R - GIS: how to use R language implementation of GIS geospatial analysis and model prediction

With the development of geographic information systems (GIS) and large-scale research, spatial data management, statistics and mapping have become increasingly important.R language plays an important role in data analysis, mining and visualization, among which it plays an important role in spatial analysis, and the number of spatial-related packages has reached more than 130.We will combine some classic examples to understand the operation of R language in spatial data processing, management and visualization, from the aspects of spatial data measurement, spatial data interpolation, spatial data modeling, machine learning, data visualization, spatial covariate extraction, etc.Perform GIS operations and mapping in orientation, get rid of the complex operations of ArcGIS, and realize the automation, rapid batch operations, modeling and visualization of spatial data.

[Expert]: Dr. Xiao, with rich experience in scientific research and teaching, with senior technical background and professional background, and has long been engaged in research on crop growth models, smart agriculture, agricultural informatization, and the combination of crop models with remote sensing and GIS.

>>>R-GIS: 如何用R 语言实现GIS 地理空间分析及模型Forecast

Topic 1 R Language and Data Science (Theory + Practice)strong>
1 R language and data science
1) R language development context
2) R language data manipulation skills
3) R language in GISApplication in
2 R language programming skills data manipulation
1) R and working directory
2) R data type and structure
3) R data typesThe assignment and operation of
4) The reading, writing and operation of various data formats

Thematic 2 R language spatial data explanation and processing technology (theory + practical operation)
1 R language spatial data types and characteristics
1) R spatial vector data
2) R raster data

2 R language spatial data mining and processingTechnology
sp: basic package for processing geographic data
sf: dataframe-style geographic data package
rgdal: encapsulates GDAL (an open source geographic data abstraction library that provides very rich geographic dataRead-write driver))
rgeos: Encapsulates GEOS, an open source geometry engine, providing functions such as geometric model, geometric relationship judgment, basic geometric calculation operations, etc.
proj4: PROJ4 An open source map projection library, providing rich map projection conversion algorithms
spatstat: spatial point pattern analysis
gstat, field: geostatistical modeling, spatial interpolation
plot, image, image.plot, tmap, ggmap, ggplot2: spatial thematic map
leaflet: modernMobile-first interactive mapping framework
leaflet.extras: leaflet plugin
leaflet.esri: ESRI extension package
spdep: spatial correlation analysis

Topic 3 Advanced Processing of R Language Spatial Data (Theory + Practical Operation)
1 R Language SpaceAdvanced application of vector data processing
1) Point, line, area data
2) Coordinate system transformation of space vector data
3) Clipping, intersection and merging of space vector data
4) Numerical calculation of space vector data

2 Advanced application of R language raster data processing
5) Raster data generation and data management
6) Raster data coordinate system transformation
7) Raster dataClipping, intersecting and splicing
8) Numerical calculation of raster data
3 Advanced application of mutual conversion between raster data and vector data in R language

Thematic Four Advanced Application of Spatial Mapping in R Language (Theory + Practical Operation)
1 Plot, a Plot Package in R LanguageApplication technology
2 R language space to make image image application technology
 

Thematic Five Advanced Application of R Language Space Mapping (Theory + Practical Operation)
1 Drawing in R Language Space ggplot2Application technology
2 R language space thematic map tmap application technology

Thematic Six R Language Multidimensional Spatial Data Processing (Theory + Practical Operation)
1 Reading of nc Spatial Data, processing and exporting
2 Reading, processing and exporting of tiff spatial data
3 Management of multi-dimensional spatial data
4 Interactive map leaflet

Topic Seven R Language Spatial Interpolation Method (Theory + Practical Operation)
1 R Language Inverse Distance Interpolation Method
2 R language Kriging space interpolation method
3 R language thin disk spline interpolation method

Topic eight R language machine learning and scaling techniques and bias correction
1 Use of machine learning methods
2 Spatial upscaling and downscaling techniques
2 Correction of interpolation bias using environmental variables and machine learning