GIS is the use of electronic computers and their external equipment to collect, store, analyze and describe the entire or part of the earth's surface and spatial information systems. Simply put, it combines geospatial information and some attribute information related to the geographic information in a certain region to achieve comprehensive management of geographic and attribute information. The research object of GIS is the whole geographic space, and geographic information is related to geographic location, so the development of GIS has been paid attention to worldwide. In recent years, GIS has also received much attention in our country, and has been used in urban and rural planning, disaster monitoring, resource inventory, land survey, environmental management, urban pipe network, combat command, macro decision-making, urban public services, transportation, navigation, e-government and other fields. been widely applied. So how to deeply understand the principles of GIS? How to efficiently process multi-source spatial data? How to establish practical GIS technology application solutions for specific fields? A set of methods and cases based on ArcGIS Pro spatial data processing will be provided.
Compared with ArcGIS, ArcGIS Pro, as ESRI's GIS product for the new era, inherits the powerful data management, mapping, and spatial analysis capabilities of traditional desktop software (ArcMap) on the original ArcGIS platform, and also has its unique Some special functions, such as 2D and 3D fusion, big data, vector slice production and release, task workflow, super-forced map, space-time cube, etc. At the same time, it integrates ArcMap, ArcSence, and ArcGlobe to realize 3D integration and synchronization.
You'll organize your GIS work into projects using ArcGIS Pro, which you can use to map 2D and 3D data. With ArcGIS Pro, you can create and edit a variety of features, while also integrating data from multiple sources such as text, vector, raster, lidar, multidimensional data into your project. This course will teach you to use ArcGIS Pro to analyze data, manage GIS data, and build tools for automating work or solving complex problems. You can use analysis and geoprocessing features in ArcGIS Pro to answer many spatial questions and perform spatial analysis. With spatial analysis tools such as vector data analysis, raster data analysis, 3D analysis, hydrological analysis, etc., you can solve complex location-oriented problems, explore and understand your data from a geographic perspective, identify relationships, detect and quantify patterns, evaluate trends, and make predictions and decisions. Python can be used to automate geoprocessing tools and provides the ability to create your own geoprocessing tools, either as script tools or as Python toolbox tools. This course will teach you to save time on repetitive tasks, minimize errors, and efficiently iterate on analyzes by creating models or scripts to transform into custom tools.
You will learn to use the above principles and technical methods in specific practical cases to improve the application ability and efficiency of GIS technology.
Getting Started
Chapter 1: GIS Theory and ArcGIS Pro Basics
1. Introduction to basic principles of GIS and commonly used software
2. ArcGIS Pro installation and configuration
3. Introduction of new features of ArcGIS Pro 3.0
4. The main components of the ArcGIS Pro user interface (ribbon, views, and panes) and their interactions.
5. ArcGIS Pro project creation: including maps, scenes, layouts and other items
6. Browsing and viewing of spatial information
7. Inquiry and output of spatial information 8. Document storage 
method
Base
Chapter 2: ArcGIS Data Management and Conversion
1. ArcGIS data management
2. Data type and conversion
3. Data structure and conversion
4. Data format and conversion
5. Conversion between ArcGIS and external data
6. Theories, methods and steps of geospatial data database construction
7. Map projection basics
8. Common map projection and projection transformation operations in my country
9. Transformation of Beijing54, Xian80, WGS84, CGCS2000 different geographic coordinate systems
Chapter 3: Data Editing and Query, Topology Check
1. Introduction to Common Data Sources
2. Spatial data collection method
3. Introduction to multiple georeferencing methods
4. Geometry acquisition of spatial data
5. Spatial data attribute collection
6. Data inspection and topology processing
7. Data processing: data cutting, data splicing, data extraction
Cartography
Chapter Four: Map Symbols and Layout Design
1. Introduction to GIS mapping
2. Symbol settings for spatial data display
3. Make professional map symbols
4. Labeling and Annotation
5. Thematic map layout design and finishing
6. Thematic map drawing skills and map output
7. Research area map making
Spatial Analysis
Chapter 5: ArcGIS vector space analysis and application
1. Introduction to ArcGIS Pro geoprocessing tools
✔ Run tools in the Geoprocessing pane
✔ Run tools in ModelBuilder
✔ Run tools in the Python window
2. GIS spatial analysis and functions
3. Vector processing tools and cases
4. Basic processing of vector data (stitching, cropping, fusion, etc.) and cases
5. Vector space overlay analysis and application
6. Proximity analysis and application
Chapter 6: ArcGIS raster spatial analysis and application
1. Introduction to spatial analysis of raster data
2. Geoprocessing environment settings; application, tool, model, and model process environment settings
3. Basic processing of raster data (stitching, cropping, resampling, NoData processing, data conversion, etc.) and cases
4. Distance Mapping
5. Density Mapping
6. Raster interpolation
7. Statistical analysis
8. Reclassify
9. Raster Computing
10. Calculation and application of raster data model
video
Chapter 7: Remote Sensing Image Processing
1. UAV data processing
✔ Data loading
✔ Puncture points
✔ correction
✔Accuracy evaluation
✔ Generate products
2. Mosaic dataset
✔ Create mosaic datasets
✔Add rasters to mosaic datasets
✔Remove black border
✔Image color uniformity
✔ Synchronize mosaic datasets
✔ Mosaic dataset repair
✔NDVI calculation
3. Remote sensing image preprocessing and information extraction
✔Image viewing
✔ Image Enhancement
✔ Band combination
✔ Orthorectification
✔Image fusion
✔ Extraction of vegetation index
✔ Using raster function chains
4. Remote sensing image classification
✔ Create classification samples
✔ Image segmentation
✔Training sample management
✔ Choose a classifier
✔ Output classification results
✔ Merge classes
✔ Reclassify
✔Accuracy evaluation
chapter eight
3D analysis
1. 3D production and animation demonstration
2. 3D data source
- 2D and 3D data
- BIM data
- Oblique photogrammetry data
3. Three-dimensional data analysis
- DEM three-dimensional production
- 3D format conversion
- 3D symbol design
- Quickly create 3D models
- PolyhedronEdit
- Visibility analysis
4. Digital surface model and its application
- Create a raster surface
- Basic Analysis Method Based on Grid DEM
- Slope and aspect calculations
- Create curved surfaces
- Hydrological analysis
- Visibility Computing
- Hillshade Calculation
5. Use of Lidar data
- Create a LAS dataset
- Generate DEM, DSM
extract trees
Advanced
Chapter nine
Spatial statistical analysis and spatial relationship modeling analysis
1. Fishnet analysis
- How to Set Spatial Extents
- Set the number of rows and columns
- Rotation angle
- output feature class
2. Spatial Autocorrelation Analysis "Analyze Mode" Tool
- average nearest neighbor
- High/Low Clustering : The Getis-Ord General G statistic measures the degree of clustering of high or low values
- Global Moran's I statistic measures spatial autocorrelation
- Hot spot analysis
- Optimized Hot Spot Analysis
- Initial Data Evaluation
- event aggregation
- Count events within aggregated polygons
- Capture nearby events to create weighted points
- Analysis range
- Hot Spot Analysis: Gi* statistics are automatically corrected for multiple testing and spatial dependence using a false discovery rate (FDR) correction method .
- Output Result: The output features will reflect the aggregated weighted features (fishnet or hexagon cells or the aggregated polygons or weighted points provided for the Incident Points Aggregated Polygons parameter). Each feature has a z-score, p-value , and Gi Bin result, as well as the number of neighbors each feature included in the calculation.
3. Spatial relationship modeling
Ordinary Least Squares (OLS)
- Geographically Weighted Regression (GWR)
- Complete the dataset with the Fill Missing Values tool
- Three types of regression models: continuous (Gaussian), binary (logistic), and count (Poisson)
- Select Neighborhood (Bandwidth)
- local weight scheme
- predict
- coefficient grid
- Interpret messages and diagnostics
- output graph
Multiple scale geographically weighted regression (MGWR)
Chapter 10 GIS-based Geospatial Modeling
Geospatial Modeling Ideas
2. GIS-based geospatial modeling
- Analysis of model connotation
- Realization of Model Based on GIS
- Using Modelbuilder to Build Computational Models
3. Modelbuilder modeling environment introduction
- Build and save the model in ModelBuilder.
- Set model parameters.
- Set model tool properties.
- recording tool
4. NC data batch processing modeling training
5. Implement the Suitability Modeling Workflow
6. Create a suitability model:
- Determine and prepare conditional data
- Convert the value of each condition to a generic suitability scale
- Weight conditions relative to each other and combine them to create a suitability map
- Find a siting area or protected area
- Share and run on the server
Chapter 11 Introduction to Python Scripting in ArcGIS Pro
1. Introduction to Python and environment configuration and installation;
2.Python basics
3. Introduction to ArcPy import and function
- Charts module (arcpy.charts)
- Data Access Module (arcpy.da)
- Geocoding module (arcpy.geocoding)
- Image analysis module (arcpy.ia)
- Cartography module (arcpy.mp)
- Metadata module (arcpy.metadata)
- Network Analyst modules (arcpy.nax and arcpy.na)
- Sharing module (arcpy.sharing)
- Spatial Analyst Module (arcpy.sa)
Workflow Manager (Classic) Module (arcpy.wmx)
4. Access and manage spatial data
5. Element geometry object operation
6. Raster data operation
7. Cartography
8. Tool creation in the Python toolbox
Special Application Analysis
Hydrological analysis
suitability evaluation
