Posted on December 30, 2022 (Updated on July 20, 2025)

Standardizing data over time (not space) in ArcGIS Pro 10.8?

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Standardizing Data Over Time in ArcGIS Pro 10.8: Digging Deeper

Ever felt like you’re comparing apples and oranges when analyzing data that changes over time? I know I have! That’s where standardizing your data comes in, and ArcGIS Pro 10.8 has some pretty neat tricks up its sleeve to help you do just that. This isn’t just about making your charts look prettier; it’s about unlocking real insights hidden within your temporal datasets. Let’s dive in.

Temporal Data: What’s the Big Deal?

Temporal data, in essence, is data that has a “when” component. Think about it: land-use changes over the decades, rainfall amounts tracked daily, or even crime incidents logged by the hour. ArcGIS Pro is built to handle this kind of information, letting you visualize and analyze how things evolve. You can set up layers to recognize which fields hold the time information, and then use the time slider to watch the data unfold. It’s like having a time machine for your GIS!

Why Bother Standardizing?

So, why go through the trouble of standardizing data over time? Well, imagine trying to compare this month’s sales figures to last year’s if your business doubled in size. The raw numbers wouldn’t tell you much, would they? Standardizing levels the playing field. It lets you compare values across different time periods, even if the overall scale of the data is different. This makes it easier to spot trends that might otherwise be hidden by those variations. Plus, many statistical analyses work best when your data is on a similar scale, so standardizing can improve the accuracy of your results.

Getting Down to Business: Standardization Methods in ArcGIS Pro 10.8

Okay, so ArcGIS Pro doesn’t have a magic “standardize over time” button (wouldn’t that be nice?), but you can achieve the same thing by creatively combining different tools. Here’s how:

  • The Standardize Field Tool: Your Swiss Army Knife

    • This tool is a lifesaver. It lets you convert field values to a common scale using methods like Z-Score, Min-Max, Absolute Maximum, and Robust standardization. Let’s break those down:
      • Z-Score: This tells you how many standard deviations a value is away from the average. It’s great for seeing how unusual a particular value is compared to the rest of the data.
      • Min-Max: This scales your values to a range you define, like 0 to 1. I often use this when I want to put everything on a percentage scale.
      • Absolute Maximum: This divides each value by the largest absolute value in the field. It’s useful when you want to emphasize the relative size of values compared to the largest one.
      • Robust Standardization: This is your go-to when you have outliers messing things up. It uses the median and interquartile range, which are less sensitive to extreme values.
    • To use this for temporal data, make sure your table has each time period as a row, and then standardize the field you’re interested in.

  • Field Calculator: For the DIY Enthusiast

    • Want total control? The Field Calculator is your friend. You can write your own formulas to standardize the data exactly how you want.
    • For example, that 0-1 scaling I mentioned earlier? The formula is simple: (value – minimum) / (maximum – minimum). Just make sure you have the minimum and maximum values for each time period handy. You can calculate those using the Summary Statistics tool.

  • Space Time Cubes and Hot Spot Analysis: Finding Emerging Trends

    • If you’re working with point data (like crime incidents or disease outbreaks), this is where things get really interesting. The “Create Space Time Cube By Aggregating Points” tool turns your data into a 3D cube, where two dimensions are space and the third is time.
    • Then, the “Emerging Hot Spot Analysis” tool identifies trends in how those points cluster over time. It can tell you if a hot spot is new, intensifying, or even disappearing. I’ve used this to track the spread of invasive species, and it’s incredibly powerful.

  • Time Series Forecasting: Peering into the Future

    • Want to predict what’s going to happen next? ArcGIS Pro has a whole set of Time Series Forecasting tools. These use your space-time cube to predict future values based on current patterns.
    • You’ve got options here: Curve Fit Forecast (which fits curves to your data), Exponential Smoothing Forecast (which breaks down your data into seasonal and trend components), and Forest-based Forecast (which uses a machine learning algorithm).

  • Temporal Profile Charts: Visualizing Change

    • These charts are great for visualizing how things change over time in a multidimensional raster or mosaic dataset. You can compare different locations, variables, and spectral bands to see how they’re trending.
    • Plus, you can aggregate the data to different time intervals (like daily, monthly, or yearly) and add trend lines to make the patterns even clearer.

    • Step-by-Step: Standardizing Sales Data with Field Calculator

    Let’s walk through a quick example. Say you’ve got monthly sales data for different product categories, and you want to compare their performance over time.

  • Calculate Min and Max: Use the Summary Statistics tool to find the minimum and maximum sales for each category. Group the statistics by product category.
  • Join the Stats: Join the output table from the Summary Statistics tool back to your original sales data table, using the product category as the link.
  • Do the Math: Open the Field Calculator for a new field called “StandardizedSales.” Use the formula (Sales – Minimum) / (Maximum – Minimum), but replace “Sales,” “Minimum,” and “Maximum” with the actual names of your fields.
  • Visualize: Create a chart or map using the “StandardizedSales” field to see how the different product categories stack up against each other over time.

    • Pro Tips for Temporal Data

    • Date Fields are Your Friend: Always store your time stamps in a date field. It makes everything faster and easier.
    • Rows, Rows, Rows: Store your temporal data in a row format. Trust me on this one.
    • Index Those Fields: Indexing fields containing time values will speed up your queries and visualizations.
    • Stay Consistent with Time Zones: Use the same time zone for all your data to avoid headaches.
    • Handle Missing Data: Use null values for missing data, and consider using the “Fill Missing Values” tool before creating a space-time cube.
    • Convert Strings and Numbers: If your time values are stored as text or numbers, convert them to a date field using the “Convert Time Field” tool.

    Final Thoughts

    Standardizing data over time in ArcGIS Pro 10.8 might seem a bit daunting at first, but with a little practice, it becomes second nature. By using the right tools and following these best practices, you can unlock a whole new level of insight from your temporal data. So go ahead, dig in, and see what you can discover!

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