CN113344436A

CN113344436A - Tourism resource evaluation method and system based on GIS gridding fitting analysis

Info

Publication number: CN113344436A
Application number: CN202110728320.9A
Authority: CN
Inventors: 虞虎; 李鹏
Original assignee: Institute of Geographic Sciences and Natural Resources of CAS
Current assignee: Institute of Geographic Sciences and Natural Resources of CAS
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2021-09-03

Abstract

The invention relates to the technical field of tourism management, and provides a tourism resource evaluation method and system based on GIS grid fitting analysis. The tourism resource data objects in the area are set in the GIS according to the predetermined method, and the predetermined area and the tourism resource data objects are spatially gridded using at least two levels of scales to obtain gridded data. According to the fitting analysis results, the predetermined area is divided into several grade areas, and several different colors are used to correspond to several grade indicators to generate legends, and several grade areas are marked with corresponding colors to obtain the distribution of grade indicators. According to the fitting analysis results and the distribution of grade indicators, an evaluation conclusion is drawn to achieve the purpose of analyzing and evaluating tourism resources.

Description

Tourism resource evaluation method and system based on GIS gridding fitting analysis

Technical Field

The invention relates to the technical field of tourism management, in particular to a tourism resource evaluation method and system based on GIS grid fitting analysis.

Background

The tourism resource evaluation is a process of comprehensively analyzing, judging and identifying the scale, quality, grading and development prospect and external development conditions of the tourism resources in a certain area by selecting certain factors and applying a scientific method from the aspects of reasonably developing, utilizing and protecting the tourism resources and obtaining the maximum social, economic and environmental benefits on the basis of the tourism resource investigation. Through evaluation, scientific judgment standards can be provided for development scale, development theme, development stage and management planning of the tourist area, and theoretical bases are provided for investment, target market positioning, supporting facility construction and the like. Therefore, the evaluation of the tourism resources is the premise of correctly developing and utilizing the tourism resources and building the tourism area, the degree and the future of the development and the utilization of the regional tourism area are directly influenced, and the objective and scientific evaluation of the tourism resources is an important link of the comprehensive development and planning of the tourism area. However, the diversity and complexity of the tourism resources bring many difficulties to scientific evaluation, the existing tourism resource evaluation method is mainly limited to the evaluation of the contents such as the distribution characteristics of the tourism resource monomers, the integral attributes of the tourism resources and the like, a mechanical quantitative evaluation method or a qualitative evaluation method with strong subjectivity is often adopted, and the scientific evaluation can be made only by organically combining the quantitative evaluation method and the qualitative evaluation method. The current geographic information system GIS is generally applied to aspects of tourism resource structure analysis, tourism functional area identification, tourism facility site selection and the like, has simple service, can not meet the domestic market demand, has limited evaluation range and can not integrate more tourism resources.

Therefore, based on the above reasons, it is necessary to design a method and a system for evaluating tourism resources by using a GIS gridding technology and fitting analysis, so as to analyze and evaluate the tourism resources from the perspective of space science, and provide technical support and method reference for the technical field of tourism management.

Disclosure of Invention

Aiming at the technical problems in the prior art, the invention provides a tourism resource evaluation method and system based on GIS gridding fitting analysis, which are mainly realized by the following technical scheme.

According to a first aspect of the present invention, a method for evaluating tourism resources based on GIS gridding fitting analysis is provided, which comprises:

determining at least one of the following travel resource evaluation types: developing potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation; determining a plurality of grade indexes contained in the travel resource evaluation type; acquiring a tourism resource data object corresponding to the tourism resource evaluation type in a preset area, and setting the tourism resource data object in a GIS according to a preset mode; spatial gridding is carried out on the preset area in the GIS by adopting at least two levels of scales, and gridding modeling is carried out on the tourism resource data object to obtain gridded data; and respectively performing fitting analysis on the gridding data by adopting at least two fitting methods as follows: a global polynomial GPI fitting method, a local polynomial LPI fitting method, an inverse distance weight IDW fitting method, a radial basis function RBF fitting method and a Kriging fitting method; dividing the preset area into a plurality of grade areas corresponding to the quantity of the grade indexes according to the fitting analysis result; generating a legend corresponding to the plurality of grade indexes by adopting a plurality of different colors; marking the plurality of grade areas with corresponding colors to obtain the distribution condition of grade indexes; and obtaining an evaluation conclusion according to the fitting analysis result and the grade index distribution condition.

Further, the method also comprises the step of comprehensively evaluating the tourism resource evaluation type by adopting tourism resource grid, wherein the tourism resource grid comprehensive evaluation comprises the following steps: the method comprises the following steps of development potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation.

Further, the several ranking indicators include: developing potential grid evaluation optimization indexes, tourism traffic zone grid evaluation optimization indexes, tourist data space grid evaluation optimization indexes, tourism economic industry development level grid evaluation optimization indexes and tourism resource restriction factor grid evaluation optimization indexes.

Further, the plurality of grade indexes are five grade indexes.

Further, the corresponding travel resource data object comprises part or all of the following data objects: tourist resource monomer space data, administrative district space data, traffic space data, water system space data, data related to the statistical yearbook, tourist number statistical data and natural disaster statistical data.

Further, the at least two level scales are scales of three levels, 0.5km, 1km and 2 km.

Further, obtaining an evaluation conclusion according to the fitting analysis result and the distribution condition of the plurality of grade indexes, wherein the evaluation conclusion comprises: an optimal fitting method, a gridding scale of an optimal level, a spatial distribution condition of a level index and reasons.

According to another aspect of the present invention, there is also provided a tourism resource evaluation system based on GIS gridding fitting analysis, including:

the evaluation type selection module is used for selecting at least one of the following travel resource evaluation types: developing potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation; the grade index management module is connected with the evaluation type selection module and is used for dividing the selected evaluation type of the tourism resource into a plurality of grade indexes; the tourism resource data object management module is connected with the evaluation type selection module and is used for managing tourism resource data objects corresponding to the tourism resource evaluation types in a preset area; the gridding scale level selection module is connected with the tourism resource data object management module and is used for selecting a certain level scale to carry out spatial gridding to obtain gridding data; the fitting method selection module is respectively connected with the evaluation type selection module and the grade index management module and is used for selecting at least two fitting methods to perform fitting analysis on the gridding data; the fitting result generation module is connected with the fitting method selection module and used for generating a fitting analysis result and a grade index distribution condition; and the analysis conclusion generation module is connected with the fitting result generation module and used for obtaining an evaluation conclusion according to the fitting analysis result and the grade index distribution condition.

The system further comprises a tourism resource data object receiving module which is connected with the tourism resource data object management module and used for receiving the tourism resource data object.

And the default selection module is respectively connected with the evaluation type selection module, the gridding scale level selection module and the fitting method selection module and is used for setting default options according to a preset mode.

Compared with the prior art, the tourism resource evaluation method and system based on GIS gridding fitting analysis provided by the invention solve the problems that the evaluation in the related technology is not scientific enough, the quantitative evaluation method and the qualitative evaluation method cannot be organically combined, the application range is limited, and more tourism resources cannot be integrated, provide a new tourism resource evaluation method and system, and improve the user experience.

Drawings

Fig. 1 is a flowchart of a travel resource evaluation method based on GIS gridding fitting analysis according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of spatial data of a single classified travel resource in Qingdao city according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the spatial data of the hierarchical tourist resource monomers in Qingdao city according to an embodiment of the present invention;

FIG. 4 is a schematic diagram of a GIS tourism resource development potential grid model respectively adopting a 0.5km scale according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a GIS tourism resource development potential grid model respectively adopting a 1km scale according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a GIS tourism resource development potential grid model respectively adopting a 2km scale according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a GIS tourist resource tourist traffic zone grid modeling model respectively adopting scales of 0.5km, 1km and 2km according to an embodiment of the present invention;

fig. 8 is a schematic diagram of a spatial grid model of GIS tourists respectively adopting scales of 0.5km, 1km and 2km according to an embodiment of the present invention;

fig. 9 is a schematic diagram of a horizontal grid modeling model for GIS tourism economic industry development, which is provided by an embodiment of the present invention and respectively adopts scales of 0.5km, 1km, and 2 km;

fig. 10 is a schematic diagram of a GIS tourism resource restriction factor grid model respectively adopting scales of 0.5km, 1km and 2km provided by the embodiment of the present invention;

fig. 11 is a schematic diagram of a GIS tourism economic industry development level model respectively adopting scales of 0.5km, 1km and 2km provided by an embodiment of the present invention;

fig. 12 is a block diagram of a structure of a travel resource evaluation system based on GIS gridding fitting analysis according to an embodiment of the present invention;

FIG. 13 is a block diagram of another GIS gridding fitting analysis-based tourism resource evaluation system according to an embodiment of the present invention;

reference numerals:

the system comprises an evaluation type selection module-1, a grade index management module-2, a tourism resource data object management module-3, a gridding scale grade selection module-4, a fitting method selection module-5, a fitting result generation module-6, an analysis conclusion generation module-7, a tourism resource data object receiving module-8 and a default selection module-9.

Detailed Description

In order to make the technical field of the invention better understand the scheme of the invention, the scheme of the embodiment of the invention is clearly and completely described below in combination with the attached drawings in the embodiment of the invention, and obviously, the described embodiment is only a part of the embodiment of the invention, but not the whole embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example one

The embodiment of the invention provides a tourism resource evaluation method based on GIS gridding fitting analysis, and FIG. 1 is a flow chart of the evaluation method according to the embodiment of the invention, as shown in FIG. 1, the method comprises the following steps:

step S101, determining at least one of the following travel resource evaluation types: developing potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation;

step S102, determining a plurality of grade indexes contained in the evaluation type of the tourism resource;

step S103, acquiring a tourism resource data object corresponding to the tourism resource evaluation type in a preset area, and setting the tourism resource data object in a GIS according to a preset mode;

step S104, performing spatial gridding on the preset area in the GIS by adopting at least two level scales respectively, and performing gridding modeling on the tourism resource data object to obtain gridded data;

step S105, performing fitting analysis on the gridded data by respectively adopting at least two fitting methods as follows: a global polynomial GPI fitting method, a local polynomial LPI fitting method, an inverse distance weight IDW fitting method, a radial basis function RBF fitting method and a Kriging fitting method;

step S106, dividing the preset area into a plurality of grade areas corresponding to the quantity of the grade indexes according to the fitting analysis result;

s107, generating a legend corresponding to the grade indexes by adopting a plurality of different colors;

step S108, marking the plurality of grade areas by adopting corresponding colors to obtain the distribution condition of grade indexes;

and S109, obtaining an evaluation conclusion according to the fitting analysis result and the grade index distribution condition.

It should be noted that, because the types of the data objects of the tourism resources are various, the contents of the tourism resources to be evaluated are also various, and therefore, the adopted tourism resource evaluation type needs to be determined according to the actual needs, in the embodiment of the present invention, the following commonly used tourism resource evaluation types are listed: development potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation, and it should be understood that embodiments obtained by adopting other tourism resource evaluation types without creative labor by ordinary technicians in the field all belong to the protection scope of the invention.

The plurality of grade indexes are evaluation indexes with different grades contained in the travel resource evaluation type, and the GIS is an English abbreviation of a geographic information system.

In step S105, fitting and analyzing the gridded data by using at least two fitting methods, so as to compare the fitting and analyzing results obtained by the at least two fitting methods, and obtain an analysis conclusion of the optimal fitting method.

It should be noted that, in the fitting method adopted in the embodiment of the present invention, the global polynomial GPI fitting method solves the predicted value through a polynomial according to the value of the total tourist resource point in the research area; the local polynomial LPI fitting method is also a non-exact fitting model, which uses several polynomials located in the neighborhood of a specific overlap to construct a smooth surface.

The inverse distance weight IDW fitting method is based on the similar similarity principle and carries out weighted average by taking the distance between the fitting point and the observation point of the tourist resource as the weight. The specific formula is as follows:

in the formula:

is the target interpolation; z (x)_i,y_i) Is the observed value of the tourism resource of the ith grid center; d_iThe distance from the ith tourist resource observation value to the interpolation point; lambda [ alpha ]Representing the weight, the closer the travel resource observation value is to the interpolation point, the higher the weight is.

The RBF fitting method is composed of functions of single variables and is suitable for fitting operation of a large amount of tourist resource point data. In GIS, the RBF typically includes different basis functions such as a tension spline function, a regular spline function, a higher order surface function, and the like. For given multivariate scattered data

Selecting a radial function phi R^*→ R, constructing basis function system by translation

And finding a fitting function s (X) in the form of:

wherein, the fitting condition is satisfied:

s(X_j)＝f_j；

and an orthogonality condition:

solving the following equation yields the coefficient λ_jAnd c1, c2, c3, c 4.

In the fitting method adopted by the embodiment of the invention, the Kriging fitting method is based on the geostatistics of a variation function theory basis as a core, the unbiased optimal estimation of the regional variables in a specific range is realized, and the characteristics of the spatial position, the size, the shape and the like estimated by an observed value are considered. Epsilon is usually reflected by a half-variance function r (h)₁How much(s) a semi-variogram is a singly increasing function of h, i.e. the closer the distance is to its nullThe smaller the inter-variability, the formula is as follows: z(s) ═ m(s) + γ (h) + ε₂。

In the formula: z(s) is an interpolated value; m(s) represents the spatial trend change of the observed value of the tourism resource; epsilon₁(s) represents a variant; epsilon₂Representing a residual error term; and s is the spatial positioning of the observed value of the tourism resource. The formula for r (h) is:

N(h)＝{(s_i,s_j):s_i-s_j＝h；i,j＝1,2,…,n}

in the formula: and N (h) point pair sets representing the observed value evaluation results of the tourism resources. N is the total number of observations; the semivariogram primarily determines the weighting factor lambda_i. As in ordinary kriging, the spatial position s is assumed₀Is a linear combination of observed values:

under the condition of minimum mean square error of the predicted value, the following objective equation can be obtained:

in the formula: m is a Lagrange multiplication constant, and further,

further can obtain

Respectively to lambda_i(i is 1,2, …, n) and m, and the resulting partial derivatives are made 0, and the parameter values obtained are as follows:

m＝-(1-1Γ^-1r)/(1Γ^-11)

where λ' ═ λ (λ)₁,λ₂,...,λ_n),r＝(r(s₀-s₁),r(s₀-s₂),…,r(s₀-s_n) ') and Γ is an n × n matrix.

Different types of kriging fitting methods are determined for m(s), r (h), λ, etc. in 3 aspects. Wherein m(s) in Ordinary Kriging (OK) is unknown constant,

simple Kriging (SK), m(s) being a known constant, λ being unlimited; the basic assumption of the Universal Kriging (UK) fitting model is E (m (s)) u(s),

it should be noted that, the travel resource data object is to obtain the relevant data corresponding to the travel resource evaluation type in the predetermined area according to the actual need of the travel resource evaluation type, including but not limited to: the system comprises classified tourism resource single space data, administrative region space data, traffic space data, water system space data, data related to statistical yearbook, statistical data of tourist number and natural disaster statistical data.

Meanwhile, the travel resource data object is set in a GIS according to a predetermined manner, taking the Qingdao city as an example, fig. 2 is a schematic diagram of spatial data of a classified travel resource monomer in the Qingdao city provided by an embodiment of the present invention, and fig. 3 is a schematic diagram of spatial data of a classified travel resource monomer in the Qingdao city provided by an embodiment of the present invention.

As shown in fig. 2, in the present embodiment, 3661 total tourism resource units in the Qingdao city tourism resource data objects are collected, wherein the total number of the natural tourism resource units is 1140, which accounts for 31.14% of the total number of the units, the total number of the human tourism resource units is 2521, which accounts for 68.86% of the total number of the units, the tourism resource data objects are specifically divided into eight types, namely, a geographical landscape, a water scene, a biological landscape, an astronomical and climatic landscape, an ruined site and vestige, a building and a facility, a tourism commodity and a human activity, and the tourism resource data objects are set in a GIS according to a predetermined manner; as shown in fig. 3, the total number of the first-level, second-level, third-level, fourth-level and fifth-level human tourism resource monomers in the whole city is 497, 412, 186, 41 and 4 respectively, and each of the monomers accounts for 43.60%, 36.14%, 16.31%, 3.60% and 0.35% of the total number of the tourism resources, the tourism resource data object is specifically divided into the first-level to fifth-level tourism resource monomers, and the tourism resource data object is set in the GIS according to a predetermined manner.

Preferably, the tourism resource evaluation type adopting development potential grid evaluation is determined.

Taking Qingdao city as an example, fig. 4, 5 and 6 are schematic diagrams of a GIS tourism resource development potential gridding model respectively adopting scales of 0.5km, 1km and 2km provided by the embodiment of the present invention, which will be described in detail below:

as shown in FIG. 4, FIG. 5 and FIG. 6, an improved model of the development potential of the tourist resource group is selected, and the simulation is performed by using the tourism resource gridding with the scale of 0.5km, 1km and 2km respectively. Determining a plurality of grade indexes to be a special-grade tourism resource group, a good-grade tourism resource group, a medium-grade tourism resource group, a secondary tourism resource group and a poor-grade tourism resource group in sequence, weakening the development potential in sequence, and respectively performing fitting analysis on the gridding data by adopting the following five fitting methods: global polynomial GPI fitting method, local polynomial LPI fitting method, inverse distance weight IDW fitting method, radial basis function RBF fitting method, Kriging fitting method.

The radial basis function RBF fitting method comprises the following three models: the method comprises a tension spline function (SWT) model, a regular spline function (CRS) model and a high-order surface function (MF) model, wherein the Kriging fitting method comprises the following two models: common kriging (OK) models, Simple Kriging (SK) models.

Selecting an index prediction error mean value and a root mean square prediction error evaluation tourism resource development potential gridding model, wherein the table 1 is an error analysis table, the root mean square prediction errors of the fitting models are different in size, and the fitting model with a smaller value is selected. Wherein, the RBF-based tension spline function, the regular spline function, the higher-order surface function and the simple Krigin function are selected under the scale of 0.5km grid and are mainly considered in the research, and the RBF-based tension spline function, the regular spline function and the simple Krigin function are selected under the scale of 1km grid and are mainly considered in the research; local polynomial fitting models, tension spline functions and high-order surface function fitting are selected under the 2km grid scale and are considered in key during research, and the local polynomial fitting models, the tension spline functions and the high-order surface function fitting are shown in table 1:

TABLE 1

In summary, the following evaluation conclusions are drawn: the tourism resource development potential results obtained by different gridding methods on the same scale have certain difference, and the gridding results of different scales have different spatial characteristics: the grid of 0.5km scale is fine, the calculation result is smooth in spatial display, the grid granularity is obvious, and the tourism resources have obvious north-south difference in the overall layout; after the scale of 1km is increased, the spatial distribution condition of the development potential of the tourism resources can be obviously expressed, which shows that the tourism hotspots in Qingdao cities are excessively concentrated in Laoshan, coastal areas and other mature long and narrow areas, the tourism development space is lack of depth, and the tourism resources in inland areas are not effectively developed; the 2km scale shows the spatial distribution of the development potential of the tourism resources more thickly, the grid particles are distributed more obviously, and more obvious special-grade and excellent-grade tourism resource groups appear in main urban areas, namely Laoshan mountain and Daze mountain.

Preferably, the tourism resource evaluation type of the grid-based evaluation of the tourism traffic area is determined.

Taking Qingdao city as an example, fig. 7 is a schematic diagram of a grid networking model of a GIS tourist resource tourist traffic area respectively adopting scales of 0.5km, 1km and 2km, which is provided by an embodiment of the present invention and will be described in detail below:

as shown in fig. 7, the main road data of the Qingdao city is selected, and the gridding accessibility under the multi-level scale is directly constructed by combining the road network density model of each region city. The five grade indexes for determining the tourism resource evaluation type comprise an excellent traffic zone, a general traffic zone, a poor traffic zone and a poor traffic zone.

Analysis shows that under the scale of 0.5km, the northern part of the glue bay of the Qingdao road is obviously distributed in an inverted region shape and is diffused along the administrative center of each row in a point-axis mode, the southern part of the road is mainly distributed along the sea, 11 routes such as Jinqing high speed, Huanjiao high speed, Shunkong high speed and Qinglai high speed are successively established in the whole city, wherein the glue bay tunnel and the cross-sea bridge form a link of 'blue and yellow connection', and the time is greatly shortened for the Qingdao travel. Under the scale of 1km, the areas with the highest highway density are concentrated in the gulf of Oncao and the developed economic areas in the north; the reachability measurement patterns of the city of flatness and the city of leisy show remarkable high-speed pointing characteristics. Under the scale of the 2km grid, the accessibility level of the west coast and the north of the southwest Qingdao region, which mainly comprise the south China jiao city, to the Lai West region is gradually reduced by taking the urban region as a core region.

Preferably, the tourism resource evaluation type of the tourism resource evaluation by adopting the tourism data spatial grid evaluation is determined.

Taking Qingdao city as an example, fig. 8 is a schematic diagram of a spatial grid model of GIS tourists data respectively adopting scales of 0.5km, 1km and 2km provided by an embodiment of the present invention, which will be described in detail below:

as shown in fig. 8, several grade indexes for determining the evaluation type of the tourist resources are divided into five grades according to the clustering degree of tourists in the whole market: the tourist high collecting area, the tourist medium collecting area, the tourist general collecting area and the tourist rare area.

Wherein, the distribution of tourists is integrated by the factors of tourism resources, supporting facilities, traffic areas and the likeInfluence, initial selection of Tourism resource development potential (DX)₁) Distance between main and urban areas (DX)₂) Distance to trunk (DX)₃) Star hotel Distance (DX)₄) POI center Distance (DX) in travel industry₅) Distance of population concentration area (DX)₆) And 6 influence factors are used as the spatial grid simulation of the tourist data. Table 2 is a GWR model fitting result table, and after the sample data is respectively standardized, a GWR model of the Qingdao city tourist data is constructed, and the adjustment type spatial kernel regression and the AICc method are selected to analyze and simulate the Qingdao city tourist data. The minimum value of the AICc is obtained by calculation when the number of adjacent points is 55. R²Is 0.8651, correcting for R²The value of (d) is 0.7981, and the fitting effect is better at this time, as shown in table 2:

residual value (Residual)	Sigma	AICc	R²	Correcting for R²	Sum of squares of residuals
						0.3221	0.1409	-47.8363	0.8651	0.7981	0.9528

TABLE 2

And the table 3 is a GWR model estimation result table, and data such as the average value, the minimum value, the maximum value, the standard deviation, the lower quartile, the upper quartile and the like of the influence degree of each factor on the tourist data distribution are obtained through GWR model estimation. The development potential of the tourism resource shows a remarkable influence, which shows that the larger the development potential of the tourism resource is, the more tourists are; distance between main and urban areas (DX)₂) The average value and the median of the index influence regression coefficient are positive numbers, namely, the number of tourists is improved along with the increase of the distance of tourist resources along with the main urban area; distance to trunk (DX)₃) The average value of the index influence regression coefficient is also positive, but from the Qingdao city, the influence of the traffic zone on the tourist distribution is low, mainly because the Qingdao city is convenient to traffic and famous scenic spots can be reached smoothly; while the star hotel>Population gathering area>The average coefficients of the tourism industry are negative values, which indicates that various facilities have positive promotion effect on the distribution of tourists, and are shown in table 3:

TABLE 3

And substituting the coefficient index value obtained by GWR analysis into a tourist data gridding model in Qingdao city to further obtain the spatial distribution pattern of tourists in the whole city, wherein five grades of the clustering degree of the tourists in the whole city are obtained, a large number of tourists are intensively distributed in the main city area and radiate to the north, and the clustering characteristics are shown in a central point manner in other areas such as flatness and south China along the east part of the coastline and the second place of the gulf in Jiaozhou.

Preferably, the grid evaluation type of the tourism economy industry development level is determined.

Taking Qingdao city as an example, fig. 9 is a schematic diagram of a grid model of the GIS tourism economic industry development level respectively adopting scales of 0.5km, 1km and 2km provided by the embodiment of the present invention, which will be described in detail below:

firstly, determining a plurality of grade indexes of the evaluation type of the tourist resources, and dividing the grade indexes into five grades according to the clustering degree of tourists in the whole city: the method comprises the following steps of performing fitting analysis on grid data by adopting the following five fitting methods respectively: global polynomial GPI fitting method, local polynomial LPI fitting method, inverse distance weight IDW fitting method, radial basis function RBF fitting method, Kriging fitting method.

In the specific implementation process, the tourism status of each district city of Qingdao is selected, indexes such as A-level scenic spots, hotels above 3 star level, travel agencies, star-level restaurants, golf courses, tourist shopping malls and the like are selected, longitude and latitude coordinates of various POI are obtained through a network map API, and the development level of the tourism industry in each grid unit is analyzed. By measuring and calculating the space density of the tourism industry in each level of scale grid unit, the tourism industry has radiation effect on the periphery of the tourism industry within a certain range, so that the final result is subjected to fitting analysis by multiple models such as IDW, GPI, LPI, SWT, CRS, MF, OK, SK and the like, and the simulation result is shown in FIG. 9.

According to the analysis results of the embodiment, the local polynomial fitting and the common kriging fitting under the 0.5km grid scale have the best effect, the average prediction errors are-0.151 and 0.054 respectively, and the root mean square prediction errors are 3.172 and 7.391 respectively; the high-order surface function, the ordinary kriging method and the simple kriging fitting method are selected under the scale of 1km grid and are mainly considered in research, the average prediction errors are respectively 0.220, 0.021 and 0.223, and the root-mean-square prediction errors are respectively 8.280, 7.391 and 2.836; the fitting effect of the high-order surface function and the local polynomial fitting model is optimal under the 2km grid scale, the average prediction errors are respectively 0.659-0.200, the root mean square prediction errors are respectively 15.004 and 13.705, and table 4 is an error analysis result table:

TABLE 4

Through the analysis of the embodiment, the core distribution of the tourism industry can be more prominent under the scale of 0.5km grid which is sporadically distributed in the gulf of the islands of the national islands, the coastal areas of the eastern China and the urban areas, which also shows that the areas with higher development level of the tourism industry are highly related to the developed degree of urban economy, and the development of the tourism industry can be directly influenced by the quality of the economic development; the distribution range under the scale of 1km is distributed in Qingdao urban areas and important tourism areas, so that the tourism status of the Qingdao urban tourism industry is continuously improved along with the high-speed development of economy; the areas around the tourist industry gathering place under the 2km scale are mostly tourist resource gathering places, such as the old ink wine house, the Guzhou culture street, the Laxi Xisha ancient ruins, the plum blossom mountain ecological sightseeing garden and the like, which shows that the Qingdao tourist industry benefits from the good urban environment.

Preferably, determining the tourism resource evaluation type by adopting the tourism resource restriction factor grid evaluation.

Taking Qingdao city as an example, fig. 10 is a schematic diagram of a GIS tourism resource restriction factor grid model respectively adopting scales of 0.5km, 1km and 2km provided by the embodiment of the present invention, which will be described in detail below:

as shown in fig. 10, several grade indexes for determining the evaluation type of the tourist resources are divided into four grades according to the clustering degree of tourists in the whole market: the method can be used for developing areas, sub-dangerous natural disaster areas, dangerous natural disaster areas and extremely dangerous natural disaster areas.

The main restriction factor considering the tourism resource development of Qingdao city is natural disasters, and various kinds of disasters are selected to have XH frequency₁And disaster-suffering tourist capacity XH₂Economic loss XH₃Amount of travel resources XE₁Infrastructure perfection XE₂Number of annual reception visitors XE₃Travel income XE₄Number of national focus points of interest XV₁Key protection unit number XV₂The method comprises the following steps of establishing a gridding model by indexes such as tourism facility perfection degree XV3, tourist age structure ratio XV4, tourism GDP contribution rate XV5, traffic conditions XR1, refuge number XR2, rescue capacity XR3, tourism insurance perfection degree XR4 and hospital health place bed number XR5 (data comes from Qingdao city statistics yearbook in 2019), then calculating hierarchy single ordering, determining weight values of importance of each element connected with the hierarchy, namely judging characteristic roots and characteristic vectors of a matrix B by calculation:

BW＝λ_maxW。

in the formula: lambda [ alpha ]_maxIs the largest characteristic root of matrix B, and W is the root corresponding to λ_maxIs normalized to the vector of (1). In order to check and judge the consistency of the matrix, the requirement solution index CI:

to determine whether the matrix has consistency, the CI and the RI are compared, generally when

If so, the decision matrix is considered to be true, otherwise, the decision matrix needs to be adjusted, wherein the random consistency index is shown in table 5:

TABLE 5

The calculated result of the single ordering of the matrix of each level is shown in table 6:

TABLE 6

And finally, calculating the total hierarchical ordering. The weight of all elements in the layer relative to the previous layer can be calculated according to the results of all single ordering in the same layer, wherein the index weight results are shown in table 7:

index (I)	X_H1	X_H2	X_H3	X_E1	X_E2	X_E3	X_E4	X_V1	X_V2
										Weight value	0.191	0.163	0.12	0.057	0.045	0.026	0.061	0.031	0.028
Index (I)	X_V3	X_V4	X_V5	X_R1	X_R2	X_R3	X_R4	X_R5
										Weight value	0.014	0.029	0.016	0.022	0.057	0.023	0.083	0.034

TABLE 7

Respectively establishing 0.5km x 0.5km and 2km gridded simulation distribution models according to the principle, obtaining combined weighting results through a linear weighting method, taking the weighting coefficient to be 0.5, and calculating to obtain combined weight W_j(j ═ 1,2,3 … 17)), wherein the geological disaster grid evaluation index weights are shown in table 8:

TABLE 8

According to the gridding index weighted value of the embodiment of the invention, a tourism resource disaster risk index is constructed, and the area with the strongest geological disaster effect in Qingdao city is found to be mainly distributed in one area of Laoshan, Betty mountain and big Betty mountain; the collapse geological disaster has weaker effect and is mainly distributed in Wangchuang office and sand mouth office in the Laoshan area; landslide geological disasters are mainly in Ling mountain island in south jiao city, and Daze mountain town in flatness city; the scale of the geological disaster of the debris flow is small, and the geological disaster is mainly distributed in Laoshan areas and large Zealand towns in the flatness city.

Therefore, in the embodiment of the invention, compared with the prior art, the tourism resource evaluation method based on GIS gridding fitting analysis at least has the following technical effects: the quantitative evaluation method and the qualitative evaluation method are organically combined, so that the tourism resource evaluation is more scientific, the application range is wider, a novel tourism resource evaluation method is provided, and the user experience is improved.

In a preferred embodiment, an evaluation conclusion is obtained according to the fitting analysis result and the distribution condition of the plurality of grade indexes, and the evaluation conclusion comprises: an optimal fitting method, a gridding scale of an optimal level, a spatial distribution condition of a level index and reasons.

In the specific implementation process, by comparing various fitting methods and the fitting results of grid formation of all levels of scales, the optimal fitting method with small error, good grid formation effect and stability and the grid formation scale of the optimal level are obtained, and the spatial distribution conditions of all levels of indexes are explained, such as a main distribution area with the maximum development potential, a main distribution area of tourists, a main area of natural disaster distribution and the like, and the reasons of the spatial distribution conditions of all levels of indexes are explained, so that the correlation can be obtained through the change analysis of different related tourism resource data, for example: the measures find that the tourism resource development potential has the greatest influence on tourists, and the tourism industry is the star hotel > the population gathering area >.

In the embodiment of the invention, the optimal fitting method, the optimal level gridding scale, the spatial distribution condition of the level index and the reason are analyzed, so that the quantitative evaluation method and the qualitative evaluation method are organically combined, and the evaluation of the tourism resources is more scientific.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the quantitative evaluation method and the qualitative evaluation method are organically combined, the evaluation of the tourism resources is more scientific, and the user experience is improved.

Example two

On the basis of the first embodiment, another tourism resource evaluation method based on GIS gridding fitting analysis is further provided in the embodiments of the present invention, as shown in fig. 1, including:

the steps S101 to S109 and the specific implementation thereof can refer to the detailed description in the first embodiment, and are not described again in the embodiments of the present invention.

Step S110, the tourism resource evaluation type adopts tourism resource grid comprehensive evaluation, and the tourism resource grid comprehensive evaluation comprises the following steps: the method comprises the following steps of development potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation.

The grid-type comprehensive evaluation of the tourism resources in the step S110 includes five evaluation types, namely, development potential grid-type evaluation, tourism traffic zone grid-type evaluation, tourist data space grid-type evaluation, tourism economic industry development level grid-type evaluation and tourism resource restriction factor grid-type evaluation.

In the embodiment of the invention, the tourism resource data objects corresponding to all five evaluation types are subjected to grid fitting analysis to obtain five types of analysis results, and the five types of analysis results are subjected to grid fitting analysis in the GIS by the method in the first embodiment to obtain comprehensive indexes and conclusions, so that the coverage range is wider, the tourism resource analysis and evaluation in the preset area are more comprehensive, and the tourism resource analysis and evaluation is more scientific.

In a preferred embodiment, the several ranking indicators include: developing potential grid evaluation optimization indexes, tourism traffic zone grid evaluation optimization indexes, tourist data space grid evaluation optimization indexes, tourism economic industry development level grid evaluation optimization indexes and tourism resource restriction factor grid evaluation optimization indexes.

In the specific implementation process, taking Qingdao city as an example, fig. 11 is a schematic diagram of a model of development level of GIS tourist economy industry using scales of 0.5km, 1km and 2km respectively according to an embodiment of the present invention, which will be described in detail below:

in order to further construct a grid comprehensive evaluation model of tourist resources in Qingdao city, the five grade indexes are summarized into two types of comprehensive evaluation indexes:

the first type of comprehensive evaluation indexes are as follows: the method comprises 3 indexes of tourism economic development level, traffic dominance degree, tourist concentration degree and the like, and the indexes describe the economic and social development conditions of the tourism resource area from different angles:

the second type comprehensive evaluation index: the method comprises the following steps of developing potential of tourism resources and natural disaster risk, wherein the potential and the natural disaster risk reflect the supporting conditions of regional tourism development: p₂Tourism resource development potential/natural disaster risk

On the basis of index classification and merging, a comprehensive evaluation index (A) for tourism development is constructed, and the calculation method comprises the following steps: a ═ kp₁+p₂

In this embodiment, referring to the technical specification of division of provincial main body functional areas, that is, K is 1.1, substituting corresponding values of P1, P2 and K into a calculation formula, dividing the tourism development comprehensive evaluation index result of each island city into five grades according to a natural fracture method, and analyzing and finding that the development of the tourism industry of the Qingdao city forms a spatial development pattern of 'one heart, one belt, three shafts and five zones'. In the aspect of comprehensive evaluation of tourism resources, the size of a 0.5km-2km scale grid is moderate, and the Kriging fitting effect is relatively stable from the viewpoint of an evaluation model; in the aspect of grid measurement and calculation of traffic areas, the northern parts of the Taizhou bay of the Taoist of Qingdao city are distributed in an inverted 'area' shape and are diffused along each administrative center in a point axis manner; in the aspect of tourist market measure, tourists mainly focus on radiation with a main urban area as a center, and the influence of tourism resource development potential on the tourists is found through the measure, and then star-level hotels, population gathering areas and tourism industry are realized; in the aspect of evaluating the development level of the tourism industry under the grid scale of 0.5km-2km, the effect of adopting a local polynomial fitting method and a common kriging fitting method is optimal.

In the embodiment of the invention, by adopting the tourism resource evaluation type of the tourism resource grid comprehensive evaluation, the grid comprehensive evaluation of the tourism POI data, the economic and social data, the traffic data, the disasters, the ecology and other multi-source data under different scales of multiple indexes is carried out, so that a more comprehensive and scientific grid evaluation method of the tourism resource is provided, and the application range is wider.

Therefore, in the embodiment of the present invention, compared with the prior art, the method for evaluating tourist resources based on GIS gridding fitting analysis has the following technical effects in addition to the technical effects of the above embodiment: the quantitative evaluation method and the qualitative evaluation method are organically combined, so that the evaluation of the tourism resources is more scientific, the application range is wider, and the user experience is improved.

In the implementation of the present invention, the detailed description of the first embodiment can be referred to, and is not repeated in the embodiments of the present invention.

EXAMPLE III

On the basis of the first embodiment, the embodiment of the invention also provides another tourism resource evaluation method based on GIS gridding fitting analysis, which comprises the following steps:

Wherein, the plurality of grade indexes are five grade indexes in step S102.

In the embodiment of the invention, the determined travel resource evaluation type comprises five grade indexes, for example, the five grade indexes can be divided into five grade indexes, namely, an upper grade index, a middle grade index, a lower grade index and a middle grade index, the grade levels are distinct, the evaluation indexes can be conveniently and quantitatively combined with the evaluation indexes, and the unified management is facilitated.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the quantitative evaluation and the qualitative evaluation can be organically combined, so that the evaluation of the tourism resources is more scientific.

In a preferred embodiment, the corresponding travel resource data object includes some or all of the following data objects: tourist resource monomer space data, administrative district space data, traffic space data, water system space data, data related to the statistical yearbook, tourist number statistical data and natural disaster statistical data.

In the specific implementation process, different tourism resource data objects are selected according to different selected tourism resource evaluation types, for example, when the tourism resource evaluation type is a tourism traffic area grid evaluation type, three kinds of data, namely administrative area space data, traffic space data and water system space data, can be selected and are arranged in a GIS according to a preset mode; when the tourist resource evaluation type is tourist data space grid evaluation, administrative region space data and tourist quantity statistical data can be selected and set in the GIS according to a preset mode.

The statistical yearbook data comprise data in aspects of administrative divisions, natural resources, national economic accounting, population and the like, and can be flexibly selected according to different tourism resource evaluation types in the specific implementation process, so that more comprehensive evaluation data are obtained, and the gridded evaluation of the tourism resources is more comprehensive and scientific.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the grid evaluation method of the tourism resources is more comprehensive and scientific, and the application range is wider.

In a preferred embodiment, the at least two level scales are three level scales of 0.5km, 1km and 2 km.

In the specific implementation process, the spatial grid formation is carried out on the preset area in the GIS by adopting scales of three levels of 0.5km, 1km and 2km respectively. Because the spaces occupied by different tourism resource data objects in the preset area are different in size, the gridded data obtained by performing gridded modeling on the tourism resource data objects under different scales has larger difference in GIS distribution, and the result difference generated by performing fitting analysis on the tourism resource data objects is larger, so that the accuracy and the scientificity of tourism resource evaluation are influenced.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the tourism resource evaluation is more accurate and scientific.

Example four

On the basis of the foregoing method embodiment, an embodiment of the present invention further provides a system for evaluating tourist resources based on GIS gridding fitting analysis, and fig. 12 is a block diagram of a structure of the system for evaluating tourist resources based on GIS gridding fitting analysis according to an embodiment of the present invention, as shown in fig. 12, including:

the evaluation type selection module 1 is used for selecting at least one of the following travel resource evaluation types: developing potential grid evaluation, tourism traffic zone grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation;

the grade index management module 2 is connected with the evaluation type selection module 1 and is used for dividing the selected evaluation type of the tourism resources into a plurality of grade indexes;

the tourism resource data object management module 3 is connected with the evaluation type selection module 1 and is used for managing tourism resource data objects corresponding to the tourism resource evaluation types in a preset area;

the gridding scale level selection module 4 is connected with the tourism resource data object management module 3 and is used for selecting a certain level scale to carry out spatial gridding to obtain gridding data;

the fitting method selection module 5 is respectively connected with the evaluation type selection module 1 and the grade index management module 2 and is used for selecting at least two fitting methods to perform fitting analysis on the gridding data;

the fitting result generating module 6 is connected with the fitting method selecting module 5 and is used for generating a fitting analysis result and a grade index distribution condition;

and the analysis conclusion generation module 7 is connected with the fitting result generation module 6 and is used for obtaining an evaluation conclusion according to the fitting analysis result and the grade index distribution condition.

In the specific implementation process, one or more tourism resource evaluation types are selected and adopted through the evaluation type selection module 1, five types of tourism resource evaluation types including development potential grid evaluation, tourism traffic area grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation and tourism resource restriction factor grid evaluation are selected, and then the selected tourism resource evaluation types are divided into a plurality of grade indexes through the grade index management module 2.

Furthermore, the tourism resource data object management module 3 manages the tourism resource data object corresponding to the tourism resource evaluation type in the preset area, and the management modes are various, such as new creation, import, deletion and the like, so that the management and the operation are convenient.

Furthermore, a certain level scale is selected to be adopted for spatial gridding through the gridding scale level selection module 4, so that gridding data is obtained, in the specific implementation process, a plurality of preset optional level scales are adopted, for example, optional scales of four levels of 0.5km, 1km, 2km and 3km are adopted, so that a proper gridding scale is selected according to the difference of the gridding data in the GIS distribution under different scales, and the tourism resource evaluation is more accurate and scientific.

Further, by the fitting method selection module 5, at least two fitting methods are selected to perform fitting analysis on the grid data, and the implementation of the fitting method specifically includes: global polynomial GPI fitting method, local polynomial LPI fitting method, inverse distance weight IDW fitting method, radial basis function RBF fitting method, Kriging fitting method.

Further, a fitting analysis result and a distribution situation of the grade index are generated by the fitting result generation module 6, where the fitting analysis result includes data related to the grade index, such as a mean prediction error value, a root mean square prediction error value, and the like.

Further, an evaluation conclusion is obtained through the analysis conclusion generation module 7 according to the fitting analysis result and the grade index distribution condition.

Other embodiments of the present invention may refer to the detailed description of the above method embodiments, and are not repeated in the embodiments of the present invention.

In a preferred embodiment, the evaluation system further comprises a travel resource data object receiving module 8 connected to the travel resource data object management module 3 for receiving the travel resource data object.

In the specific implementation process, the tourism resource data object receiving module 8 is connected with an external system through a network system interface or a software system interface and is used for acquiring and receiving the tourism resource data object, so that a user can directly acquire the tourism resource data in the evaluation system conveniently, and the use convenience is improved.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the use is more convenient, and the user experience is improved.

In a preferred embodiment, the system further includes a default selection module 9, which is respectively connected to the evaluation type selection module 1, the gridding scale level selection module 4, and the fitting method selection module 5, and configured to set default options according to a preset manner.

Referring to fig. 13, a block diagram of another tourism resource evaluation system based on GIS gridding fitting analysis according to an embodiment of the present invention is shown in fig. 13:

in a specific implementation process, the default selection module 9 is used for locking and unlocking the evaluation type of the tourism resource, the gridding scale level and the fitting method which need to be selected so as to perform default setting on the evaluation condition of the tourism resource, so that the related condition can be set more quickly in the evaluation process of the tourism resource of the same type, and convenience and user experience are improved.

Therefore, the technical scheme in the embodiment of the invention at least has the following technical effects or advantages: the data access is more convenient and efficient, and the user experience is improved.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. a tourism resource evaluation method based on GIS grid fitting analysis, is characterized in that, comprises:

Determine to use at least one of the following tourism resource evaluation types: grid-based assessment of development potential, grid-based assessment of tourism traffic locations, spatial grid-based assessment of tourist data, grid-based assessment of tourism economic and industrial development level, grid-based assessment of tourism resource constraints evaluation;

Determine several grade indicators included in the tourism resource evaluation type;

Obtaining the tourism resource data object corresponding to the tourism resource evaluation type in the predetermined area, and setting the tourism resource data object in the GIS in a predetermined manner;

The predetermined area in the GIS is spatially gridded using at least two levels of scales, and gridded modeling is performed on the tourism resource data object to obtain gridded data;

At least two fitting methods are used to perform fitting analysis on the gridded data: global polynomial GPI fitting method, local polynomial LPI fitting method, inverse distance weight IDW fitting method, radial basis function RBF fitting method, Kerry Golden Kriging fitting method;

dividing the predetermined area into several grade areas corresponding to the number of grade indicators according to the fitting analysis result;

A legend is generated by using several different colors corresponding to the several grade indicators;

Mark the several grade areas with corresponding colors to obtain the distribution of grade indicators;

An evaluation conclusion is obtained according to the fitting analysis result and the distribution of the grade index.

2. The tourism resource evaluation method based on GIS grid fitting analysis as claimed in claim 1, further comprising, the tourism resource evaluation type adopts tourism resource grid comprehensive evaluation, and the tourism resource grid The comprehensive network evaluation includes: grid evaluation of development potential, tourism traffic location grid evaluation, tourist data space grid evaluation, tourism economic industry development level grid evaluation, and tourism resource constraint factor grid evaluation.

3. The tourism resource evaluation method based on GIS grid fitting analysis as claimed in claim 2, wherein the several grade indicators comprise: development potential grid evaluation optimization index, tourism traffic location grid Evaluation optimization index, tourist data spatial grid evaluation optimization index, tourism economic industry development level grid evaluation optimization index, tourism resource constraints factor grid evaluation optimization index.

4 . The tourism resource evaluation method based on GIS grid fitting analysis according to claim 1 , wherein the several grade indexes are five grade indexes. 5 .

5. The tourism resource evaluation method based on GIS grid fitting analysis as claimed in claim 1, wherein the corresponding tourism resource data objects include part or all of the following data objects: tourism resource monomer spatial data, Administrative area spatial data, traffic spatial data, water system spatial data, statistical yearbook-related data, tourist number statistics, and natural disaster statistics.

6 . The tourism resource evaluation method based on GIS grid fitting analysis according to claim 1 , wherein the at least two scales are three scales of 0.5km, 1km and 2km. 7 .

7. The tourism resource evaluation method based on GIS grid fitting analysis according to any one of claims 1 to 6, wherein the evaluation is obtained according to the fitting analysis result and the distribution of the several grade indicators In conclusion, the evaluation conclusion includes: the optimal fitting method, the grid scale of the optimal level, the spatial distribution of the level indicators and the reasons.

8. A tourism resource evaluation system based on GIS grid fitting analysis, characterized in that, it is mainly used in any tourism resource evaluation based on GIS grid fitting analysis as claimed in claims 1-7. methods, including:

The evaluation type selection module is used to select at least one of the following tourism resource evaluation types: grid-based assessment of development potential, grid-based assessment of tourism traffic locations, spatial grid-based assessment of tourist data, and grid-based assessment of tourism economic industry development level , Grid evaluation of tourism resource constraints;

a grade index management module, connected with the evaluation type selection module, for dividing the selected tourism resource evaluation type into several grade indicators;

A tourism resource data object management module, connected with the evaluation type selection module, for managing the tourism resource data objects corresponding to the tourism resource evaluation type in the predetermined area;

The grid scale level selection module is connected with the tourism resource data object management module, and is used for selecting a certain level of scale to perform spatial gridization to obtain gridded data;

a fitting method selection module, which is respectively connected with the evaluation type selection module and the grade index management module, and is used for selecting at least two fitting methods to perform fitting analysis on the gridded data;

a fitting result generation module, connected with the fitting method selection module, for generating fitting analysis results and rank index distribution;

An analysis conclusion generating module is connected to the fitting result generating module, and is used for drawing an evaluation conclusion according to the fitting analysis result and the distribution of the grade indexes.

9. The tourism resource evaluation system based on GIS grid fitting analysis as claimed in claim 8, further comprising, a tourism resource data object receiving module, connected with the tourism resource data object management module, for Receives a travel resource data object.

10. The tourism resource evaluation system based on GIS gridded fitting analysis according to claim 8 or 9, further comprising: a default selection module, respectively associated with the evaluation type selection module, the gridded The scale level selection module is connected with the fitting method selection module, and is used for setting default options according to a preset manner.