Utilize imagery and crowdsourced data on spatial employment modelling
Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java rec...
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| Format: | Article |
| Language: | English |
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Pendidikan Matematika, UIN Raden Intan Lampung
2024-12-01
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| Series: | Al-Jabar |
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| Online Access: | https://ejournal.radenintan.ac.id/index.php/al-jabar/article/view/24518 |
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| author | Novi Hidayat Pusponegoro Ro'fah Nur Rachmawati Maria A. Hasiholan Siallagan Ditto Satrio Wicaksono |
| author_facet | Novi Hidayat Pusponegoro Ro'fah Nur Rachmawati Maria A. Hasiholan Siallagan Ditto Satrio Wicaksono |
| author_sort | Novi Hidayat Pusponegoro |
| collection | DOAJ |
| description | Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach.
Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island.
Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data.
Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities.
Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks. |
| format | Article |
| id | doaj-art-06a5274849b4410880c8e7cc2d005e34 |
| institution | Kabale University |
| issn | 2086-5872 2540-7562 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Pendidikan Matematika, UIN Raden Intan Lampung |
| record_format | Article |
| series | Al-Jabar |
| spelling | doaj-art-06a5274849b4410880c8e7cc2d005e342025-01-14T22:20:48ZengPendidikan Matematika, UIN Raden Intan LampungAl-Jabar2086-58722540-75622024-12-0115259361010.24042/ajpm.v15i2.245186891Utilize imagery and crowdsourced data on spatial employment modellingNovi Hidayat Pusponegoro0Ro'fah Nur Rachmawati1Maria A. Hasiholan Siallagan2Ditto Satrio Wicaksono3Department of Statistics, Politeknik Statistika STIS(Scopus ID: 55364175100) Indonesia Defense UniversityDepartment of Statistics, Politeknik Statistika STISDepartment of Statistics, Politeknik Statistika STISBackground: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach. Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island. Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data. Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities. Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks.https://ejournal.radenintan.ac.id/index.php/al-jabar/article/view/24518spatialgeographically weighted regression (gwr)crowdsourced dataimagery dataemployment |
| spellingShingle | Novi Hidayat Pusponegoro Ro'fah Nur Rachmawati Maria A. Hasiholan Siallagan Ditto Satrio Wicaksono Utilize imagery and crowdsourced data on spatial employment modelling Al-Jabar spatial geographically weighted regression (gwr) crowdsourced data imagery data employment |
| title | Utilize imagery and crowdsourced data on spatial employment modelling |
| title_full | Utilize imagery and crowdsourced data on spatial employment modelling |
| title_fullStr | Utilize imagery and crowdsourced data on spatial employment modelling |
| title_full_unstemmed | Utilize imagery and crowdsourced data on spatial employment modelling |
| title_short | Utilize imagery and crowdsourced data on spatial employment modelling |
| title_sort | utilize imagery and crowdsourced data on spatial employment modelling |
| topic | spatial geographically weighted regression (gwr) crowdsourced data imagery data employment |
| url | https://ejournal.radenintan.ac.id/index.php/al-jabar/article/view/24518 |
| work_keys_str_mv | AT novihidayatpusponegoro utilizeimageryandcrowdsourceddataonspatialemploymentmodelling AT rofahnurrachmawati utilizeimageryandcrowdsourceddataonspatialemploymentmodelling AT mariaahasiholansiallagan utilizeimageryandcrowdsourceddataonspatialemploymentmodelling AT dittosatriowicaksono utilizeimageryandcrowdsourceddataonspatialemploymentmodelling |