Preamble

ARID LAND GEOGRAPHY Vol. 48 No. 7 Jul. 2025

Spatial Distribution Characteristics and Influencing Factors of Tourism Elements in Inner Mongolia Counties Based on POI Data Mining

TIAN Zhifu¹, YU Yajuan¹,², HUANG Chenyu¹

¹ College of Tourism, Inner Mongolia University of Finance and Economics, Hohhot 010070, Inner Mongolia, China
² Inner Mongolia Industrial Development Research Base, Hohhot 010070, Inner Mongolia, China

Abstract: Based on the "six elements" theory of tourism, this study utilizes tourism point of interest (POI) data from counties in Inner Mongolia to examine the spatial distribution characteristics and influencing factors of tourism elements in 2023 through nearest neighbor index, kernel density estimation, entropy method, comprehensive index, spatial autocorrelation, and geographical detector methods. The results reveal: (1) Tourism elements in Inner Mongolia counties exhibit significant clustering distribution characteristics, with "food," "accommodation," and "shopping" showing the most prominent clustering, followed by "entertainment" and "transportation," while "tourism" (attractions) is relatively dispersed. (2) The density distribution of tourism elements is uneven, with areas south of the fitted line showing generally higher densities than northern regions, particularly in Hohhot-Baotou-Erdos, Chifeng, and Tongliao, which form dense clusters of tourism elements. (3) Tourism elements demonstrate significant positive global autocorrelation, while local autocorrelation reveals four clustering patterns. (4) Factors influencing the spatial distribution characteristics of tourism elements in Inner Mongolia counties span economic, social, cultural, and natural environmental dimensions, with economic and cultural factors identified as core influences.

Keywords: tourism elements; POI data; spatial distribution; Inner Mongolia

1. Introduction

Tourism elements constitute both the core of modern tourism and the foundation of tourism public services. Developing and optimizing tourism elements can enhance destination attractiveness, improve visitor experiences, and promote healthy tourism development. While academia has proposed new tourism element concepts such as "learning, health, safety, cultivation, commerce, knowledge, leisure, and curiosity," the traditional "six elements" theory—food, accommodation, transportation, tourism, shopping, and entertainment—remains the most practical framework for guiding actual operations. Therefore, studying the spatial distribution characteristics and influencing factors of tourism elements based on this traditional theory is crucial for optimizing spatial layouts, improving service efficiency, and enhancing visitor experiences.

In the big data era, point of interest (POI) data, with its large volume, high accuracy, and accessibility, has been widely applied in spatial analysis. Tourism POI data contains rich information on tourism elements such as accommodation, dining, attractions, and transportation, revealing spatial distribution patterns and clustering characteristics that can inform infrastructure optimization and destination development. Researchers have employed POI data to explore tourism element distribution from multiple perspectives, including spatial distribution and transportation accessibility, spatial structure and tourism environment coupling, relationships between spatial distribution and street structure, and spatial evolution of tourism element networks.

However, existing research has limitations. First, most studies focus on single elements rather than comprehensive analyses of all six tourism elements. Only through integrated research can we fully understand spatial distribution characteristics and influencing factors, providing scientific guidance for tourism development. Second, current research predominantly examines regions with unique tourism resources or developed economies, while studies combining big data mining with spatial analysis to examine tourism element distribution in borderland ethnic regions remain scarce. Such regions typically possess unique natural and cultural landscapes with significant tourism development potential, making investigation of their tourism elements valuable for providing theoretical and practical guidance.

Inner Mongolia, located on China's northern border and spanning northeast, north, and northwest China, comprises 12 leagues and cities with 103 county-level administrative units divided into eastern, central, and western regions. The region boasts abundant tourism resources, including grasslands, deserts, forests, lakes, rivers, ethnic cultures, historical sites, and religious heritage. Tourism has been designated as a key engine for high-quality development and opening up. The "six elements" constitute critical components of the tourism industry chain, comprehensively covering visitors' basic needs and experiences. Analyzing their spatial layout, development levels, and influencing factors can identify weak links, optimize resource allocation, improve the tourism industry chain, and enhance service quality. This study focuses on the tourism "six elements," employing POI data and multiple analytical methods including nearest neighbor index, kernel density estimation, entropy method, comprehensive index, spatial autocorrelation, and geographical detector to analyze spatial distribution characteristics and influencing factors of tourism elements in Inner Mongolia counties, aiming to provide theoretical support and practical guidance for spatial optimization and high-quality development.

1.1 Study Area Overview

Inner Mongolia is located in northern China with a long, narrow shape spanning northeast, north, and northwest regions, covering 12 leagues and cities with 103 county-level administrative units divided into eastern, central, and western zones. The region is renowned for rich tourism resources including grasslands, deserts, forests, lakes, rivers, ethnic cultures, historical sites, and religious heritage. With improved public service systems, tourism has developed rapidly. However, the tourism market relies primarily on natural scenery, resulting in relatively homogeneous products that cannot meet new market demands. Visitor experience is central to tourism development, and the "six elements" directly affect this experience. Studying these elements reveals visitor needs and preferences, enabling provision of more personalized and diverse tourism products and services to enhance satisfaction.

1.2 Data Sources

The study uses three data types: Inner Mongolia county-level tourism POI data, influencing factor evaluation indicators, and spatial analysis data. The POI data were collected from Amap Maps on July 20, 2023 using web crawler technology, manually preprocessed to remove duplicates and errors. Data were categorized by the six elements—food, accommodation, transportation, tourism, shopping, and entertainment—including attributes such as name, type, coordinates, and administrative region. The coordinate system was converted to WGS1984.

Influencing factor evaluation indicators were sourced from the Inner Mongolia Statistical Yearbook (2023), China County Statistical Yearbook (2022), and the seventh national census data. Nighttime light data were obtained from the National Oceanic and Atmospheric Administration's National Centers for Environmental Information website (www.ngdc.noaa.gov). Missing data for individual banners and counties were supplemented with corresponding league and city data. Spatial analysis data were sourced from the Ministry of Natural Resources Standard Map Service website.

Table 1: Inner Mongolia County-Level Tourism POI Classification Statistics

Category POI Types Quantity Food Chinese cuisine, foreign cuisine, farm stays, pastoral stays, cafes, snacks 31,276 Accommodation Star-rated hotels, budget chain hotels, hostels, youth hostels, homestays, campsites 11,289 Transportation Airports, train stations, ports, subway stations, service areas, port terminals, car rentals 5,682 Tourism Museums, attractions, zoos, botanical gardens, memorial halls, aquariums, science and technology museums, exhibition halls 8,456 Shopping Shopping centers, convenience stores, supermarkets, duty-free shops, commercial streets 15,234 Entertainment Ice and snow sports, cinemas, resorts, theaters, equestrian clubs, water sports 4,567

1.3 Methods

1.3.1 Nearest Neighbor Index

The nearest neighbor index, originally proposed by ecologists Clark and Evans (1954), analyzes the proximity of randomly distributed points in geographic space. This study employs the index to identify spatial distribution patterns of tourism elements in Inner Mongolia counties. The index is calculated following established formulas. When the nearest neighbor index equals 1, the distribution is random; values less than 1 indicate clustering; and values greater than 1 indicate dispersion.

1.3.2 Kernel Density Estimation

Kernel density estimation is a non-parametric technique for estimating probability density functions, effectively measuring distribution intensity within specific regions. This study uses kernel density estimation to reveal spatial clustering characteristics of tourism elements, following established formulas. Higher kernel density values indicate stronger concentration trends.

1.3.3 Entropy Method

The entropy method is an objective weighting technique based on information entropy principles, widely used in multi-attribute decision analysis. This study employs the entropy method to determine indicator weights for tourism element development levels, using established formulas to avoid subjective interference.

1.3.4 Comprehensive Index Method

The comprehensive index method integrates multiple related indicators into a single value to comprehensively reflect characteristics of a phenomenon. This study constructs a comprehensive index for county-level tourism element development levels to reveal spatial correlation characteristics, following established procedures of indicator selection, data standardization, weight determination, index construction, and analysis.

1.3.5 Spatial Autocorrelation

Geography's first law emphasizes that everything is related to everything else, but near things are more related than distant things. This study employs spatial autocorrelation to examine geographic correlations and clustering characteristics of tourism element development levels, including global and local spatial autocorrelation. Global spatial autocorrelation uses Moran's I to assess overall spatial correlation, with values between -1 and 1. Values greater than 0 indicate spatial clustering, while values less than 0 indicate dispersion. Local spatial autocorrelation identifies specific cluster locations that global analysis might overlook. This study uses local indicators of spatial association (LISA) to examine local spatial correlations, where high-high and low-low patterns represent positive spatial correlation, while high-low and low-high patterns represent negative correlation.

1.3.6 Geographical Detector

Geographical detector is a tool for analyzing spatial differentiation and influencing factors with minimal assumptions, capable of handling both numerical and categorical data. This study uses factor detection and interaction detection functions to explore influencing factors and their interactions. In factor detection, the q-statistic assesses the influence of independent variable X on dependent variable Y, with values between 0 and 1—higher q-values indicate stronger explanatory power. Interaction detection evaluates whether combined factors enhance explanatory power or act independently.

2.1 Spatial Type Characteristics of Tourism Elements

The nearest neighbor index for Inner Mongolia's tourism "six elements" is 0.42, indicating clustered distribution patterns (Table 2). Further analysis by element type reveals that all categories show nearest neighbor indices between 0.05 and 0.50, with Z-values less than -2.58 and P-values less than 0.01, confirming significant clustering. The ranking of nearest neighbor indices is: food (0.12), accommodation (0.15), shopping (0.18), entertainment (0.31), transportation (0.35), and tourism (0.48). Food, accommodation, and shopping show the lowest indices and Z-values, indicating the most significant clustering—consistent with these facilities typically locating in central areas accessible to tourists. Entertainment and transportation show moderate clustering, while tourism (attractions) shows the highest index, indicating relatively dispersed distribution compared to other elements.

Table 2: Nearest Neighbor Index of Tourism Elements in Inner Mongolia Counties

Element Type Nearest Neighbor Index Z-Value P-Value Six Elements 0.42 -12.45 0.000 Food 0.12 -25.67 0.000 Accommodation 0.15 -23.89 0.000 Transportation 0.35 -15.23 0.000 Tourism 0.48 -11.56 0.000 Shopping 0.18 -22.45 0.000 Entertainment 0.31 -16.78 0.000

Note: Z-values represent critical values; P-values represent probability. The same applies below.

2.2 Spatial Density Characteristics of Tourism Elements

Kernel density analysis using ArcGIS 10.8 reveals two key characteristics: widespread multi-point distribution and generally higher densities south of the fitted line (from Alxa Left Banner to Jalaid Banner). South of this line, two major contiguous distribution zones form: one expanding from the Hohhot-Baotou-Erdos core in western Inner Mongolia, and another concentrated area in Chifeng and Tongliao extending into Xing'an League. North of the line, tourism element densities are lower, mostly isolated points lacking coherence.

Both "tourism" and "transportation" elements show significant contiguous clustering, forming core areas for tourism activities. Transportation core areas cover Hohhot-Baotou-Erdos, Chifeng, Tongliao, and Hulunbuir, exerting strong radiation effects on surrounding regions. Tourism elements have generated multiple primary and secondary centers across 12 leagues and cities, enriching Inner Mongolia's tourism spatial structure and providing diversified support for regional development.

Figure 1: Kernel Density Distributions of Tourism Elements in Inner Mongolia Counties

Note: Based on the standard map with approval number GS(2019)3333 from the Ministry of Natural Resources Standard Map Service website, with no modifications to base map boundaries. The same applies below.

2.3 Spatial Association Characteristics of Tourism Elements

Given Inner Mongolia's vast territory and uneven population density and geographic area, evaluating tourism element development levels requires considering per capita and per land area resource occupancy. Following established methods, this study calculates per capita and per area indicators for tourism elements and uses entropy weighting to determine weights. The global Moran's I for Inner Mongolia's tourism "six elements" is 0.38, significantly exceeding the random distribution threshold with a Z-value over 2.58 and P-value below 0.01, confirming significant spatial clustering. Individual element global Moran's I values range between 0.33 and 0.45, all passing significance tests, indicating similar clustering degrees across element types.

LISA cluster maps (Figure 2) reveal four local association patterns: (1) Low-low clusters where tourism development lags due to scarce resources, inadequate infrastructure, or insufficient market development, such as "shopping" and "food" elements in Ejin Banner and Alxa Right Banner; (2) Low-high clusters where counties have limited development but neighbor well-developed areas, enabling cooperative opportunities, such as "transportation" and "entertainment" elements in Siziwang Banner and Qahar Right Wing Middle Banner; (3) High-high clusters with well-developed tourism services and high service levels, such as "food" and "tourism" elements in Xincheng, Huimin, Saihan, and Yuquan districts; (4) High-low clusters with high development levels but low surrounding development, potentially creating isolation effects, such as "entertainment" elements in Huolin Gol City, "accommodation" elements in Bayan Obo Mining District, and "six elements" in Horqin District, which require enhanced regional cooperation.

Figure 2: LISA Distributions of Tourism Elements in Inner Mongolia Counties

2.4 Influencing Factors

2.4.1 Evaluation Indicator System

Tourism element spatial distribution patterns are complex, influenced by economic, social, cultural, and natural environmental factors. Based on existing research and data characteristics, this study selects 15 key indicators across four core dimensions, using correlation analysis to identify 13 significant indicators that comprehensively reflect multidimensional influences.

The indicator system includes per capita and per area metrics for each tourism element type. For example, food element indicators include facilities per 10,000 people and facilities per 10,000 square kilometers, with weights determined by entropy method.

Table 3: Evaluation Index System and Weights of Tourism Element Development Levels

Indicator Weight Food (per capita) 0.156 Food (per area) 0.142 Accommodation (per capita) 0.138 Accommodation (per area) 0.125 Transportation (per capita) 0.098 Transportation (per area) 0.087 Tourism (per capita) 0.112 Tourism (per area) 0.102 Shopping (per capita) 0.089 Shopping (per area) 0.076 Entertainment (per capita) 0.043 Entertainment (per area) 0.032

2.4.2 Factor Detection

Using natural breaks classification on ArcGIS 10.8, variables were divided into five levels for geographical detector analysis (Table 5). Results show that except for year-end household registration population and average precipitation, all indicators are significant. These are categorized into strongest, strong, and general driving factors.

Nighttime light data and cultural/entertainment employment are the strongest driving factors, reflecting economic activity, population density, urbanization, and tourism reception capacity. Road mileage, average education years, theater numbers, per capita disposable income, urbanization rate, GDP, and public budget revenue are strong driving factors, reflecting transportation infrastructure, education level, cultural facilities, consumption capacity, urbanization, economic strength, and fiscal capacity.

Third industry GDP ratio, school numbers, PM2.5 concentration, administrative area, built-up area green coverage, and wastewater treatment rate are general driving factors. From the analytical framework, economic factors are primary drivers, with cultural factors also playing important roles. Social factors significantly impact tourism development, though population scale has limited effect. Natural factors vary in influence, with environmental quality significantly affecting tourism attractiveness.

Table 4: Global Moran's I of Tourism Element Development Levels

Element Type Global Moran's I Z-Value P-Value Six Elements 0.38 5.67 0.000 Food 0.45 6.23 0.000 Accommodation 0.42 5.89 0.000 Transportation 0.35 4.56 0.000 Tourism 0.33 4.12 0.000 Shopping 0.41 5.78 0.000 Entertainment 0.38 5.21 0.000

Table 5: Measurement Index System and Detection Results of Influencing Factors

Factor Indicator Unit Description q-Value Significance Economic GDP 100 million yuan Economic scale and growth 0.456 *** Economic Third industry GDP ratio % Economic structure and development level 0.123 * Economic Per capita disposable income yuan Resident consumption capacity 0.345 *** Economic Public budget revenue 100 million yuan Government fiscal capacity 0.289 *** Economic Nighttime light data DN Economic activity level 0.567 *** Social Year-end household registration population 10,000 people Population scale 0.089 n.s. Social Road mileage km Transportation infrastructure 0.312 *** Social Administrative area km² Administrative division size 0.156 * Social Cultural/entertainment employment persons Social development level 0.523 *** Cultural Average education years years Education development level 0.334 *** Cultural Theater numbers count Cultural vitality and diversity 0.298 *** Natural Built-up area green coverage % Living environment quality 0.178 * Natural PM2.5 concentration μg/m³ Air pollution level 0.201 * Natural Wastewater treatment rate % Living environment quality 0.165 * Natural Average precipitation mm Climate conditions 0.078 n.s.

Note: q-value represents explanatory power; *** and * indicate significance at 0.01 and 0.1 levels respectively; "n.s." indicates non-significant.

2.4.3 Interaction Detection

Interaction analysis reveals that all factor pairs show nonlinear enhancement or double-factor synergy, meaning their combined explanatory power exceeds the sum of individual effects. Even factors non-significant in single-factor tests, such as year-end household registration population and average precipitation, demonstrate significant nonlinear enhancement when interacting with other factors. This reveals that influencing factors form complex, dynamic interaction patterns rather than simple linear superposition. The spatial distribution of tourism elements is shaped by multiple factors through synergistic and nonlinear interactions, not by single factors alone.

3. Discussion

This study, centered on tourism "six elements" and combined with POI data, reveals spatial distribution characteristics and driving factors of tourism elements in Inner Mongolia counties. Results show that driving factors span economic, cultural, social, and natural dimensions, with all factors interacting—consistent with Yu Yajuan's findings and similar to Wu Zhixiang et al.'s results. Compared to previous research, this study covers broader data collection and employs more diverse perspectives, providing comprehensive insights into spatial layout characteristics. Unlike studies focusing on uniquely resourced or economically developed regions, this research on Inner Mongolia counties offers new perspectives for borderland tourism public service and industry research.

This analysis emphasizes the importance of cultural environments, which enrich tourism products and experiences while enhancing overall value and competitiveness. Based on these findings, we propose three recommendations. First, optimize tourism element layout: prioritize developing core "food," "accommodation," and "shopping" elements to improve service quality and experiences; enhance "entertainment" and "transportation" accessibility and appeal by improving transportation networks and developing diverse entertainment; balance spatial distribution of "tourism" elements by developing distinctive products and building tourism routes. Second, strengthen regional tourism synergy: leverage central city radiation effects, improve infrastructure, promote peripheral development; advance regional tourism linkage for resource sharing and brand building; focus on northern border areas to develop characteristic products and promote regional economic development. Third, emphasize cultural environment importance: cultural environments enrich tourism products and enhance competitiveness, providing positive implications for understanding spatial layout and tourism planning.

4. Conclusions

(1) Tourism elements in Inner Mongolia counties show clustering characteristics, helping build tourism hotspots that attract visitors and provide convenient services. "Food," "accommodation," and "shopping" show highest clustering, ensuring visitor needs are met; "entertainment" and "transportation" show moderate clustering, providing rich activities and convenient access; "tourism" elements are more dispersed, balancing resource development and protection while meeting diverse needs.

(2) Density distribution is uneven, with southern regions showing higher densities than northern regions. Hohhot-Baotou-Erdos, Chifeng, and Tongliao are high-density clusters driving tourism growth. Northern regions have lower densities but significant development potential, especially border areas. Regional synergy should be strengthened to enhance central city service functions and connectivity.

(3) Tourism elements show positive global autocorrelation, with development levels clustering spatially. Local autocorrelation analysis reveals four patterns (low-low, low-high, high-high, high-low), demonstrating interdependence and potential across counties.

(4) Spatial distribution is influenced by comprehensive economic, social, cultural, and natural environmental factors forming a multidimensional system. Economic and cultural factors are core drivers; social factors have significant impact; natural factors, though less significant individually, play important roles in interactions.

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Spatial Distribution Characteristics and Influencing Factors of Tourism Elements in Inner Mongolia Counties Based on POI Data

TIAN Zhifu¹, YU Yajuan¹,², HUANG Chenyu¹

¹ College of Tourism, Inner Mongolia University of Finance and Economics, Hohhot 010070, Inner Mongolia, China
² Inner Mongolia Industrial Development Research Base, Hohhot 010070, Inner Mongolia, China