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Better Data Quality for Better Water Equity

Every year, the State of California gathers data about water usage and production from all of its public water systems via Electronics Annual Reports (EARs). For instance, consider Dataset #8 in the Recommended Challenge Datasets: this dataset lists information taken from the EAR for each public water system (PWS) for each month of the year, including water production/delivery, water quality, and rates. In principle, this dataset is a treasure-trove of knowledge for government employees seeking to study, optimize, and understand water distribution in California.

Despite the effort expended to gather this data, it rarely gets used in analyses. Even though the dataset contains valuable insight about water use, its clunky format makes even simple analysis questions (e.g. how does water production change in a particular PWS over time?) difficult to ask. Furthermore, the low data quality (e.g. missing fields) provides other obstacles.

This repository includes Jupyter Python notebooks to address both the format and quality problems in the EAR dataset. We intend the users of this repository to be state and local government employees (or perhaps simply interested citizens) who wish to understand how, when, and where water is used in California.

Data Transformations

When addressing the problem of the EAR data format, its tempting to take the following simple approach: design the ideal data format, and then transform all of the original EAR raw data permanently into this new and shiny layout. One must recognize however, that the "ideal" data format depends on the questions being asked of the data. In other words, what is important to the user? Water delivery? Water production? What types of water systems (residential, industrial, agricultural, etc.) use the most water, and when?

So, we instead took the approach of writing different data transformation scripts for different output formats. Each script takes as input the raw Excel data, and outputs a combination of rearranged data and calculations into a new format as a csv file. The two output data formats we chose (along with corresponding Jupyter notebook and dataset number from the Recommended Challenge Datasets) were:

1) Water Produced (Dataset #5): EAR_to_WaterProduced.ipynb

This dataset format describes water production for each PWS including the amounts of water produced from groundwater, surface water, water exchanged between public water systems, and recycled water. The data is tabulated for each month of the year, and the total water production (in units of gallons per capita day) is calculated for each month and each PWS.

2) Water Delivered (Dataset #6): EAR_to_WaterDelivered.ipynb

In this format, water deliveries are tabulated for a variety of destination categories (e.g. single-family residential, multi-family residential, industrial, agricultural, etc). The data is tabulated for each month of the year, and the total residential water delivery is calculated in units of gallons per capita day. This notebook loads Dataset #6 in order to get the names of the columns to make sure our output matches the desired one: all of the actual data in the notebook output comes from the manipulated raw data.

Data Hygiene

In addition to transforming the data into a more useful format, there is an included script (dataInspection.ipynb) which converts the original data set into a single dataframe and creates a dictionary that tags each column with keys that identify string and/or number quality. These tags include capitalization errors, consistency in nonalphanumeric notation, misspellings, variance in numerical digits, etc. These tags be used to model a data cleaning algorithm to take steps toward improving dataset quailty. Future work would include making a histogram of tags (a qualitative index will need to be made as some tags return arrays and others numbers) to visualize data quality. A qualitative view of the tags would assist in identifying high error columns/fields, a potentially useful metric for updating the way in the which the EARs are submitted.

How-to

The following libraries are required to run these scripts:

  • Python3

  • Jupyter

  • pandas

  • NumPy

The easiest way to obtain all of these requirements is via the Anaconda Distribution, a popular and flexible Python data science platform.

To run the codes, simply start a Jupyter Notebook server in the main directory by typing

jupyter notebook

Then, you can run each of the three main notebooks (EAR_to_WaterProduced.ipynb and EAR_to_WaterDelivered.ipynb for transforming the data, and dataInspection.ipynb for investigating data errors) individually according to standard Jupyter Notebook usage. There is also an example workflow notebook that shows how to load the raw data, and a few different ways to play with it. Results from the data transformation notebooks are by default written to the outputData folder.

For the dataInspection notebook, one more package is required (to do the spellchecking of data entries). This can be easily installed with pip, a package manager installed with the Anaconda Distribution:

pip install pyenchant