ring pattern of older adult population in urban areas

1. Ring-Pattern of Older Adult
Population in Urban Areas
Yu Zhou (Department of Geography)
Jie Wu (Office of Institutional Research)
Bowling Green State University
Bowling Green, OH 43403

2. Mapping Population
A traditional way to map population is by using
choropleth method.

3. A choropleth map is simple to make, easy to
understand, and widely used to show the spatial
distribution of different themes.

4. A choropleth map, however, has many shortages. A good example
is that the spatial distribution displayed on choropleth maps can
only be explained by a map reader’s visual interpretation in a
subjective way.

5. This problem can make a huge impact on the decision-
making process.
Where should we build a new meals-on-wheel site?

6. Pattern Analysis
Pattern analysis is the study of the spatial arrangements
of features in 2D space (Chang, 2015).
At the general level, a pattern analysis can reveal if a
distribution pattern is random, dispersed, or clustered.
Pattern analysis can answer the question of “what is
the probability that the distribution of these
features is occurring due to random chance?”
Spatial statistics quantify the spatial pattern.

7. Pattern Analysis in ArcGIS
ArcGIS provides many
pattern analysis tools.
The toolset, however, is
kind of confusing.

9. Analyzing Patterns toolset
The tools in the Analyzing Patterns toolset identify
distances where the processes promoting spatial
clustering are most pronounced.
(Esri documentation)

10. Analyzing Patterns tools start with the null hypothesis
that the features, or the values associated with the
features, exhibit a spatially random pattern.
They then compute a p-value representing the
probability that the null hypothesis is correct.

11. Analyzing Patterns tools provide statistics that quantify
broad spatial patterns. These tools answer question
such as, "Are the features in the dataset, or the values
associated with the features in the dataset, spatially
clustered?"
Analyzing Patterns tools do not generate maps!

12. Mapping Clusters toolset
Unlike the Analyzing Patterns tools (which answer the
question “is there spatial clustering?" with yes/no), the
Mapping Clusters tools allow visualization of the
cluster locations and extent (meaning, produce maps).

13. These tools answer the questions of:
"Where are the clusters (hot spots/cold
spots)?“
"Where are the spatial outliers?”
"Which features are most alike?"

14. Hot Spot Analysis
Hot Spot Analysis tool calculates the Getis-Ord Gi*
statistic for each feature in a dataset. The Gi* statistic
returned for each feature in the dataset is a z-score.

15. The calculated z-scores (with p-values) indicate
where features with either high or low values cluster
spatially.
The tool works by looking at each feature within the
context of neighboring features. The local sum for a
feature and its neighbors is compared proportionally to
the sum of all features. If the local sum is very
different from the expected local sum, and that
difference is too large to be the result of random, a
statistically significant z-score results.

16. For statistically significant positive z-scores, the
larger the z-score is, the more intense the clustering
of high values (hence, the hot spots).
For statistically significant negative z-scores, the
smaller the z-score is, the more intense the
clustering of low values (cold spots).

17. Hot Spot Analysis tool creates a new feature class with
a z-score, p-value, and confidence level bin (Gi_Bin)
for each feature in the input feature class.
The new output feature class is automatically added to
the table of contents with default symbology applied to
the Gi_Bin field.

18. Applications of Hot Spot Analysis:
Crime analysis
Epidemiology
Voting pattern analysis
Economic geography
Retail analysis
Traffic incident analysis
Demographics
Where are the dominant IT services (or manufacturing)
in Ohio?

19. Mapping of Ohio’s Elderly Population
Data: Esri Community Dataset
Updated 2001 Demographics

22. Hot Spot Mapping of Ohio’s Elderly Population

23. Toledo
Ring Patterns of Older Adults in Urban Area

24. Dayton

25. Cleveland

26. Cleveland-Akron

27. Cincinnati

28. Columbus

29. Conclusion
A choropleth population map displays visual spatial
pattern, which is subjective.
Hot spot population map reveals spatial pattern based on
spatial statistics.
Hot spot map of Ohio’s elderly population shows a ring-
pattern for most urban areas.
This ring-pattern can be a good reference for the future
decision making.

30. Thank You!