obtain influence factors, and determining the change value of the influence intensity of the influence factors on the urban comprehensive vitality according to the detection result of the influence factors on the urban comprehensive vitality, wherein the influence factors comprise vegetation coverage and non-vegetation influence factors.
9. An apparatus for determining the impact of urban vegetation on the overall vitality of a city, comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to perform a method of determining the intensity of impact of urban vegetation on urban composite vigor as claimed in any one of claims 1-7.
10. A storage medium having stored therein a processor-executable program which, when executed by a processor, is adapted to perform a method of determining the intensity of impact of urban vegetation on urban general vitality as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210416740.8A CN114863272A (en) | 2022-04-20 | 2022-04-20 | Method and system for determining influence strength of urban vegetation on urban comprehensive vitality |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210416740.8A CN114863272A (en) | 2022-04-20 | 2022-04-20 | Method and system for determining influence strength of urban vegetation on urban comprehensive vitality |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114863272A true CN114863272A (en) | 2022-08-05 |
Family
ID=82631330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210416740.8A Pending CN114863272A (en) | 2022-04-20 | 2022-04-20 | Method and system for determining influence strength of urban vegetation on urban comprehensive vitality |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114863272A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052088A (en) * | 2023-03-06 | 2023-05-02 | 合肥工业大学 | Point cloud-based activity space measurement method, system and computer equipment |
-
2022
- 2022-04-20 CN CN202210416740.8A patent/CN114863272A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052088A (en) * | 2023-03-06 | 2023-05-02 | 合肥工业大学 | Point cloud-based activity space measurement method, system and computer equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446470B (en) | Medical facility accessibility analysis method based on vehicle trajectory data and population distribution | |
| Shang et al. | Estimating building-scale population using multi-source spatial data | |
| CN103336894B (en) | A kind of city plot ratio automatic Mesh Partition Method | |
| CN102646164A (en) | Land use change modeling method and system implemented in combination with spatial filtering | |
| CN115994685A (en) | Method for evaluating current situation of homeland space planning | |
| Ren et al. | Analysis of the spatial characteristics of inhalable particulate matter concentrations under the influence of a three-dimensional landscape pattern in Xi'an, China | |
| CN114863272A (en) | Method and system for determining influence strength of urban vegetation on urban comprehensive vitality | |
| CN114662774A (en) | City block vitality prediction method, storage medium and terminal | |
| Kazak et al. | Land use change in suburban zone: European context of urban sprawl | |
| Atubi et al. | Analysis of land use and land cover change characteristics in Warri metropolis, Nigeria | |
| CN112115844B (en) | Urban population data analysis method based on multi-source remote sensing image and road network data | |
| CN111754084A (en) | Method and system for assessing land reclamation potential of rural construction land | |
| Eslamirad et al. | Geoprocess of geospatial urban data in Tallinn, Estonia | |
| CN116415756A (en) | Urban virtual scene experience management system based on VR technology | |
| CN110070279A (en) | Ground utilization quantitative estimation method, system and storage medium based on multi-source data | |
| CN115239281A (en) | BIM + GIS-based road migration management system, method and storage medium | |
| Mao et al. | Population spatialization at building scale based on residential population index—A case study of Qingdao city | |
| CN113807724B (en) | Site selection method for slag disposal site based on comprehensive risk evaluation | |
| CN113239112B (en) | Third-production growth amount visualization method and device based on GIS system | |
| Liu et al. | Design and application of rural road lifecycle management information system | |
| Jimoh | Assessment of Urban Sprawl in Mowe/Ibafo Axis of Ogun State using GIS Capabilities | |
| Stephenson et al. | Compiling a land audit in large rural areas: Results from the methodology applied in the non-urban areas of the Matzikama municipal area | |
| Avtaeva et al. | The History of the Usage of GIS Technologies in Ecological and Faunistic Research | |
| Tsitsoni et al. | Creation of a system of monitoring, evaluation and management of urban greenery for the adaptation of cities in climatic change | |
| Keller | AN UPDATED ANALYSIS OF PRIORITY LANDS FOR CONSERVATION IN THE ELLERBE CREEK WATERSHED |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |