{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106241"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106241","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Factors influencing hunting license sales in urban and rural areas of Illinois","abstract":"Sales of hunting licenses have fallen in the past decades. To seek the means to maintain or increase current hunter numbers, state agencies need to understand their existing market base before developing strategies to boost sales. This can be achieved by exploring the characteristics of the hunting population and factors influencing hunting license sales. Recent studies have examined these factors influencing hunting license sales at an aggregated scale. These studies helped to understand not only the influence of these factors, but also the environment in which individual indicators are embedded. This study used a similar approach, but with more factors and different models. The study area was the state of Illinois. The study consisted of three parts. The first created a regression model for the entire state. Different socioeconomic and biophysical factors were included in the model. Model was transformed to reduce heteroscedasticity and non-normality. Stepwise regression was applied to the transformed model to select variables. The second part addressed the differences between rural and urban areas in Illinois, using the same methods as in the first part. The third part considered spatial dependency of the model residuals, using the Moran test and Lagrange Multiplier test for diagnosis. Global models (spatial lag model, spatial error model, and hierarchical linear model [HLM]) and a local model (geographically weighted regression [GWR]) were applied to deal with spatial autocorrelation of the residuals. The first part found that accessibility to hunting resources, economic status, age structure, education, race and ethnicity, and competition with general recreation influenced Illinois hunting license sales. The second part found that the significant factors for the entire state, rural areas, and urban areas were different. The influence of the eight variables was robust over different models. The third part found spatial dependency in the residuals of the model used in the first part. Spatial regression (spatial lag and spatial error models), HLM, and GWR were applied to reduce spatial dependency. GWR had the best fit of all the models considered. Spatial lag regression had the best fit of all the global models. The spatial lag regression model excluded spatial dependency in the residuals, but there was some spatial dependency in the residuals of the other models.","abstract_html":"Sales of hunting licenses have fallen in the past decades. To seek the means to maintain or increase current hunter numbers, state agencies need to understand their existing market base before developing strategies to boost sales. This can be achieved by exploring the characteristics of the hunting population and factors influencing hunting license sales. Recent studies have examined these factors influencing hunting license sales at an aggregated scale. These studies helped to understand not only the influence of these factors, but also the environment in which individual indicators are embedded. This study used a similar approach, but with more factors and different models. The study area was the state of Illinois. The study consisted of three parts. The first created a regression model for the entire state. Different socioeconomic and biophysical factors were included in the model. Model was transformed to reduce heteroscedasticity and non-normality. Stepwise regression was applied to the transformed model to select variables. The second part addressed the differences between rural and urban areas in Illinois, using the same methods as in the first part. The third part considered spatial dependency of the model residuals, using the Moran test and Lagrange Multiplier test for diagnosis. Global models (spatial lag model, spatial error model, and hierarchical linear model [HLM]) and a local model (geographically weighted regression [GWR]) were applied to deal with spatial autocorrelation of the residuals. The first part found that accessibility to hunting resources, economic status, age structure, education, race and ethnicity, and competition with general recreation influenced Illinois hunting license sales. The second part found that the significant factors for the entire state, rural areas, and urban areas were different. The influence of the eight variables was robust over different models. The third part found spatial dependency in the residuals of the model used in the first part. Spatial regression (spatial lag and spatial error models), HLM, and GWR were applied to reduce spatial dependency. GWR had the best fit of all the models considered. Spatial lag regression had the best fit of all the global models. The spatial lag regression model excluded spatial dependency in the residuals, but there was some spatial dependency in the residuals of the other models.","abstract_has_math":false,"creators":["Zhang, Xiaohan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Natural Res & Env Sciences","degree_department":null,"school":null,"contributors":["Miller, Craig","Brazee, Richard","McLafferty, Sara","Vaske, Jerry"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T21:58:22Z","date_published":"2020-03-02T21:58:22Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Hunting license","regression","rural and urban","spatial dependency","spatial regression"],"languages":["en"],"rights":["Copyright 2019 Xiaohan Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106241","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Miller, Craig","Brazee, Richard","McLafferty, Sara","Vaske, Jerry"]},{"key":"dc:creator","label":"Author","values":["Zhang, Xiaohan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T21:58:22Z","2019-12-04","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Natural Res & Env Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Hunting license","regression","rural and urban","spatial dependency","spatial regression"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Xiaohan Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106241"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Sales of hunting licenses have fallen in the past decades. To seek the means to maintain or increase current hunter numbers, state agencies need to understand their existing market base before developing strategies to boost sales. This can be achieved by exploring the characteristics of the hunting population and factors influencing hunting license sales. Recent studies have examined these factors influencing hunting license sales at an aggregated scale. These studies helped to understand not only the influence of these factors, but also the environment in which individual indicators are embedded. This study used a similar approach, but with more factors and different models. The study area was the state of Illinois. The study consisted of three parts. The first created a regression model for the entire state. Different socioeconomic and biophysical factors were included in the model. Model was transformed to reduce heteroscedasticity and non-normality. Stepwise regression was applied to the transformed model to select variables. The second part addressed the differences between rural and urban areas in Illinois, using the same methods as in the first part. The third part considered spatial dependency of the model residuals, using the Moran test and Lagrange Multiplier test for diagnosis. Global models (spatial lag model, spatial error model, and hierarchical linear model [HLM]) and a local model (geographically weighted regression [GWR]) were applied to deal with spatial autocorrelation of the residuals. The first part found that accessibility to hunting resources, economic status, age structure, education, race and ethnicity, and competition with general recreation influenced Illinois hunting license sales. The second part found that the significant factors for the entire state, rural areas, and urban areas were different. The influence of the eight variables was robust over different models. The third part found spatial dependency in the residuals of the model used in the first part. Spatial regression (spatial lag and spatial error models), HLM, and GWR were applied to reduce spatial dependency. GWR had the best fit of all the models considered. Spatial lag regression had the best fit of all the global models. The spatial lag regression model excluded spatial dependency in the residuals, but there was some spatial dependency in the residuals of the other models.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Xiaohan Zhang, accepted the attached license on 2019-12-04 at 12:17.","The student, Xiaohan Zhang, submitted this Dissertation for approval on 2019-12-04 at 12:18.","This Dissertation was approved for publication on 2019-12-04 at 14:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14688 on 2020-02-28 at 17:15:05","Made available in DSpace on 2020-03-02T21:58:22Z (GMT). No. of bitstreams: 3 ZHANG-DISSERTATION-2019.pdf: 4519086 bytes, checksum: 7f1428f4ea9b679ca2368cdb085b9132 (MD5) LICENSE.txt: 4210 bytes, checksum: e218007135e663b09d1c4b66a998135d (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: 3575093c28df526742addbf221ec710b (MD5) Previous issue date: 2019-12-04"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Factors influencing hunting license sales in urban and rural areas of Illinois"]}]}],"canonical_facts":{"dc:contributor":["Miller, Craig","Brazee, Richard","McLafferty, Sara","Vaske, Jerry"],"dc:creator":["Zhang, Xiaohan"],"dc:date":["2020-03-02T21:58:22Z","2019-12-04","2019-12"],"dc:description":["Sales of hunting licenses have fallen in the past decades. To seek the means to maintain or increase current hunter numbers, state agencies need to understand their existing market base before developing strategies to boost sales. This can be achieved by exploring the characteristics of the hunting population and factors influencing hunting license sales. Recent studies have examined these factors influencing hunting license sales at an aggregated scale. These studies helped to understand not only the influence of these factors, but also the environment in which individual indicators are embedded. This study used a similar approach, but with more factors and different models. The study area was the state of Illinois. The study consisted of three parts. The first created a regression model for the entire state. Different socioeconomic and biophysical factors were included in the model. Model was transformed to reduce heteroscedasticity and non-normality. Stepwise regression was applied to the transformed model to select variables. The second part addressed the differences between rural and urban areas in Illinois, using the same methods as in the first part. The third part considered spatial dependency of the model residuals, using the Moran test and Lagrange Multiplier test for diagnosis. Global models (spatial lag model, spatial error model, and hierarchical linear model [HLM]) and a local model (geographically weighted regression [GWR]) were applied to deal with spatial autocorrelation of the residuals. The first part found that accessibility to hunting resources, economic status, age structure, education, race and ethnicity, and competition with general recreation influenced Illinois hunting license sales. The second part found that the significant factors for the entire state, rural areas, and urban areas were different. The influence of the eight variables was robust over different models. The third part found spatial dependency in the residuals of the model used in the first part. Spatial regression (spatial lag and spatial error models), HLM, and GWR were applied to reduce spatial dependency. GWR had the best fit of all the models considered. Spatial lag regression had the best fit of all the global models. The spatial lag regression model excluded spatial dependency in the residuals, but there was some spatial dependency in the residuals of the other models.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-02-28 without embargo terms","The student, Xiaohan Zhang, accepted the attached license on 2019-12-04 at 12:17.","The student, Xiaohan Zhang, submitted this Dissertation for approval on 2019-12-04 at 12:18.","This Dissertation was approved for publication on 2019-12-04 at 14:07.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14688 on 2020-02-28 at 17:15:05","Made available in DSpace on 2020-03-02T21:58:22Z (GMT). No. of bitstreams: 3 ZHANG-DISSERTATION-2019.pdf: 4519086 bytes, checksum: 7f1428f4ea9b679ca2368cdb085b9132 (MD5) LICENSE.txt: 4210 bytes, checksum: e218007135e663b09d1c4b66a998135d (MD5) PROQUEST_LICENSE.txt: 4556 bytes, checksum: 3575093c28df526742addbf221ec710b (MD5) Previous issue date: 2019-12-04"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/106241"],"dc:language":["en"],"dc:rights":["Copyright 2019 Xiaohan Zhang"],"dc:subject":["Hunting license","regression","rural and urban","spatial dependency","spatial regression"],"dc:title":["Factors influencing hunting license sales in urban and rural areas of Illinois"],"dc:type":["text"],"thesis:degree_discipline":["Natural Res & Env Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}