Format options
Before you upload your own data into mySidewalk, it’s important to make sure your file is clean, clear, and well-structured. Formatting your data correctly upfront will help the platform interpret it accurately, speed up your upload process, and unlock better insights.
To help you out, we’ve created a two-part series that walks through the most common formats:
Option 1: Formatting Files with Geographic Information
For data tied to multiple places like ZIP codes, counties, or cities.
This format is appropriate in most cases.
Option 2: Formatting Files without Geographic Information
Files might not include geographic information within the file itself if:
they're super simple, only containing a few rows, and/or
all the data within is for a single location.
Both guides include examples, tips for cleaning up your data, and even prompts you can copy/paste into AI (like ChatGPT or Claude) to help with formatting.
Want to skip some of the prep work?
Our Smart Upload Formatting Guide shows how you can upload your data using our newest AI-supported feature—which accepts more flexible formats and helps you clean and map your data during the upload process.
Accepted file types
In addition to format, you should also select a filetype to upload. CSV is what we recommend.
mySidewalk supports uploading CSV, GeoJSON, SHP, KML, GML, and GDB. But CSVs are what allow you to take full advantage of mySidewalk's features.
Why use CSV? Uploading as a CSV enables you to match the places in your file with the places in our library. That means you can:
Create bivariate maps with your data and mySidewalk data
Create correlations with your data and mySidewalk data
Normalize your data by mySidewalk data like total population, area, and more.
Option 1: Files with Geographic Information
Let’s walk through how to prepare your file so that each row represents a place (like a county), and each column contains a specific variable and time period. You’ll also learn how to ask an AI assistant to help clean things up.
Proper formatting leads to smoother uploads, accurate maps, and clearer insights—getting it right from the start saves time and ensures better results.
🧭 Step 1: One row = one place
Each row in your file should represent a single location (ZIP, city, county, etc.).
Example:
County Name | … |
Jackson County |
|
Clay County |
|
Hint: For files that contain data that all represents one location, you can use this format as long as you keep to the one row = one place rule. But you might find it simpler to use the format for files without geographic information. Do what works best for you.
📅 Step 2: Time belongs in the column name
Don't use a separate “Year” column. Instead, include the year (or other time period) in the column name.
County Name | Grants_2020 | Grants_2021 | Payroll_2021 |
Jackson County | 3,000,000 | 3,100,000 | 2,400,000 |
Remember to use consistent time formatting. Examples:
Grants_2020
Grants_Jan_2021
Grants_2021_q2
👥 Step 3: Include breakdowns as separate columns
If you have subcategories (e.g., executive vs. non-executive payroll), use clear, separate column names:
County Name | Payroll_Total_2021 | Payroll_Exec_2021 | Payroll_NonExec_2021 |
Jackson County | 2400000 | 900000 | 1500000 |
🧹 Step 4: Clean before upload
✅ Do:
Only one header row
Fill empty cells with “0” or “null”
Remove symbols like $, %, and commas
🚫 Don’t:
Put “Year” in its own column
Include extra header rows or totals as separate rows
🤖 Step 5: Ask AI to help you reformat (optional)
If your file is messy or structured differently, you can ask an AI to reformat it.
Try something like:
“I have a spreadsheet where each row is a county, and columns are ‘2020 Grants’, ‘2021 Grants’, ‘2021 Payroll’. Can you help me rename the columns to use underscores, like ‘Grants_2020’, and remove % or $ symbols?”
Or:
“My file has rows for each combination of county and year, with time in a column. Can you pivot it so each column becomes a variable like ‘Grants_2020’?”
🧪 Bringing it All Together: A Final Example
County | Grants_2020 | Payroll_Total_2021 | Payroll_Exec_2021 | Payroll_NonExec_2021 |
Jackson County | 3000000 | 2400000 | 900000 | 1500000 |
Now you’re ready to upload!
Option 2: Files without Geographic Information
If your data isn’t tied to multiple geographic areas—but still includes multiple variables and time periods—you should use a wide format.
In this structure, each row represents one subject (like an organization or summary report), and each column represents a specific variable + time combination.
To help interpret your data more easily, include a “Variable Label” column as the first column. This column gives a short, human-friendly name or description of the dataset or subject.
🧭 Step 1: One row = one subject or dataset
Each row represents a single dataset summary or reporting entity. The “Variable Label” column gives a descriptive label for the row.
Example
Variable Label | Grants | Payroll |
Summary Totals | 2800000 | 2200000 |
📅 Step 2: Time belongs in the column name (optional)
If your data file contains a time element, each column header should include both the variable name and time period.
Example
Variable Label | Grants_2019 | Grants_2020 |
Summary Totals | 2800000 | 3000000 |
➕ Step 3: Add more variables and breakdowns
Each variable and time combo gets its own column. You can expand this structure by adding other variable types (like Payroll), and use consistent naming.
Example
Variable Label | Grants_2019 | Grants_2020 | Payroll_2019 | Payroll_2020 |
Summary Totals | 2800000 | 3000000 | 2200000 | 2400000 |
Example with Breakdowns
Variable Label | Grants_2020 | Payroll_Total_2020 | Payroll_Exec_2020 | Payroll_NonExec_2020 |
Summary Totals | 3000000 | 2400000 | 900000 | 1500000 |
🧹 Step 4: Clean before upload
✅ Do:
Use a Variable Label column to provide context while you upload
Use one row per subject/dataset
Fill blanks with 0, null, or none
Remove formatting like commas, $, %
🚫 Don’t:
Use separate rows for each variable or time period
Put the year in a separate column
Mix naming styles (e.g. Pay)Exec_2020 vs. Payroll_Exec_2020)
🤖 Step 5: Ask AI to Help You Reformat
If your file is in long format (e.g. one row per variable per year), you can ask the AI assistant to help restructure:
“Can you pivot this long-format table into a wide format with one row per dataset, one column per variable-year, and include a variable label at the beginning of each row?”
Now your file is fully prepped for upload into mySidewalk!
❓ Need Additional Help?
Download the Upload Template below to view examples
Click “I need help” or chat with us (click Support in the main menu, then Chat with us)