65429 پروژه gis
یک سال پیش منتشر شده
تعداد بازدید: 79
کد پروژه: 453085
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سلام
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Create a regression model based on the characterises of spatial pattern of vegetation that more effectively explains the variation in temperature.
• Independent variables:
All from “CA” to “AI”
• Use “MNF” instead of “ENN_MN”
• Use “AMF” instead of “ENN_AM”
• Use “AIF” instead of “AI”
• Dependent variable:
Temperature: “MEAM”
• Spatial weight matrix: Contiguity, 1st order
Answer the questions below in your report:
1. Are your samples independent from each other?
2. If the answer to the above question is yes: • Run a global spatial regression model and justify your selection regarding the model you chose • Run a local spatial regression model (GWR) ❖ Select variables based on all the following approaches: PCA, forward and backward
3. Did you observe any difference in the final model in relation to the approach to variable selection? • For each model and variable selection approach: which independent variables did your final model include? Sort them based on their individual importance.
4. Which regression model/variable selection approach outperformed? • Were your spatial models able to address the issue of dependency among samples?
5. What new insights an be drawn based on a local versus a global regression model? Describe your observations and elaborate on their implications
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