Data Visualization and Best Practices Webinar

Overview Environmental Data Sources and Considerations  SDWIS, radon labs, local health departments, for example  Working with data stewards, collaboration, data sharing, defining useful fields  Make sure your data has a geographic element that can be mapped! Data Processing / Making data useful  Access and Use of EPHT guidance documents and white papers: ◦ Drinking water How-to Guide and other background docs (numerous) ◦ Geo-team Visualization Guidance Team documents ◦ Radon Task Force White Paper ◦ Et cetera  Cleaning data, de-duplicating, and geocoding ◦ All of these activities aren’t always required, but are considerations. Data Input into ArcGIS  Adding data to GIS  Aggregating data from point or zip code level to census tract or county level  Visualizing (symbolizing) & displaying your data in maps

Data Sharing Most of our data, CDPHE, is internal because of the departments of health and environment being housed in one agency. Because of this we often do not need a data sharing agreement (DSA). There can be sensitivities around the data that need to be understood and typically are entered into verbally. Your agency may require, or be required to enter into a DSA. Even if not be sure to understand the data and any sensitivities, security, privacy, etc.

Data Sharing However, not all of our data is internal. We receive data from radon labs, typically private, and water quality labs that test well water, typically public. Have not been able to enter into any agreement with private labs over well water. We have developed a DSA for sharing environmental data sets and hope to utilize that to foster data sharing with private labs that do well water testing in the future.

Regardless of whether the raw data is point level, zip code or county level it is almost always processed in Excel, Access or SAS to create summary measures indicated by the Tracking guidance docs. There are often a number of ways to process the data sets and what works best for you and your data will depend primarily on staff expertise and software available to your program-although Tracking does aim to promote consistency not only in measures, but in data processing as well. When dealing specifically with EPHT NCDMs, there are guidance docs available that indicate what measures should be developed and how they will be visualized. In Colorado we utilize GIS early in data processing to attribute county names and FIPS codes to point or zip code level data – since most measures need to be at the county level-however summary calculations occur outside of GIS. Proper format of the raw data is important to get it to import to ArcGIS to perform joins or display XY data ◦There is no magic bullet on this, mostly from experience with data and ArcGIS software. ◦The biggest things that will cause problems are special characters and length of field names (less than 10 characters typically) and data type (field properties type). Data Processing

The data we receive typically has lat/long coordinates already. But, sometimes we are given only zip code level data which we often aggregate to county level. Or, sometimes we receive street addresses which we will geocode and then geomask in some way to preserve privacy of a homeowner, for example. Geomasking tool was developed by the NYS members of the Drinking Water content work group and is available at: Geoprocessing Geoprocessing

After receiving zip code level data it has to be converted to county level data. This can be done two ways using Access or ArcGIS: ◦Using Access to query zip code level data against a county zip codes list.  You can create the county:zip code data set using Intersect in ArcToolbox  This returns some entries where boundaries simply touch rather than intersect, so you will need to exclude some data to prepare the final county: zip code list. ◦Spatial join in ArcGIS: zipcode layer to county layer if the degree to which your zips cross county boundaries is limited or otherwise negligible. This is a tricky process (recap): ◦Some zip codes will cross county boundaries. ◦You will need to determine the best way to handle this. ◦We created a list of all zips within each county and queried them in Access against our raw data. ◦All the data processing is done in Excel, Access or SAS for us. ◦Geoprocessing is done in GIS Once all the zip data has a county/FIPS tied to it then we calculate summary county level measures (mean, max, 95 th percentile, etc.) Add the summary data set back into your map (mxd) Join to the attribute table of the county layer on FIPS, County, etc. Your field name maybe different. Symbolize Geoprocessing: Zip level data

Geoprocessing: Point level data Point level data will need to get processed similarly to get county level measures. Add data to ArcMap Display XY data Right click on your Excel data in the List By Source Table of Contents Make sure to set a geographic coordinate system (NAD 1983 or WGS 1984) using the “Edit” button in the “Display XY Data” Window Then export your “Events” to a shapefile or geodatabase so you retain a permanent spatial data set that you can work with. Do this from List By Drawing Order in your Table of Contents, right click Events layer, Data, Export Data. After exporting, you’ll be asked to add the layer to the map. Do this and then Spatial Join the point layer to a county layer to get a county / FIPS code tied to your points. Now export that attribute table (as a.dbf) so you can open in Excel / Access / SAS to process your summary data. After processing summary data add summary data file with FIPS code back to your map (mxd) then join to the attribute table of the county layer ***based on FIPS code*** Joining on a name or other text field can be tricky. The FIPS code is typically a string format Symbolize

To export that data with our county specific information back out as a tabular data set (like and Excel file,.dbf): Open the attribute table of the layer then click Table Options in top right, then Export. Make sure to specify your destination folder. This file can be opened in Excel, formatted and then processed in either Excel, Access or SAS (the.dbf file has some weird properties that you will want to format-away immediately in Excel, column width for example) Once the county level summary data is processed we then import it back into the GIS software to visualize. Note: Exporting data sets from GIS

ArcGIS Demo Demonstration of procedures outlined in previous slides: Adding data Displaying XY data (plotting points) Intersecting to shapefiles / features Spatial Join Attribute Join Exporting data (tabular and spatial) Geodatabase Any questions before we start the demo?

Conclusions Data availability and quality Make sure to engage the data stewards, understand the data and sensitivities of the data. Utilizing the guidance and resources developed by the various Environmental Public Health Tracking work groups There has been a lot of effort and experience put into developing measures and guidance docs. Use these resources to analyze and display your data in a consistent way if possible. Data processing and visualizing your data geographically Data sets commonly were not created or maintained with geographic analysis or display in mind, so some additional processing will be required in most cases. This often requires use of tabular data storage and processing software in conjunction with GIS software.

Contact Info Eric Brown CDPHE Environmental Data Coordinator Colorado Environmental Public Health Tracking