GIS CONFERENCE MAKING PLACE MATTER Decoding Health Data with Spatial Statistics
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1 esri HEALTH AND HUMAN SERVICES GIS CONFERENCE MAKING PLACE MATTER Decoding Health Data with Spatial Statistics Flora Vale Jenora D Acosta
2 Wait a minute
3 Wait a minute Where is Lauren??
4 Wait a minute Where is Lauren??
5 DAY 1 What are Spatial Statistics? Measuring Geographic Distributions Analyzing Patterns Multivariate Analysis DAY 2 Subjectivity of Maps Mapping Clusters Space Time Pattern Mining Modeling Spatial Relationships
6 Are you using Data or Information?
7 Spreadsheets Data or Information?
8 Maps Data or Information?
9 When you look at a spreadsheet
10 You ask for more Mean Standard Deviations Min and Max
11 Same goes for maps!
12 We can do more
13 What are Spatial Statistics?
14 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
15 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
16 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
17 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
18 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
19 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
20 Spatial Statistics are a set of exploratory techniques for describing and modeling spatial distributions, patterns, processes, and relationships.
21 coincidence area connectivity proximity orientation direction length
22
23
24
25
26 python script!
27
28 Measuring Geographic Distributions
29 Descriptive Statistics
30 Mean Median Standard Deviation
31 Central Feature identifies the most centrally located feature in a point, line, or polygon feature class
32
33 Y X
34 Y X
35 Y X
36 Y X
37 Y X
38 Y central feature X
39 Mean Center identifies the geographic center (or the center of concentration) for a set of features
40 Y (9,18) (13,12) (22,23) (14,14) (25,24) (24,16) (12,12) (14,8) (18,12) X
41 (14,14) (13,12) Y (25,24) (24,16) (22,23) (18,12) (17,15) mean center X (12,12) (14,8) (9,18) mean = (17,15)
42 Median Center identifies the location that minimizes overall Euclidean distance to the features in a dataset
43 X (9,18) (13,12) (22,23) (14,14) (25,24) (24,16) (12,12) (14,8) (18,12) Y
44 Y X: Y: median = (14,14) median center X
45 Mean vs Median?
46 Y (176,138) (9,18) (13,12) (22,23) (14,14) (25,24) (24,16) (12,12) (14,8) (18,12) X
47 Y (14,14) (13,12) (25,24) (24,16) (22,23) (18,12) (12,12) (14,8) (9,18) (74,38) X mean = (33,28)
48 Y X: Y: median = X (16,15) (14,14) (13,12) (25,24) (24,16) (22,23) (18,12) (12,12) (14,8) (9,18) (74,38) mean = (33,28)
49 Y X: Y: median = X (16,15) (14,14) (13,12) (25,24) (24,16) (22,23) (18,12) (12,12) (14,8) (9,18) (74,38) mean = (33,28)
50 Y (33,28) (16,15) (17,15) (14,14) X
51 Linear Directional Mean identifies the mean direction, length, and geographic center for a set of lines
52 Y X
53 Y X
54 Standard Distance measures the degree to which features are concentrated or dispersed around the geometric mean center
55 Y mean center X
56 Y X
57 Directional Distribution (Standard Deviational Ellipse) creates standard deviational ellipses to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends
58 Y mean center X
59 Y X
60 Analyzing Patterns
61 Global Inferential Statistics
62 Is there a PATTERN?
63 Clustered
64 Clustered Dispersed
65 Complete Spatial RANDOMNESS
66 z-scores and p-values z-scores p-value
67 z-scores and p-values 90% random 90% 95% 95% 99% 99% z-scores p-values
68 How intense is the clustering?
69 How intense is the clustering? Compared to what?
70 How intense is the clustering? Compared to where?
71 How intense is the clustering? Compared to when?
72 An Example Is the spatial segregation of the rich and the poor increasing or decreasing in New York?
73 An Example
74 Average Nearest Neighbor calculates a nearest neighbor index based on the average distance from each feature to its nearest neighboring feature
75
76
77
78
79
80
81 average distance = expected average = distance
82 ANN ratio = observed expected
83 ANN ratio = observed expected Clustered < 1
84 ANN ratio = observed expected Clustered < 1 < Dispersed
85 Spatial Autocorrelation (Moran s I) measures spatial autocorrelation based on feature locations and attribute values using the Global Moran's I statistic
86 Are distances and values correlated? Clustered Dispersed
87
88
89 Incremental Spatial Autocorrelation measures spatial autocorrelation for a series of distances and optionally creates a line graph of those distances and their corresponding z-scores
90 5 Spatial Autocorrelation by Distance 4 z-score Distance (meters)
91 5 Spatial Autocorrelation by Distance 4 z-score Distance (meters)
92 5 Spatial Autocorrelation by Distance 4 z-score Distance (meters)
93 5 Spatial Autocorrelation by Distance 4 Fixed Distance Band = 70.3 meters z-score Distance (meters)
94 High/Low Clustering (Getis-Ord General G) measures the concentration of high or low values for a given study area
95 What type of clustering is present in the data? High value clusters Low value clusters
96 Multi-Distance Spatial Cluster Analysis (Ripleys K Function) determines whether features, or the values associated with features, exhibit statistically significant clustering or dispersion over a range of distances
97 5 Spatial Clustering by Distance 4 3 L(d) Distance (meters)
98 5 Spatial Clustering by Distance 4 statistically significant clustering 3 L(d) 2 statistically significant dispersion Distance (meters)
99 dispersed
100 clustered
101 Grouping Analysis groups features based on feature attributes and optional spatial/temporal constraints
102
103 K Means 3 groups 4 groups
104 K Means 2 groups 3 groups 4 groups
105 K Means 2 groups 3 groups 4 groups
106 K Means 2 groups 3 groups 4 groups
107 Minimum Spanning Tree
108 Minimum Spanning Tree
109 Minimum Spanning Tree
110 Minimum Spanning Tree
111 MATH!
112 interpret results through box plots
113 Similarity Search identifies which candidate features are most similar or most dissimilar to one or more input features based on feature attributes
114 potential store locations
115 potential store locations high performing store
116
117 LocID PopDensity AvIncome DistToCompetition
118 LocID PopDensity AvIncome DistToCompetition LocID PopDensity AvIncome DistToCompetition
119 LocID PopDensity AvIncome DistToCompetition Rank by how similar they are to based on: PopDensity AvIncome DistToCompetition LocID PopDensity AvIncome DistToCompetition
120
121
122 Input Feature(s) to Match Candidate Features Attributes of Interest PopDensity AvIncome DistToCompetition
123 Input Feature(s) to Match average Candidate Features Attributes of Interest PopDensity AvIncome DistToCompetition
124 3 Match Methods
125 3 Match Methods Attribute Values
126 3 Match Methods Attribute Values Ranked Attribute Values
127 3 Match Methods Attribute Values Ranked Attribute Values Attribute Profiles
128 Attribute Values
129 Attribute Values standardize attributes Z-transform: ( x - x ) / SD
130 Attribute Values Population = 14,159 standardize attributes % Uninsured =.26 Distance (km) =
131 Attribute Values Population = standardize attributes % Uninsured = Distance (km) =.6433
132 Attribute Values standardize attributes subtract candidate from target square differences sum squares
133 Ranked Attribute Values
134 Ranked Attribute Values rank attributes
135 Ranked Attribute Values 9.5 rank attributes
136 Ranked Attribute Values rank attributes
137 Ranked Attribute Values rank attributes subtract candidate from target square differences sum squares
138 Attribute Profiles
139 Attribute Profiles standardize attributes cosine similarity index cosine similarity index * Must have at least 2 attributes of interest
140 Dengue Fever Risk in Kenya
141 questions?
142 Want to learn more??? esriurl.com/spatialstats
143
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