The result of global spatial autocorrelation analysis of new detected leprosy cases in Yunnan, China 2011–2020.

ObjectivesXinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.MethodsNumbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.ResultsIncidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

The results of the analysis on HFRS cases by general spatial autocorrelation.

The temple gardens are an important human landscape and have an important position in the Chinese garden system. Using GIS analysis tools, primarily the Nearest Neighbor Index, Kernel Density Estimation, and Spatial Autocorrelation, and employing a Geographic Detector model, we analyzed the spatial distribution characteristics and influencing factors of 4,317 temples and gardens in Jiangxi Province. Research shows that: 1) The spatial distribution type of temple gardens in Jiangxi Province is agglomeration type, with large spatial differences in distribution, forming a spatial distribution pattern of “generally dispersed and concentrated in some areas”; 2) the distribution of temple gardens in Jiangxi Province is uneven. They are mostly distributed in five prefecture-level cities: Ganzhou, Jiujiang, Shangrao, Fuzhou, and Nanchang; 3) The overall spatial distribution of temple gardens in Jiangxi Province has positive autocorrelation characteristics, and prefecture-level cities have significant proximity characteristics, forming a “high-high” “agglomeration” and “low-low agglomeration” distribution patterns; 4) Temple gardens in various regions are affected by geomorphological factors, and are mostly concentrated in the lower altitude range of 0–500 m and the gentle slope of 0°–30°. Most of the distribution density of temple gardens in various prefecture-level cities is within the buffer zone distance of the road network within the range of 0–1.5 km. 5) Economic, cultural, demographic, and historical factors have affected the development of temple gardens. Areas with more active economies have a denser number of temple gardens. The unique regional culture affects the distribution of temples and gardens in different regions. In places where the modern population is densely distributed, there are fewer temples and gardens, while in places where the population is less densely distributed, there are more temples and gardens. 6) The use of geographical detectors to detect influencing factors shows that the greatest impact on the spatial distribution of temple gardens in Jiangxi Province is the road network, followed by elevation, slope, GDP, and water systems. The research is conducive to scientific understanding of the distribution of temple gardens among prefecture-level cities in Jiangxi Province, and provides reference for strengthening the protection of temple gardens and exploring the tourism characteristics of temple gardens.

The result of spatial-temporal analysis of new detected leprosy cases in Yunnan, China, 2011–2020.

The attraction of tourism resources is very important to promote the sustainable development of tourism industry. This study takes China’s world cultural and natural heritage as the research object, and constructs an attractiveness evaluation system for China’s world cultural and natural heritage tourism resources by collecting user feedback data from three major travel OTA platforms. At the same time, ArcGIS 10.7 software was used for spatial autocorrelation analysis and kernel density analysis to explore the spatial distribution pattern of tourism resource attraction. The results show that China’s world cultural and natural heritage can be subdivided into 5 main categories and 10 sub-categories. From the perspective of spatial aggregation, only the Moran’s I index of tourist resource points showing a significant spatial aggregation feature. This study is helpful to reveal the weaknesses of tourism resource points and provide reference for sustainable development of attraction and optimization of tourism planning and management.

ObjectivesThis study used (Geographic Information System) GIS technology to analyze the spatiotemporal distribution of influenza incidence in Qinghai from 2009 to 2023, based on influenza surveillance data.MethodsThis study first accessed the influenza data sets of Qinghai Province from 2009 to 2023 through the Chinese Infectious Disease Surveillance System. Subsequently, trend charts of influenza incidence in each city and prefecture were employed to illustrate the trends of influenza incidence during the period from 2009 to 2023. To explore the risks of influenza incidence in different counties and districts, methods including spatial autocorrelation, cluster analysis, hotspot analysis, Gravity center shift model, and standard deviation ellipse were utilized.ResultsThe study showed that the incidence of influenza showed significant fluctuations, with marked spikes in 2019 and 2023. Spatial autocorrelation analysis revealed significant positive autocorrelation in 2015, 2017–2019, and 2022–2023 (Moran’s I > 0 and p < 0.05). Local spatial autocorrelation analysis identified clustering patterns in different regions, with high - high clustering in eastern Qinghai and low - low clustering in the west. Hot - spot analysis indicated that the counties with a high incidence of influenza in Qinghai were mainly located in the lower - altitude east. Standard deviation ellipse analysis showed that in 2021, the spread of influenza was the most extensive, almost covering eastern and parts of western Qinghai. From 2021 to 2022, the spread range shrank and expanded again in 2023. The gravity center of influenza moved southeastward year by year from Gangcha County in 2018 to Gonghe County in 2023. The spread of influenza was found to be expanding eastward, with the epidemic center shifted over time.ConclusionThe prominent spatiotemporal heterogeneity of influenza incidence in Qinghai Province indicates the need to develop differentiated and precise influenza prevention and control strategies in different regions to address the changing trends of influenza epidemics.

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

The spatial configuration and social performance of public sports facilities serve as crucial indicators for evaluating the equity of public sports services and the coherence of urban spatial structure. As Shanghai accelerates its development into a globally renowned sports city, the construction of public sports facilities has encountered significant opportunities. However, challenges persist in the spatial distribution, accessibility, and quality of these facilities. This study investigates the spatial agglomeration characteristics, accessibility, and social performance of urban public sports facilities in Shanghai at both the street and grid scales. Using geographic information system (GIS) tools and analytical methods such as kernel density estimation, standard deviation ellipse, spatial autocorrelation, Gaussian two-step moving search, and the Gini coefficient, the analysis yields the following findings: 1) Public sports facilities in Shanghai are concentrated in the central urban areas and exhibit scattered spatial distribution patterns in peripheral regions. These facilities display a significant directional coupling with population distribution (northeast-southwest), reflecting pronounced spatial imbalances. 2) Social performance analysis reveals clear regional inequities in Shanghai’s public sports facilities. While overall accessibility is relatively high, disparities remain, with suburbs facing facility shortages. Regional equity measurements indicate that the Gini coefficient for public sports facilities in Shanghai is 0.58. Central urban areas possess a high density of facilities, while suburban areas suffer from inadequate facility coverage, leading to uneven service radii and a pattern of high agglomeration but low coverage. 3) The social equity analysis shows that the service capacity entropy of public sports facilities exhibits a distinct spatial distribution, characterized by high values in the east and west and low values in the center. The highest entropy value is 4.25, while the lowest is 0.02. This study provides valuable insights for the planning and optimization of urban public sports facilities in Shanghai, contributing to the enhancement of spatial equity and service effectiveness.

This paper examines the spatial distribution pattern and influencing factors of Martial Arts Schools (MASs) based on Baidu map data and Geographic Information System (GIS) in China. Using python to obtain the latitude and longitude data of the MASs through Baidu Map API, and with the help of ArcGIS (10.7) to coordinate information presented on the map of China. By harnessing the geographic latitude and longitude data for 492 MASs across 31 Provinces in China mainland as of May 2024, this study employs a suite of analytical tools including nearest neighbor analysis, kernel density estimation, the disequilibrium index, spatial autocorrelation, and geographically weighted regression analysis within the ArcGIS environment, to graphically delineate the spatial distribution nuances of MASs. The investigation draws upon variables such as martial arts boxings, Wushu hometowns, intangible cultural heritage boxings of Wushu, population education level, Per capita disposable income, and population density to elucidate the spatial distribution idiosyncrasies of MASs. (1) The spatial analytical endeavor unveiled a Moran’s I value of 0.172, accompanied by a Z-score of 1.75 and a P-value of 0.079, signifying an uneven and clustered distribution pattern predominantly concentrated in provinces such as Shandong, Henan, Hebei, Hunan, and Sichuan. (2) The delineation of MASs exhibited a prominent high-density core centered around Shandong, flanked by secondary high-density clusters with Hunan and Sichuan at their heart. (3) Amongst the array of variables dissected to explain the spatial distribution traits, the explicative potency of ‘martial arts boxings’, ‘Wushu hometowns’, ‘intangible cultural heritage boxings of Wushu’, ‘population education level’, ‘Per capita disposable income’, and ‘population density’ exhibited a descending trajectory, whilst ‘educational level of the populace’ inversely correlated with the geographical dispersion of MASs. (4) The entrenched regional cultural ethos significantly impacts the spatial layout of martial arts institutions, endowing them with distinct regional characteristics.

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis.

We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state.

This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Spatial autocorrelation global Moran’s I analysis for the prevalence of echinococcosis.

More DetailsSpatial patterns in mussel richness and endemism (i.e., weighted and corrected weighted endemism) at HUC4, HUC6, and HUC8 spatial scales were characterized. Stream SR was estimated by summing the total number of mussel species predicted within each stream. Stream-level weighted endemism was calculated by weighting each species’ presence in the stream by the inverse of the total number of streams it was predicted to occupy, then summing across all species predicted present within the stream. This biodiversity metric therefore provides context about the range extent of the species predicted within the stream (e.g., higher values indicate either high richness or the presence of several range-restricted species). Stream-level corrected weighted endemism was calculated by dividing weighted endemism by the total number of species predicted present within the stream. This metric therefore corrects for an abundance of species predicted present within a stream (meaning, larger corrected weighted endemism values indicate the presence of more range-restricted species). Endemism metrics were rescaled to fall between zero and one to increase metric interpretability. These rescaled values were used in ensuing analyses.Stream-level SR and endemism metrics within each HUC region were strongly right-skewed (many smaller values and few large values). This necessitated calculating the median value of these richness and endemism metrics across all streams within each HUC region. Next, local neighborhoods for each HUC4, HUC6 and HUC8 region were generated to account for the degree of spatial connectivity among HUC regions (i.e. the number of neighboring regions for each HUC). These neighborhoods, as well as HUC median richness and endemism values, were then used to estimate 1) a global Moran's I across the entire study region and 2) a local Getis–Ord Gi statistic for each HUC region. The global Moran's I analysis evaluates directionality in, and magnitude of, spatial autocorrelation (i.e., whether high or low values cluster together, are randomly distributed or are overdispersed) throughout an entire region. Alternatively, the local Getis–Ord Gi statistic identifies clusters of zones within the entire region that contain statistically greater or lesser values of interest. HUC regions associated with statistically significant (α = 0.05, P < 0.025 and P > 0.975) values from the Getis-Ord analysis were classified as hot or cold spots of mussel richness or endemism. Fields of NoteHUC - HUC region IDStates - State(s) containing each HUC regionName - HUC region nameSS_SR - Estimated species richnessssSR_HtSp - HUC region hotspot status based on species richnessAdditional ResourcesThis data, as well as data layers depicting hot/cold spots of species richness and other biodiversity metrics at HUC4 and HUC6 spatial scales, are available for download here: https://www.sciencebase.gov/catalog/item/65d762ced34ec3e1801d7e30.

Results of the global spatial autocorrelation analysis of pulmonary TB incidence from 2005–2013.

Zoom in on the map above and click your area of interest or use the Tile Index linked below to determine which package(s) you require for download. The Digital Elevation Models (DEM) are 2-m resolution raster elevation products that were generated from the Ontario Classified Point Cloud (Imagery-Derived) data. The point clouds were created via a pixel-autocorrelation process from the stereo aerial photography of the Land Information Ontario (LIO) imagery program. It is important to note that the DEM does not represent a full ‘bare-earth’ elevation surface. There are areas where there are very few points classified as Ground and interpolation has occurred across the resulting voids. Points classified as Ground have not been assessed for accuracy to determine if they represent true ground features. Some features are still raised above ground surface, such as larger buildings, larger forest stands and other raised features.

For more detailed information about this dataset, refer to the associated User Guide.

Now also available through a web service which exposes the data for visualization, geoprocessing and limited download.

The service is best accessed through the ArcGIS REST API, either directly or by setting up an ArcGIS server connection using the REST endpoint URL. The service draws using the Web Mercator projection.

For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca.

Service Endpoints https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DEM_ImageryDerived/ImageServer https://intra.ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/Ontario_DEM_ImageryDerived/ImageServer (Government of Ontario Internal Users)

Additional Documentation

Ontario DEM (Imagery-Derived) - User Guide (DOCX)Ontario DEM (Imagery-Derived) - Tile Index (SHP)SCOOP 2013 - Vertical Accuracy Assessment (Word) SCOOP 2013 - Vertical Accuracy Assessment - Data (SHP)

Product Packages

SCOOP 2013 DEM Package A (IMG) SCOOP 2013 DEM Package B (IMG) SCOOP 2013 DEM Package C (IMG) SCOOP 2013 DEM Package D (IMG) SCOOP 2013 DEM Package E (IMG) SCOOP 2013 DEM Package F (IMG) SCOOP 2013 DEM Package G (IMG) SCOOP 2013 DEM Package H (IMG)

DRAPE 2014 DEM Package A (IMG) DRAPE 2014 DEM Package B (IMG) DRAPE 2014 DEM Package C (IMG) DRAPE 2014 DEM Package D (IMG) DRAPE 2014 DEM Package E (IMG) DRAPE 2014 DEM Package F (IMG) DRAPE 2014 DEM Package G (IMG) DRAPE 2014 DEM Package H (IMG) DRAPE 2014 DEM Package I (IMG)

Algonquin 2015 DEM Package (IMG)

SWOOP 2015 DEM Package A (IMG) SWOOP 2015 DEM Package B (IMG) SWOOP 2015 DEM Package C (IMG) SWOOP 2015 DEM Package D (IMG) SWOOP 2015 DEM Package E (IMG) SWOOP 2015 DEM Package F (IMG) SWOOP 2015 DEM Package G (IMG) SWOOP 2015 DEM Package H (IMG)

COOP 2016 DEM Package A (IMG) COOP 2016 DEM Package B (IMG) COOP 2016 DEM Package C (IMG) COOP 2016 DEM Package D (IMG) COOP 2016 DEM Package E (IMG) COOP 2016 DEM Package F (IMG) COOP 2016 DEM Package G (IMG) COOP 2016 DEM Package H (IMG) COOP 2016 DEM Package I (IMG)

NWOOP 2017 DEM Package A (IMG) NWOOP 2017 DEM Package B (IMG) NWOOP 2017 DEM Package C (IMG) NWOOP 2017 DEM Package D (IMG) NWOOP 2017 DEM Package E (IMG) NWOOP 2017 DEM Package F (IMG)

Status On going: Data is continually being updated

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

Contingency tables from spatial overlay analyses.

The attraction of tourism resources is very important to promote the sustainable development of tourism industry. This study takes China’s world cultural and natural heritage as the research object, and constructs an attractiveness evaluation system for China’s world cultural and natural heritage tourism resources by collecting user feedback data from three major travel OTA platforms. At the same time, ArcGIS 10.7 software was used for spatial autocorrelation analysis and kernel density analysis to explore the spatial distribution pattern of tourism resource attraction. The results show that China’s world cultural and natural heritage can be subdivided into 5 main categories and 10 sub-categories. From the perspective of spatial aggregation, only the Moran’s I index of tourist resource points showing a significant spatial aggregation feature. This study is helpful to reveal the weaknesses of tourism resource points and provide reference for sustainable development of attraction and optimization of tourism planning and management.

The data used in this paper are: the range and boundary data of the Qinghai Tibet Plateau [12]; 90 m in the international scientific data mirror website of computer network information center of Chinese Academy of Sciences (http: / / www.gscloud. CN) × DEM data products with 90 m spatial resolution; The site data is mainly based on the results of the second national cultural relic survey, combined with the cultural relic Atlas of relevant provinces. In the process of data processing, firstly, the specific location of the site is determined, and the site with unknown longitude and latitude is interpreted with google satellite map; Secondly, according to the identification standard of China's cultural relics census, the sites are classified and dated (excluding the points with unknown age), and a small number of cross age sites are calculated repeatedly. Finally, according to the characteristics of archaeology, history and chronology system, the sites in the study area are counted according to the comprehensive division method of cultural type and history. The application of GIS and RS in the research of settlement and regional archaeology is becoming more and more mature. The shortest path in GIS is used to simulate the prehistoric traffic route of the Qinghai Tibet Plateau, and the kernel density estimation method is used to calculate the data aggregation of the whole region according to the input feature data set, so as to produce a continuous density surface. The results show that the distribution probability of the research object can be directly expressed, and the size of the kernel density represents the agglomeration degree of the site in the spatial distribution. The larger the kernel density estimation is, the denser the distribution density of the site is. The distance between the centroid of each element and its nearest element is measured by the average nearest neighbor index, and the average value of all the nearest distances is calculated, and compared with the average distance in the hypothetical random distribution, so as to judge whether the studied elements are clustered distribution. The description of the spatial distribution characteristics of attributes in the whole region is used to judge whether a certain element or phenomenon in the study area has aggregation characteristics in space. In this paper, the global Moran's I index is used to measure the global spatial autocorrelation degree of the sites in the Qinghai Tibet Plateau.

A discussion of recoding the cumulative probabilities to the ordinal scale.