Pengaruh Kawasan Berorientasi Transit terhadap Gentrifikasi Sosial Ekonomi: Studi Kasus Kawasan Stasiun Manggarai
Bibliographic record
Abstract
Abstract. Transit-oriented development (TOD) integrates transportation and land use with high density and mixed functions. However, the development of these areas has the potential to trigger gentrification, as happened in Manggarai, Jakarta, where land prices increased after TOD development. This study aims to analyze the influence of TOD elements on socioeconomic gentrification around Manggarai Station. The method used is multivariate regression analysis based on observations, documentation, and literature from government agencies. The results show that land use area influences population size, particularly through the conversion of vacant land into residential or commercial use. However, socioeconomic variables such as education level, occupation type, income, and land prices are not significantly affected by land area, commercial density, or pedestrian length. Conversely, distance from the station significantly affects population size, education level, and land prices. Areas closer to the station are more desirable due to transportation accessibility, although this does not have a major impact on inbound migration or income. These findings emphasize the importance of policies that consider the socioeconomic impacts of TOD development. Abstrak. Kawasan berorientasi transit (TOD) mengintegrasikan transportasi dan penggunaan lahan dengan karakteristik kepadatan tinggi dan fungsi campuran. Namun, pengembangan kawasan ini berpotensi memicu gentrifikasi, seperti yang terjadi di Manggarai, Jakarta, di mana harga lahan meningkat pasca-pengembangan TOD. Penelitian ini bertujuan menganalisis pengaruh elemen-elemen TOD terhadap gentrifikasi sosial ekonomi di sekitar Stasiun Manggarai. Metode yang digunakan adalah analisis regresi multivariat berdasarkan observasi, dokumentasi, dan literatur dari instansi pemerintah. Hasil menunjukkan bahwa luas penggunaan lahan berpengaruh terhadap jumlah penduduk, terutama melalui alih fungsi lahan kosong menjadi perumahan atau perkantoran. Namun, variabel sosial ekonomi seperti tingkat pendidikan, jenis pekerjaan, pendapatan, dan harga lahan tidak terpengaruh secara signifikan oleh luas lahan, kepadatan komersial, maupun panjang pedestrian. Sebaliknya, jarak dari stasiun berpengaruh signifikan terhadap jumlah penduduk, tingkat pendidikan, dan harga lahan. Area yang lebih dekat ke stasiun lebih diminati karena aksesibilitas transportasi, meskipun tidak berdampak besar pada migrasi masuk atau pendapatan. Temuan ini menekankan pentingnya kebijakan yang memperhatikan dampak sosial ekonomi dari pengembangan TOD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".