Modernizing Data Systems in Environmental Public Health: A Blueprint for Action
Data Processing and Analysis Tools Data frequently requires transformation, cleaning, and analysis before they can be used for decision-making or reporting. This process, known as extract, trans- form, load (ETL), is essential to making raw information usable and reliable. ETL is a data processing workflow that transfers information from source systems to destination systems while preparing it for analysis. It includes: Extract: Data are pulled from sources such as databases, spreadsheets, or appli - cation programming interfaces (APIs). Transform: Data are cleaned and standardized, duplicates are removed, incon - sistencies are corrected, rules are applied, and information is organized into us - able formats. Load: The processed data are placed into their destinations, such as dashboards, databases, or cloud warehouses. In EPH, an ETL workflow might extract inspection data from an EHMS, trans- form the data by standardizing violation codes and dates, and load the data into a dashboard for field staff or community reporting. Automating these steps minimizes manual data entry, reduces errors, and allows for faster, more consistent analysis. Python is especially valuable for automating ETL workflows. It is widely used in EPH to script repeatable processes for data cleaning, validation, and transformation. Python’s extensive ecosystem of libraries—including pandas for tabular data, geopandas for spatial operations, and Arcpy for GIS automation— enables teams to integrate and prepare data at scale. Python allows agencies to streamline routine data preparation tasks and dedicate more time to analysis and decision-making. EPH professionals use a variety of tools to perform ETL operations (Table 10). These tools are often used in concert: data might be queried with SQL, trans- formed in Python, and then visualized in Power BI, Tableau, or published to ArcGIS Online. The development of cross-functional fluency in these tools is essential for modern environmental health teams.
Table 10
PLATFORM DESCRIPTION
BEST USES
Python
Open-source programming language ideal for automation, analysis, and integration with APIs. Standard libraries include pandas, geopandas, and Arcpy. Standard language for querying relational databases. Allows precise extraction and organization of structured records.
Automated workflows, spatial analysis, and custom data processing Database queries, reporting pathways, and data integration Quick data exploration, basic reporting, and formatting tasks Advanced analytics, public health research, and data modeling Institutional analytics, regulatory reporting, and secure datasets
SQL
Excel
Widely used spreadsheet tool for entry-level analysis, formulas, pivot tables, and charting.
R
Statistical programming language used heavily in epidemiology and research. Offers powerful modeling and visualization capabilities. Commercial suite for analytics, modeling, and secure data handling. Often used in large public health agencies.
SAS
18
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