GIS Spatial Statistics for Public Opinion Survey Response Rates
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1 GIS Spatial Statistics for Public Opinion Survey Response Rates July 22, 2015 Timothy Michalowski Senior Statistical GIS Analyst Abt SRBI - New York, NY t.michalowski@srbi.com
2 Introduction Timothy Michalowski, Senior Statistical GIS Analyst Abt SRBI in New York City GIS Manager - Abt SRBI GIS group in the Advanced Methods department, formed in years GIS experience, focus on GIS for Social Research. Abt SRBI - One of the nation s largest and most trusted survey, opinion, and policy research organizations. Abt SRBI offices in NYC, Cambridge, DC area, Chicago, Arizona, North Carolina, & Florida. Expertise in many practice groups, including Health, Transportation, Social Policy, Housing, Energy, Election Polling, etc. Abt SRBI pg 2
3 Introduction Goals for today Keep everyone awake, it is 8:30am! Why is GIS important to Survey Research? Sample of GIS work at Abt SRBI for Survey Research Geographic Based Statistics for Response Rates Get you out of here for SD Padres 12:40pm game!!!! Abt SRBI pg 3
4 GIS for Survey Research For 2012 to 2015: the GIS market in the US to grow at a Compound annual growth rate of 10.96% the key growth is due to increased demand from Government sectors. The GIS industry grew worldwide 10.3% in 2010 to US $4.4 billion with an and was expected to US $10.6 Billion in Global Industry Analysts (GIA), Inc., Geographic Information Systems (GIS): A Global Outlook, January Abt SRBI pg 4
5 GIS for Survey Research The fastest growing segment of GIS from 2004 to 2011 was in GIS data, which grew at a compound annual rate of 15.5%, twice the rate of growth for GIS software and services. Global sales to governmental agencies remained had a 7.2% compound annual growth rate over these eight years. demand for GIS/geospatial products is driven by an increasing global need for geographically correlated information. As more and more websites, such as Google Earth, and consumer navigation systems, bring awareness of the power of linking business and consumer information with their geography, geo applications will become the norm. Bridgett Pelosi, Daratech, Inc, GIS/Geospatial Market Report," January 2011, available from Directions Magazine, accessed March 28, Abt SRBI pg 5
6 Survey of GIS Survey Researchers Abt SRBI GIS completed a Survey of GIS Survey Researchers (March 2015) The target population for the survey was peer GIS professionals at fellow survey research organizations. Developed a short survey instrument to gauge immersion of GIS in the survey research marketplace. The short web survey was distributed to comparable major survey research competitors. Abt SRBI pg 6
7 Survey of GIS Survey Researchers Avg. Number of GIS Employees To All Employees (46%) (2%) Abt SRBI pg 7
8 Survey of GIS Survey Researchers Abt SRBI pg 8
9 Survey of GIS Survey Researchers Abt SRBI pg 9
10 Survey of GIS Survey Researchers Abt SRBI pg 10
11 Survey of GIS Survey Researchers Abt SRBI pg 11
12 Abt SRBI GIS for Survey Research Surveys always contain a where. Respondent Location (Street, Zip Code, City, State, County, Census geographies, neighborhoods, customized zones) Respondent Other Locations (Work, School, Shopping, Recreation, Transit Stations) Location of a Study Entity: (Powerplant, Coastline, Bank, Commercial Location, etc) Perceived Locations: (Where is?) There exists a geographic element to every survey for potential analysis. Abt SRBI GIS leverages the where in surveys Abt SRBI pg 12
13 GIS for Survey Research Start Main Study End Proposals Study Areas Simple Maps Demographics Survey Targets Basic uses of GIS Sample Plans Data Collection Field Surveys Geocoding Monitoring Quality Checks Analysis Visualization Web Maps GeoStatistics! Advanced uses of GIS Abt SRBI pg 13
14 Study Area Boundaries Abt SRBI pg 14
15 Study Area Boundaries Abt SRBI pg 15
16 Study Area Boundaries Abt SRBI pg 16
17 Complex Study Areas Abt SRBI pg 17
18 Complex Study Areas Customized Target Areas Abt SRBI pg 18
19 Demographics of Study Areas Abt SRBI pg 19
20 Demographics of Study Areas Survey Target Populations? Abt SRBI pg 20
21 Sample Plans Target Areas Oversampling of Transit Propensity Areas Census Tracts Abt SRBI pg 21
22 Sample Plans Target Areas Oversampling of Transit Propensity Areas Census Tracts Abt SRBI pg 22
23 Address Based Sample & GIS Increase in cell phone only households requires Address Based Sample (ABS) vs. traditional landline approach GIS allows for customized survey target plans w/abs Addresses directly in Tornado Paths Abt SRBI pg 23
24 Address Based Sample & GIS Address Based Sample Stratification Plan by Area in CT Evacuation Zones & Census Block Groups Abt SRBI pg 24
25 Address Based Sample & GIS Address Based Sample based on Stratification Plan Abt SRBI pg 25
26 Mapping Survey Results Abt SRBI pg 26
27 Mapping Survey Results Abt SRBI pg 27
28 Mapping Survey Results Abt SRBI pg 28
29 GEOGRAPHIC STATISTICS! Abt SRBI pg 29
30 Why are GeoStatistics important for Survey Research? Survey plans/sample plans look at a geographic distribution on a broad level statistically, weight those large areas For general survey administration, assumptions made on a homogeneous spread within the areas Surveys expect the selected study sample to reflect the population distribution and mirror the natural clustering. GeoStatistics helps to either confirm the natural pattern or reveals departures from expected. Abt SRBI pg 30
31 Why are GeoStatistics important for Survey Research? Statistically significant geographic patterns of data: Examination of survey participation rates to geography Location of where surveys are completed, from survey distribution locations, could impact survey participation Geographic clustering can be considered in survey design, sample plans, response rates, etc. Spatial patterns of survey results help explain findings Geographic areas could be oversampled for a study, just as certain demographics are Abt SRBI pg 31
32 What are GeoStatistics? Spatial AutoCorrelation Source: Data Urbanism. Dataurbanist.com Positive Spatial No spatial Negative spatial autocorrelation autocorrelation autocorrelation CLUSTERED RANDOM DISPERSED (+) (0) (-) Statistical tests to measure significance of spatial autocorrelation Abt SRBI pg 32
33 Geostatistics Methods / Tools Z scores & P values Source: ESRI, resources.arcgis.com Z score measure of standard deviation P value measure of significance Abt SRBI pg 33
34 Geostatistics Methods / Tools Anselin Global Moran s I Moran s I is used to measure spatial autocorrelation, i.e. clustering Global Moran s I Determines whether all features part of a statistically significant cluster Tests if overall pattern is randomly distributed (null hypothesis), dispersed, or clustered Assigns a Z score & P value for all data GLOBAL STATISTIC A measurement of all data points Abt SRBI pg 34
35 Geostatistics Methods / Tools Anselin Local Moran s I Moran s I is used to measure spatial autocorrelation, i.e. clustering LOCAL STATISTIC A measurement of each data point Local Moran s I Determines whether each feature is part of a statistically significant cluster Identifies statistically significant hot spots, cold spots, and spatial outliers Assigns a Z score & P value for each data point Abt SRBI pg 35
36 Example 1: GPS Unit Retrieval GPS Units sent to households in Minneapolis for a Travel Survey Source: Abt SRBI % of Households returned GPS Units with valid travel data Q: Is there a geographic pattern to GPS travel study compliance? Abt SRBI pg 36
37 Example 1: GPS Unit Retrieval Map the household locations with value of compliance vs. non-compliances Use of Interpolation first to visualize spatial patterns Inverse Distance Weighting (IDW). Clusters do appear of non-compliance but are there statistically valid patterns? Source: Abt SRBI Abt SRBI pg 37
38 Example 1: GPS Unit Retrieval Source: ESRI, resources.arcgis.com Z score measure of standard deviation P value measure of significance Abt SRBI pg 38
39 Example 1: GPS Unit Retrieval LOCAL Moran s I Tests INDIVIDUAL data point for clustering, part of a statistically significant cluster ( hot spot ) for the variable "GPS unit return compliance Each point receives a Z score and P value RESULTS = Most Z scores < 1.65 and all P values > 0.1 NOT SIGNIFICANT GLOBAL Moran s I Tests OVERALL dataset for clustering Overall Z score and P value RESULTS = Z score = 0.78, P value = 0.43 NOT SIGNIFICANT Abt SRBI pg 39
40 Example 1: GPS Unit Retrieval One-way ANOVA Demographic Data RESULTS: Age / household size are significantly correlated with GPS compliance Household location not significantly correlated with GPS compliance Age and household size are the most significant predictors of GPS compliance, not geographic location Source: Abt SRBI Abt SRBI pg 40
41 Example 1: ESRI UC Map Gallery 2015! GPS Unit Retrieval Abt SRBI pg 41
42 Example 1: ESRI UC Map Gallery 2015! GPS Unit Retrieval Abt SRBI pg 42
43 Example 2: Survey Response Rate Coastline Survey of households with fishing licenses in Southern California % response rate for completed surveys Q: Is there a geographic pattern to response rate? Q: Does the response rate vary by distance from coastline? Source: Abt SRBI Abt SRBI pg 43
44 Example 2: Survey Response Rate From Coastline Interpolation of data points, households with completed surveys (green) vs. non-response (red) Can be used to predict response rate in non sampled areas Reveals a general pattern of clustering of completed surveys near coastline (green) Statistically significant? Source: Abt SRBI Abt SRBI pg 44
45 Example 2: Survey Response Rate From Coastline Directional Distribution Mapping distributional trend Elliptical polygons centered on the mean centers: Households with completed surveys vs. non-response Source: ESRI, resources.arcgis.com Source: Abt SRBI Abt SRBI pg 45
46 Example 2: Survey Response Rate From Coastline Global Moran s I Z score = 2.86 P value = Results: Strong significance of spatial autocorrelation clustering Correlation between survey response and distance from coastline Source: Abt SRBI Abt SRBI pg 46
47 Example 3: Foreclosure survey sample selection A survey for homeowners in neighborhoods with highest foreclosure rates. National dataset of foreclosures - Where to conduct interviews? Q: Where are significant neighborhoods ( clusters ) of foreclosures? Anselin Local Moran s I for all foreclosure locations to find points with P<.05 and highest Z scores selected as central points for sampling neighborhood interviews. Abt SRBI pg 47
48 Example 3: Foreclosure survey sample selection Anselin Global Moran s I used for all foreclosure locations to verify that the entire dataset has a Z score of clustering and P value of significance. Getis Ord to examine concentrations of hot spots and cold spots. Abt SRBI pg 48
49 Example 3: Foreclosure survey sample selection Data points (foreclosure locations) with P<.05 and highest Z scores selected as central points for sampling neighborhood interviews. Source: Abt SRBI Abt SRBI pg 49
50 Example 4: Field survey location analysis A survey completed of voting behavior Completed survey data lists addresses / persons who voted (green) or did not vote (red) Corresponding polling places are known (yellow) Q: Is there a significance between polling location and voting behavior? Source: Abt SRBI Abt SRBI pg 50
51 Example 4: Field survey location analysis Geographically Weighted Regression (GWR) / Exploratory Regression Analysis Dependent Variable: Voted / Did Not Vote Explanatory Variable: Distance to polling location RESULT: Through geographic based statistical tests, interpretation of results, distance to polling place shows a significant correlation on voting behavior Abt SRBI pg 51
52 Example 4: Field survey location analysis Reverse: Could be used to also measure significance of field survey interviewing locations (customer satisfaction surveys). Do respondents who live farther from survey locations show correlation to non-response? A tool to better establish survey distribution locations for potential respondents Source: Abt SRBI Abt SRBI pg 52
53 Conclusions Collected survey data has an inherent location (respondent address, survey location, etc.) GIS adds a dimension to existing survey data with advanced analysis via geographic based statistics Geographic statistics can be used to help better plan for and optimize surveys, examine sample address data, regional response rates, clusters of data, and management of field survey locations, etc. Abt SRBI pg 53
54 Questions? Abt SRBI pg 54
55 Thank you! Timothy Michalowski Senior Statistical GIS Analyst Abt SRBI 275 Seventh Ave, Suite 2700 New York, NY 10001
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