GIS CONFERENCE MAKING PLACE MATTER Decoding Health Data with Spatial Statistics

Transcription

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

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26 python script!

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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

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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

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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

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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

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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

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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

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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

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