Spatial regression model of households: a case of the Czech Republic
In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as well as a reduction of family numbers in general as a result of the low fertility rate and the postponement of childbearing in Europe. On the o...
Saved in:
| Published in: | Espace populations sociétés Vol. 2022/1; no. 2022/1 |
|---|---|
| Main Author: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
Université de Lille
01.01.2022
Université des Sciences et Technologies de Lille |
| Subjects: | |
| ISSN: | 0755-7809, 2104-3752 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as well as a reduction of family numbers in general as a result of the low fertility rate and the postponement of childbearing in Europe. On the other hand, there has been a significant increase in the proportion of one-person households and families without children among people at an older age because of population ageing.The research question is to what extent these changes in the structure of households are spatially homogeneous and whether the proportion of households of a given type in a given territory can be explained by other demographic variables such as age, education, marital status or economic activity.The above-mentioned principles can be demonstrated by the example of data for households, whose detection and subsequent analysis is an integral part of population censuses. To solve this problem, it is possible to use spatial data analysis methods, which can be defined as a quantitative data analysis, in which the explanation is dependent on explicit spatial variables when predicting the investigated phenomenon based on spatial autocorrelation.The assumption of spatial regression is the existence of autocorrelation. The results of both Moran’s I and Geary’s C show that the autocorrelation for both types of households was found to be statistically significant and increases as the distance between adjacent elements (i.e. municipalities) decreases.Age is an important factor affecting the structure of households. The results for both types of households show that the age groups with the greatest influence on the creation of one-person households or one-family households can also be used to create a spatial model. A similar claim applies to the average age. Education showed that the share of persons with primary education has no influence on spatial regression, unlike the share of persons with secondary or university education. In the case of marital status, there is a statistically significant spatial regression for one-person households, but not clearly for one-family households. Economic activity or employment is statistically significant for simple regression even in a small territory such as the Czech Republic.For the solution, it is possible to use several types of models offered by (econometrics) theory. In the case of households, the spatial Durbin model (SDM) is relatively widely used, because of the inclusion of both endogenous and exogenous interaction effects, based on the criteria chosen (Log Likelihood, AIC, SBC). However, the results for other models (SAR, SEM, SDEM) are not significantly different. |
|---|---|
| AbstractList | In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as well as a reduction of family numbers in general as a result of the low fertility rate and the postponement of childbearing in Europe. On the other hand, there has been a significant increase in the proportion of one-person households and families without children among people at an older age because of population ageing.The research question is to what extent these changes in the structure of households are spatially homogeneous and whether the proportion of households of a given type in a given territory can be explained by other demographic variables such as age, education, marital status or economic activity.The above-mentioned principles can be demonstrated by the example of data for households, whose detection and subsequent analysis is an integral part of population censuses. To solve this problem, it is possible to use spatial data analysis methods, which can be defined as a quantitative data analysis, in which the explanation is dependent on explicit spatial variables when predicting the investigated phenomenon based on spatial autocorrelation.The assumption of spatial regression is the existence of autocorrelation. The results of both Moran’s I and Geary’s C show that the autocorrelation for both types of households was found to be statistically significant and increases as the distance between adjacent elements (i.e. municipalities) decreases.Age is an important factor affecting the structure of households. The results for both types of households show that the age groups with the greatest influence on the creation of one-person households or one-family households can also be used to create a spatial model. A similar claim applies to the average age. Education showed that the share of persons with primary education has no influence on spatial regression, unlike the share of persons with secondary or university education. In the case of marital status, there is a statistically significant spatial regression for one-person households, but not clearly for one-family households. Economic activity or employment is statistically significant for simple regression even in a small territory such as the Czech Republic.For the solution, it is possible to use several types of models offered by (econometrics) theory. In the case of households, the spatial Durbin model (SDM) is relatively widely used, because of the inclusion of both endogenous and exogenous interaction effects, based on the criteria chosen (Log Likelihood, AIC, SBC). However, the results for other models (SAR, SEM, SDEM) are not significantly different. |
| Author | Kraus, Jaroslav |
| Author_xml | – sequence: 1 givenname: Jaroslav surname: Kraus fullname: Kraus, Jaroslav |
| BookMark | eNptkLlKBEEQhhtRcNUNfINJDAxG-z7MZD1BEDzipo9qd2TcHrrHQJ_e0VUDMSqo-v6f4ttBm6u8AoT2CT7iGONjGOoRoQyrDTSjBPOWKUE30QwrIVqlsdlG81o7j7GkWmlOZ-jsfnBj5_qmwFOB6ZhXzUuO0Dc5Ncv8WmGZ-1hPGtcEV-FzOy6hWbxDWDZ3MLz6vgt7aCu5vsL8e-6ix4vzh8VVe3N7eb04vWkDk3hsBdXca864otF5znUQ2gP1kcZkaNAJggzJKCxAG-LAS5qU14RSQ7BQju2i63VvzO7ZDqV7ceXNZtfZr0UuT9aVsQs9WAbRCGloBJ641tE5Q5KRPkpmpAM5dR2vu0LJtRZINnTjpCKvxuK63hJsP53ayan9cjolDv8kfj74jz34ZnvIvxzFVkwYYVgbm8aBsQ9T0oTY |
| CitedBy_id | crossref_primary_10_4000_eps_12718 |
| Cites_doi | 10.1007/978-1-4757-5424-7 10.1111/pirs.12318 10.1007/978-3-642-40340-8 10.1007/978-3-319-22810-5 10.2307/143141 10.1111/j.1365-3121.1992.tb00605.x 10.1007/978-3-642-03647-7 10.1007/978-3-319-23519-6 10.4135/9781412952422 10.37040/geografie2012117030266 10.1080/01621459.1976.10480949 10.1111/j.1538-4632.1995.tb00338.x 10.1007/978-3-319-22786-3 |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.4000/eps.12307 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Economics |
| DocumentTitleAlternate | Modèle de régression spatiale des ménages: un cas de la République tchèque |
| EISSN | 2104-3752 |
| ExternalDocumentID | oai_doaj_org_article_3ed95692de4f488daa91f96bd6396ae6 10_4000_eps_12307 20.500.13089/ftp3 |
| GroupedDBID | AABPO AAFWJ ADBBV ADJAA AFPKN ALMA_UNASSIGNED_HOLDINGS AXZTB BCNDV E6A FPS GROUPED_DOAJ HVGLF M~E OK1 PAKPZ XN9 ~45 ~V6 AAYXX CITATION |
| ID | FETCH-LOGICAL-c360t-5284b843472dab448c58be2bd2df92c8fec6cf9705e891aeb62f7b812291057a3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000778804900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0755-7809 |
| IngestDate | Fri Oct 03 12:45:59 EDT 2025 Tue Nov 18 21:33:37 EST 2025 Sat Nov 29 07:43:03 EST 2025 Thu Sep 18 15:55:45 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2022/1 |
| Language | English |
| License | CC-BY-NC-ND-4.0 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c360t-5284b843472dab448c58be2bd2df92c8fec6cf9705e891aeb62f7b812291057a3 |
| OpenAccessLink | https://doaj.org/article/3ed95692de4f488daa91f96bd6396ae6 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3ed95692de4f488daa91f96bd6396ae6 crossref_citationtrail_10_4000_eps_12307 crossref_primary_10_4000_eps_12307 cleo_primary_20_500_13089_ftp3 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-01-01 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Espace populations sociétés |
| PublicationYear | 2022 |
| Publisher | Université de Lille Université des Sciences et Technologies de Lille |
| Publisher_xml | – name: Université de Lille – name: Université des Sciences et Technologies de Lille |
| References | ref13 ref12 ref15 ref14 ref11 ref10 ref0 ref2 ref1 ref17 ref16 ref19 ref18 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref8 ref7 ref9 ref4 ref3 ref6 ref5 |
| References_xml | – ident: ref16 doi: 10.1007/978-1-4757-5424-7 – ident: ref1 – ident: ref4 doi: 10.1111/pirs.12318 – ident: ref7 – ident: ref9 doi: 10.1007/978-3-642-40340-8 – ident: ref20 – ident: ref15 doi: 10.1007/978-3-319-22810-5 – ident: ref24 doi: 10.2307/143141 – ident: ref25 – ident: ref27 – ident: ref8 doi: 10.1111/j.1365-3121.1992.tb00605.x – ident: ref10 doi: 10.1007/978-3-642-03647-7 – ident: ref19 – ident: ref17 – ident: ref13 – ident: ref22 doi: 10.1007/978-3-319-23519-6 – ident: ref6 – ident: ref2 doi: 10.4135/9781412952422 – ident: ref3 doi: 10.37040/geografie2012117030266 – ident: ref28 – ident: ref21 – ident: ref23 – ident: ref5 doi: 10.1080/01621459.1976.10480949 – ident: ref26 – ident: ref18 – ident: ref0 doi: 10.1111/j.1538-4632.1995.tb00338.x – ident: ref11 doi: 10.1007/978-3-319-22786-3 – ident: ref12 – ident: ref14 |
| SSID | ssib006287842 ssj0068728 |
| Score | 2.1949947 |
| Snippet | In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as... |
| SourceID | doaj crossref cleo |
| SourceType | Open Website Enrichment Source Index Database Publisher |
| SubjectTerms | Czech Republic household households population and housing census spatial regression |
| Title | Spatial regression model of households: a case of the Czech Republic |
| URI | https://journals.openedition.org/eps/12307 https://doaj.org/article/3ed95692de4f488daa91f96bd6396ae6 |
| Volume | 2022/1 |
| WOSCitedRecordID | wos000778804900003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2104-3752 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0068728 issn: 0755-7809 databaseCode: DOA dateStart: 20040101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2104-3752 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0068728 issn: 0755-7809 databaseCode: M~E dateStart: 20040101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3PS8MwFA4yBL2IP3H-GEE8eKnr0h9JvOnc8KBDRGG30CQvKsg2turBg3-7L2k3JghevJQSQlpev_a9r0m-j5BTB7ngSK0iaR2PUsyheMZ4ZLUGfPdczk140rd8MBDDobxfsvrya8IqeeAqcO0ELJbwkllIHYLNFoXsOJlri6k1LyCIbWPVs0SmApKQCIh0ISSF9xNcVjE_ZhEXsaw0hhC_cRsms_OOXw6NH2W84PhHglrS8Q8Jp79JNupKkV5Wd7hFVmC0TdbmG4lnO-Ta2wkjfOgUnqvVrCMajG3o2NEXZPTgp5ZmF7SgBnOVb8Vqj3Y_wbzQB6gErnfJU7_32L2JalOEyCR5XCJxFKkWaZJyZguN5MpkQgPTllknmREOTG6c5HEGQnYK0BhwrjGNM-ktfYtkjzRG4xHsE4qlE5dcWuHnIrU2mkFHG89SM_CuVU3S8gFRk0r3QrFYZXGYABNSuXKSNMnZPFTK1JLi3tniTSG18MFVGFwVgtskJ4uu8_F-6XTl473o4KWvQwMCQtWAUH8B4uA_Bjkk68zvcwj_Wo5Io5y-wzFZNR_l62zaCljD491X7xspptYN |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Spatial+regression+model+of+households%3A+a+case+of+the+Czech+Republic&rft.jtitle=Espace+populations+soci%C3%A9t%C3%A9s&rft.au=Kraus%2C+Jaroslav&rft.date=2022-01-01&rft.issn=0755-7809&rft.eissn=2104-3752&rft.issue=2022%2F1&rft_id=info:doi/10.4000%2Feps.12307&rft.externalDBID=n%2Fa&rft.externalDocID=10_4000_eps_12307 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0755-7809&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0755-7809&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0755-7809&client=summon |