Goal: Where does statistically significant spatial clusters of high values (hot spots) and low values (cold spots) of drop lie?
Variable: drop (employment loss)
- Monthly employment Jan 2003- Dec.2014
- Data availabl:2840 counties
Tool : Hot spot analysis
Step:
- Add Mean center of population.shp (3141 counties), U.S. county.shp (3141 counties), drop value (2840 counties)
- Join the drop to the mean center, export as a new layer file.
- Project the data
- Run Hot Spot Analysis (Default for Conceptualization of Spatial Relationship, and Distance Method)
- Make a Map
- Distribution of drop, using natural Break
2 Hot spot Analysis using 3141 counties.
- Low employment loss gathers in north part
- High employment loss lies in west and southeast part
Questions:
- Previous map is made of 3141 counties, however, there are only 2840 counties with drop value. Missing value will be noted as 0, will this affect the result?
- Hawaii and Alaska are included. Will exclusion of HI and AK affect the result?
Both: Yes
- Redraw the map
- Only keep the matched records – 2838 counties
- Exclude Hawaii and Alaska
3. Hot spot Analysis using 2838 counties
4. Hot spot Analysis using U.S. contiguous counties, Hawaii and Alaska removed.
Conclusion: Hot Spot analysis is sensitive to spatial outlier. It only identifies low value clusters or high value clusters. What if I want to find an outlier, for example low value unit among high value cluster?
I should use spatial autocorrelation tool- Anselin Local Moran’s I
