This page has links and resources from the presentation to the Missouri Social Studies teachers on November 12, 2019,. in Jefferson City, MO.
On this past Wednesday, the Esri Education Team delivered a webinar through Directions Magazine to teachers who use GIS and mapping tools in projects and research. The graphic below is one that we prepared to help illustrate where in the research cycle a particular geotool is most valuable.
These tool recommendations are not absolute; in the hands of an experienced student or educator, there can be a great deal of fluidity. This chart may be considered a starting point.
The latest from Carte Diem Press is a Third Edition of this popular book from Tom and Roger on field studies includes the technology and how to information to implement into many settings. The 3rd Edition includes drones, programming, Raspberry Pi, and updated workflows… and an online companion.
We created this resource for educators who want to do research with learners –typically classroom teachers working with their students in Earth Systems or Environmental Science, Geography, or History. The book would also be useful to those running outdoor education or field research programs for students of all ages; however, as the title suggests, we do focus on tech-enabled methods, tools, and analysis. Most of the content assumes that the research will be conducted outside or “in the field”. We believe it will provide powerful justification to include these projects in your classes. we believe this volume would make a great addition to any reference library on field research techniques.
Order at GISetc. >>
This is the third blog post in a series that uses the Raspberry Pi as an Internet of Things device to collect environmental data (with a DHT22 weather sensor) and post the data in real-time to an ArcGIS Online feature service. The post below is a highly synopsized version of a chapter in the forthcoming third edition of the book, Tech-enabled Field Studies.
Begin by setting up a Raspberry Pi with the DHT22 sensor.. See The Pi Becomes a Data Collector
Use the Pi’s command prompt to make the following installations and configurations:
- pip3 install virtualenv
- virtualenv datalogger
- cd datalogger
- source bin/activate datalogger
- pip3 install Adafruit_DHT
- pip3 install pandas
- pip3 install arcgis – -no-deps
Note: Step 6 above (Pandas) may take several minutes. The above steps create a virtual environment in Python called datalogger and install three libraries into that environment including a streamlined ArcGIS API for Python. When you call the weather script below, you will need to reference it from this environment, described below.
Continue the set-up by:
- Configuring the ArcGIS Online service as described in the previous blog post, A Time-aware Geo Data Bucket.
- Download the sample Python file that reads data from the DHT22 and posts to ArcGIS Online. The Python file is here or download/clone the entire project here. be sure to update the python file with your information, as described in the file.
- Put the Python file in your datalogger directory to help you remember that it must be run from that environment. Once you have added your data to the python script and have the ArcGIS Online service running, you can run the script by using the command:
- /home/pi/datalogger/bin/python3 addwx.py
The service does have a date/time attribute and the service can become time-aware. Once the service is added to a map, you may also choose a symbology that you prefer.
Finally, on the Pi, you may choose to set-up a Cron job to schedule the script execution on a reoccurring basis. Do a quick Google search to find tutorials on how best to do this. Enjoy!
A CSV spreadsheet file can become the basic building block of a Feature Service, capable of holding existing or inbound data. Follow the steps below to create a simple time-enabled GIS feature service bucket for tracking the weather data you provide to it from users or sensors.
1. Log into your ArcGIS Online publisher account.
2. Upload the sample CSV (wx_start.csv):
- Click “Content”.
- Click button, “Add Item”. Select “From My Computer”.
- Select the sample CSV and fill out metadata.
- Click “Publish this file as a hosted layer”.
- Select “Coordinates”.
- Make sure the date field name shows date field type.
- Make sure lat and long field names show latitude and longitude field types
- Set the service to output to your local time zone. (In a later blog post, you will see the python script is set to send UTC time data to the feature class – which is converted to your local on map display).
3. View the feature service Overview page.
- Find the “Layers” section. Click “Time Settings”.
- In the pop-up window, click the checkbox, “Enable Time”. Select the first option, “Specific events in time”. Set pull down to “date”.
4. From the feature service Overview page, click “Open in Map Viewer”.
- In the Change Style panel, make sure button 1 (Attribute to show) is set to “tempF”. Press Done.
- Save your new map.
- Hover over the new feature class layer name. Click the “View Options” button (ellipses). Select “Refresh Interval”. Enable the refresh and set to 1 minute (or whatever number makes sense to you).
- Hover over the new feature class layer name. Click the “Cluster features” button. Enable or disable clustering to your preference. We recommend turning it off, initially.
- Optional: You may also want to modify the transparency of the symbology, available under the “View Options” (ellipses) button. Select “Transparency”. Optionally, set to 50% initially.
- Save your map edits.
- Note: If new data are not showing on the map, click the layer’s “Show Table” button.If all the data is showing in the table, close it. Then click the layer’s “Show Options” (ellipses) button and select “Disable Time Animation”. You can normally turn time animation back on and the data will appear.