DIFFERENT INFLUENCES OF SOCIOECONOMIC FACTORS ON THE HUNTING AND FISHING LICENSE SALES IN COOK COUNTY, IL

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1 DIFFERENT INFLUENCES OF SOCIOECONOMIC FACTORS ON THE HUNTING AND FISHING LICENSE SALES IN COOK COUNTY, IL Xiaohan Zhang and Craig Miller Illinois Natural History Survey University of Illinois at Urbana Champaign

2 BACKGROUND Hunting and fishing are popular recreational activates in the US 13.7 million hunters in the US in million anglers in the US in 2011 Socioeconomic status affects participation in hunting and fishing, and thus influences license sales Socioeconomic status significantly influences hunting license sales (Floyd and Lee 2002) Few of previous studies linked the license sales with the aggregated socioeconomic context

3 OBJECTIVE Explore the influence of aggregated socioeconomic factors (census tract level) on hunting/fishing license sales in Cook County, IL Explore whether the influence differ between hunting and fishing license sales Detect hotspots requiring further studies

4 DATA Hunting and fishing license sales data From Illinois Department of Natural Resources Use address information for geocoding Socioeconomic data From US Census Bureau 2010 ACS 5-year estimates Variables were selected based on empirical studies economic (median household income and poverty rate), occupation (unemployment rate), education (percentage of high school graduate or higher and percentage of bachelor s degree or higher), ethnic (percentage of Hispanic or American Africans), age structure (median age, old dependency ratio and age dependency ratio), household (Percentage of single mothers, household with no cars, one car, two cars and three or more cars)

5 METHODS Principle Component Analysis (PCA) Three components were extracted Quartimax rotation was applied LISA method Local Indicators of Spatial Association (LISA) indicate the presence or absence of significant spatial correlations for each location. Detect the spatial relationship Regression Linear regression Multinomial logistic regression

6 RESULTS Factor loadings of PCA Socioeconomic Status Household Mobility Age Index Percent Hispanic and African American.887 Percent bachelor's degree or higher Percent Female with child.825 Unemployment Rate.773 Median Income Percent high school graduate or higher Poverty Rate.720 Household with 3 or more cars Household with 2 cars Household with no car.749 Household with 1 car.664 Old Dependency Ratio*.949 Median Age.757 Age Dependency Ratio**.611 *Old Dependency Ratio: the ratio of older dependents--people older than 64--to the workingage population--those ages **Age Dependency Ratio: the ratio of dependents-- people older than 64 or younger than 14--to the working-age population-- those ages )

7 Socioeconomic Index

8 Spatial distribution of License Sales

9 LISA method for Hunting license (a) (b) (c) (a) (b) (c) correlation between density and Socioeconomic Status; correlation between density and Household Mobility; correlation between density and Age Index.

10 LISA method for Fishing license (a) (b) (c) (a) (b) (c) correlation between density and Socioeconomic Status; correlation between density and Household Mobility; correlation between density and Age Index.

11 Linear regression Hunting Coefficients Std. Error t p-value R-square Socioeconomic Status Household Mobility Age Index Fishing Coefficients Std. Error t p-value R-square Socioeconomic Status Household Mobility Age Index

12 Multinomial logistic regression (low license sales as reference) Hunting Coefficients Std. Error Wald p-value SES Medium HM AI SES High HM AI Fishing Coefficients Std. Error Wald p-value SES Medium HM AI SES High HM AI

13 Model Accuracy Linear Regression Predicted (hunting) Observed Percent Low Medium High Correct Low Medium High Overall Percentage Predicted (fishing) Observed Percent Low Medium High Correct Low Medium High Overall Percentage

14 Model Accuracy Multinomial logistic regression Predicted (hunting) Observed Percent Low Medium High Correct Low Medium High overall Percentage Predicted (fishing) Observed Percent Low Medium High Correct Low Medium High Overall Percentage

15 Model residuals--moran Scatter Plot The Moran scatter plot regresses a spatially lagged (a variable that essentially averages the neighboring values of a location)transformation of a variable (y-axis) on the original standardized variable (x-axis). The slope of the scatter plot corresponds to the value for Moran's I (a) Linear regression for hunting (b) Linear regression for fishing (c) Multinomial logistic regression for hunting (d) Multinomial logistic regression for fishing

16 HOTSPOTS DETECTION (HUNTING) (a) (b) (c) (a) (b) (c) correlation between density and Socioeconomic Status; correlation between density and Household Mobility; correlation between density and Age Index.

17 CONCLUSION Hunting and fishing license sales are influenced by surrounding socioeconomic context All the three indices positively influence the hunting license sales Household Mobility and Age Index positively influence the fishing license sales Linear regression predicted a linear negative correlation between the Socioeconomic Status and fishing license sales Multinomial logistic regression predicted a non-monotonic correlation.

18 CONCLUSION Hot spots were detected One of the three indices is likely to prevail over the others Factors other than the indices need to be explored to explain the local license Better understanding of the special cases in hot spots is probably able to guide the local license promotion.

19 CONCLUSION Regression Models need to be improved Multinomial logistic regression generated better predications, but the linear regression was more statistically correct.(regression assumes that the residuals are not auto-correlated.) To improve the logistic regression model by using a logistic mixed model and a geographically weighted logistic model (add spatial factors to the regression)

20 ACKNOWLEDGEMENT Illinois Department of Natural Resources Illinois Natural History Survey All Staff of Human Dimension Lab Federal Aid in Wildlife Restoration Grant W-112-R-24 All license purchasers

21 THANK YOU!