# data[].attributes.body
Finally, spatial analysis aids in the discovery of data in specific locations, qualities, and correlations of features in spatial data using analytical tools to address a topic or statistic. We may then obtain the data that was collected at a specific point in seconds. We can also forecast new data values based on observations made after examining specific points.

# data[].attributes.body
a) Reclassify tools
- Create a remap table depending on the input raster values.
- The values in a raster are reclassified (or changed).

Process
To build a remap table depending on the values of the input raster, use the Classify or Unique options. The Classify option displays a window in which you can select a method from the Data classification methods and the number of classes. The Unique option will populate the remap table using the supplied dataset's unique values.

b) Theressian Polygon
- May proportionally divide and distribute a point coverage into areas and can also be used to determine a service area for a group of shops

Process
All points are triangulated to form a Delaunay-satisfying Triangular Irregular Network (TIN). The edges of the Thiessen polygons are formed by generating perpendicular bisectors for each triangle edge. The vertices of the Thiessen polygon are determined by where the bisectors intersect. Thiessen polygons are created in order to build polygon topology. The point fare locations were utilised as label points for the Thiessen polygons.

# data[].attributes.body
https://drive.google.com/drive/folders/1ayj5tkTGwHyjFy5Jwf2CK2zVDEY6l2-Y?usp=sharing

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https://drive.google.com/folderview?id=1LKdC8DhsTkttK0jrthjLP4n-ppjkhv4z

# data[].attributes.headline
Solve this problem. You are looking petrol station within 10km from the exit of the highway. You also need a fast-food restaurant such as McDonald's and KFC) nearby the petrol station. Suggest your idea and procedure can be applied using both (attribute and spatial) data queries.

# data[].attributes.subject
Solve this problem. You are looking petrol station within 10km from the exit of the highway. You also need a fast-food restaurant such as McDonald's and KFC) nearby the petrol station. Suggest your idea and procedure can be applied using both (attribute and spatial) data queries.

# data[].attributes.body
- Search for the petrol station - spatial data query
- Search for Mcdonald and KFC - attribute data query
- Locate the highway exit using select by location
- Use buffer, then draw a circle around the exit with 10km radius
- Through spatial data query, pick the petrol station within the circle
Look for the fast-food within the 10km radius of petrol station using attribute data query

# data[].attributes.body
Prepare the methodology involved to interpolate air quality values obtained from the Department of Environment (DOE) using Geostatistic of kriging interpolation.

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I forgot to screenshot my attendance so i took this evidence as there is my name on the meet.