Understanding Fertility Trends of Assam: A District-Level Spatial Analysis Through K-Means Clustering

Background: Understanding regional fertility patterns is crucial for effective demographic planning and policy formulation. This study estimates the district-level Age-Specific Fertility Rate (ASFR) for Assam and employs cluster analysis to classify the districts of Assam based on their current fertility patterns. The cluster analysis identifies distinct groups with similar fertility characteristics, providing insights into regional variations and demographic transitions across the region. The findings highlight significant heterogeneity in fertility levels within Assam, reflecting diverse socio-economic and cultural influences. Methods: The K-means clustering technique has been used to group the districts of Assam into distinct clusters. Results: It was found that based on the calculated single-year age-specific fertility rates, the districts of Assam can be divided into two distinct and non-overlapping clusters. A further comparison of some socio-demographic factors between the two clusters revealed that Cluster 1 (Low Fertility Zone) had higher education levels and a greater proportion of Assamese-speaking women compared to Cluster 2 (High Fertility Zone). Conclusion: The districts of Assam, India, can be divided into two distinct groups with significantly different fertility patterns and demographics of the mothers. These findings can guide targeted reproductive health interventions in high-fertility districts.

Authors

Institutions

Publication Details

Journal
National Journal of Community Medicine
Published
2025-09-01
DOI
https://doi.org/10.55489/njcm.160920255794
Primary Topic
South Asian Studies and Conflicts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding Fertility Trends of Assam: A District-Level Spatial Analysis Through K-Means Clustering

Saurav Sarma, Ruma Talukdar, Chayanika Baruah
National Journal of Community Medicine
South Asian Studies and Conflicts
article

Understanding Fertility Trends of Assam: A District-Level Spatial Analysis Through K-Means Clustering

Saurav Sarma, Ruma Talukdar, Chayanika Baruah
article en

Abstract

Background: Understanding regional fertility patterns is crucial for effective demographic planning and policy formulation. This study estimates the district-level Age-Specific Fertility Rate (ASFR) for Assam and employs cluster analysis to classify the districts of Assam based on their current fertility patterns. The cluster analysis identifies distinct groups with similar fertility characteristics, providing insights into regional variations and demographic transitions across the region. The findings highlight significant heterogeneity in fertility levels within Assam, reflecting diverse socio-economic and cultural influences. Methods: The K-means clustering technique has been used to group the districts of Assam into distinct clusters. Results: It was found that based on the calculated single-year age-specific fertility rates, the districts of Assam can be divided into two distinct and non-overlapping clusters. A further comparison of some socio-demographic factors between the two clusters revealed that Cluster 1 (Low Fertility Zone) had higher education levels and a greater proportion of Assamese-speaking women compared to Cluster 2 (High Fertility Zone). Conclusion: The districts of Assam, India, can be divided into two distinct groups with significantly different fertility patterns and demographics of the mothers. These findings can guide targeted reproductive health interventions in high-fertility districts.

National Journal of Community MedicineVol. 16(09)
Cotton University (IN)
Openalex Percentile: Top 73%
South Asian Studies and Conflicts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Understanding Fertility Trends of Assam: A District-Level Spatial Analysis Through K-Means Clustering — Saurav Sarma, Ruma Talukdar, et al. · National Journal of Community Medicine (2025) | TGRS Research Map | TGRS