Data Science

by Mingfeng Xue. Hierarchical Linear Modeling (HLM) is an extension of linear models, which offers an approach to analyzing data structures with nested levels. This blog elucidates HLM's significance over traditional linear regression models, particularly in handling clustered data and multilevel predictors. Illustrated with an example from educational research, the blog demonstrates model implementation and interpretation steps. It showcases how HLM accommodates both independent variables from different levels and hierarchical structure data, providing insights into their impacts on the outcome variable. Recommended resources further aid readers in mastering HLM techniques.

by Leah Lee. Large-scale geographic data over time on insect diversity can be used to answer important questions in Entomology. Open-source, open-access citizen science platforms like iNaturalist generate huge amounts of data on species diversity and distribution at accelerating rates. However, unstructured citizen science data contain inherent biases and need to be used with care. One of the efforts to validate big data from iNaturalist is to cross-check with systematically collected data, such as museum specimens.

by Leïla Njee Bugha. Among its many uses, remote sensing can prove especially useful to document changes and trends from eras or settings, where traditional sources are either inexistent or infrequently collected. This is the case when one wants to study urban expansion in sub-Saharan countries over the past 20 years. To further remedy the lack of data on land cover uses from earlier time periods, classification methods can be used as well. Using easily accessible satellite imagery from Google Earth Engine, I provide here an example combining remote sensing with classification to detect changes in the land cover in Nigeria since 2000 due to urban expansion.