Latest Contributions by LaurenGriffin

I haven't converted tiger files in forever, but I can tell you a good strategy for getting 2000 Census Data for the State of Louisiana. Go to the ArcGIS Living Atlas of the world and search for 2000 Census Data. A number of layers will come up. I got Population Change, for example; it has State, County, Tract, and Block Group geometry. Once you have your geometry, you can join it to the census data you need (the layer I got has several 2000 fields). You can also use the Enrich tool to get other 2000 census data fields including these:  The Enrich tool does use credits, but they are very affordable (especially compared to the time it takes to find, clean, and join the data from other sources). Also, a trial version of the software comes with some free credits. I hope this helps! Best wishes! ... View more

Almost exactly right! Humor me for a minute, I'm going to backtrack just a bit. Hot Spot Analysis and Emerging Hot Spot Analysis both run the Gi* Statistic under the hood. Conceptually, the Gi* statistic works by computing the mean value for a feature and its neighbors, and comparing that local mean to the global mean (the mean value for ALL features in the dataset). Then, taking the number of features and variance into account, it decides if the local mean is different enough from the global mean to be a statistically significant hot or cold spot. For Emerging Hot Spot Analysis, neighbors can be defined to include temporal AND spatial neighbors, as you know. Let's consider an easy one: you define neighbors to be the 8 closest spatial neighbors and 1 time step. The local neighborhood would be 9 features from the same time step (the feature itself plus the 8 nearest neighboring features) and 9 features for the preceding time step, for a total of 18 neighbors. If you choose 2 for the Neighborhood Time Step parameter, there will be a total of 27 features in the neighborhood for each feature in the dataset. Note: the Gi* analysis is performed for every feature at every time step. Now on to your question: If you choose Entire Cube to decide if the local feature and its neighbors at a particular location in the cube is a statistically significant hot or cold spot, the tool will compare the local mean to the mean for all features in the cube. You did understand that one well! 🙂 If you choose Neighborhood Time Step (let's assume you typed 2 for Neighborhood Time Step), the tool will compare a feature and its 27 neighbors to the mean for:  all features in the same time step along with all features in the preceding 2 time steps. This is a good option if you have strong trends in the data (like with Covid, fewer cases at the beginning, more at the end of the time period you're analyzing). So that's the one you didn't quite get right. The number of time steps used to compute the Neighborhood ("global") mean will match whatever you typed for the Neighborhood Time Step parameter (it wouldn't necessarily be TWO). Last is the Individual Time Step. This option is like taking a snap shot at each time step to compute the individual ("global") mean. The mean for each feature and its 27 neighbors (assuming you typed 2 for the Neighborhood Time Step parameter) will be compared to the mean for all features in the same time step. There is only one time step used to compute the "global" mean and it's based on all features in the current time step (the time step matching the feature being analyzed). This is often a good option if you are making decisions about how to react today (assuming the final time step is today) to address the emerging trends. I hope this is clearer. If not, please ask again. Best wishes,  Lauren ... View more

What version of the software are you using? (It shouldn't matter, but I can try to reproduce what you're seeing). This is what I'm seeing: In the Geoprocessing pane, I select Toolboxes. In The Spatial Statistics toolbox under Mapping Clusters, I right click on the Cluster and Outlier Analysis tool, select Edit, and up pops the script. Another way to find it is to navigate to: <your ArcGIS folder, where ever you installed the software... like maybe in ProgramFiles\ArcGIS> then navigate to: \Pro\Resources\ArcToolBox\Scripts and view the LocalMorans.py python script file. For example, you should be able to right click that file and choose open with <Notepad or Wordpad, for example> I hope this works for you! Best wishes, Lauren   ... View more

Sure. Please contact me at LGriffin@esri.com and we can chat about it. If we figure out anything that may be helpful to others, I’ll ask you to please post what we learn to the community 😊 Lauren ... View more

Only because this has happened to me, make sure your Location ID fields are numeric (rather than text fields). If that's not the issue and you can send me your data, please contact me at LGriffin@esri.com and I'm happy to explore further. Best wishes, Lauren Griffin, Esri ... View more

Hi again, The EMERGING_{ANALYSIS_VARIABLE}_HS_BIN variable is one of 7 values for each bin in the Space Time Cube and can be visualized in 3D: 3: Statistically significant clustering of high/large values at the 0.01 level 2: Statistically significant clustering of high/large values at the 0.05 level 1: Statistically significant clustering of high/large values at the 0.10 level 0: no statistically significant clustering -1: Statistically significant clustering of low/small values at the 0.10 level -2: Statistically significant clustering of low/small values at the 0.05 level -3: Statistically significant clustering of low/small values at the 0.01 level That variable does not include the Mann-Kendal trend information. The 2D output layer from Emerging Hot Spot Analysis (EHSA) shows the patterns and encapsulates the hot spot and trend information. To me, it is difficult to draw conclusions from 3D maps (unless your data set is very small). The 2D maps (like the default output from the EHSA tool) seem more useful, but it really very much depends on the questions you're hoping to answer with your analysis. If you haven't seen it already, consider watching this video, (at about 5:41 I give some examples of interpreting the EHSA results). The entire learning path might be interesting to you, though (my colleague Kevin provides some great examples of Time Series Clustering and Forecasting). Best wishes! Lauren Griffin, Esri ... View more

Your interpretation of the Gi* z-scores is exactly right! Technically, though, you could have a z-score of 5.0 for time period N-2, 4.5 for time period N-1, and 4.0 for time period N (where N is the total number of time steps in the cube). All of those z-scores would be statistically significant at the 0.01 level (ignoring the FDR correction for simplicity), but the pattern would not be an intensifying hot spot because the z-scores are getting smaller. Even if the z-scores are increasing, Mann-Kendal would be able to sort out if the increases are significant (little tiny increases probably wouldn't be considered "intensifying", but rather "persistent", and Mann-Kendal considers all the values in the column, not just the last couple ones). If you look at the tool in the Geoprocessing pane toolbox, you can right click on it and see the source code. I can try to find the section of the code that provides the exact descriptions for each of the patterns, or you can see if you can find it. Let me know if you want additional information. Again, thank you so much for pointing out the need to improve our documentation on the Emerging Hot Spot Analysis tool outputs! An issue has been created, so better documentation will be available in a future software release. Best wishes, Lauren ... View more

I agree it's a shame that the chart isn't generated automatically. Fortunately it's quite easy to create the chart from the output table. Right-click the output table and choose to create a line chart.  I hope this helps! Best wishes, Lauren Griffin, Esri   ... View more

Hi! Sorry for the delay in our reply. Both @LynneBuie  and I were swamped with the Esri User Conference this week. But I'm so glad you brought this to our attention. The documentation is not clear at all, as you mention. The table, here, does indeed correctly document the fields that are added to the Input Space Time Cube (STC) after you run Emerging Hot Spot Analysis (EHSA), but it doesn't document how to interpret the fields that appear in the EHSA output layer results (as you found).  Hopefully, this will help: The EHSA tool runs Gi*, comparing each bin and its space-time neighbors to the Global Window (Entire Cube, Neighborhood Time Step, or Individual Time Step) to determine if the local values are significantly larger (hot spot) or significantly lower (cold spot).   Once all bins in the cube have been assessed (for the categories shown in the graphic above), it runs Mann-Kendal on the Gi* Z-Score values to get the trend for each location (column). The PATTERN field in the EHSA output layer is one of potentially 17 different categories documented here. If you read the description for each category, you'll notice that to classify each location, the tool needs to know the count and percentage of significant bins and if they are hot or cold... and if they are at the top of the cube (most recent time steps) or not... And for most of the patterns this is enough, but to determine if the hot bins are intensifying or the cold bins are diminishing it computes Mann-Kendall on the Gi* z-score values. The TREND_Z, TREND_P, and TREND_BIN values in the EHSA output layer are those Mann-Kendall results. If that doesn't answer your first question, please ask again and I'll do my best to clarify. For your second question, you want to visualize the EMERGING_{ANALYSIS_VARIABLE}_HS_BIN. The 3rd column of the variable table indicates that particular variable is available in 3D. To visualize it, there are two steps: 1) Insert a NEW SCENE (select New Global Scene if it makes sense to show the curvature of the earth, otherwise select New Local Scene). 2) Run the Visualize Space Time Cube in 3D tool. Then you can navigate around the 3D scene with your mouse. I'm not sure if this will be helpful to you or not (it's NOT based on raster input), but @KevinButler-Analysis and I created a Learning Path for a workshop we did for UCGIS.  I hope this answers your questions. If not, please let me know and I'm happy to try again. Best wishes, Lauren Griffin, Esri   ... View more

Check out this video on Emerging Hot Spot Analysis. It visually describes the differences between each of the Global Window choices, and also discusses the differences in the results (and when one option might be better than another). https://www.youtube.com/watch?v=9VDRYBvOoDI&list=PLGZUzt4E4O2LuV0vuH74WN6j9nxv0jUty&index=5  It's one of 5 videos associated with this learning path: https://learn.arcgis.com/en/paths/spatio-temporal-analysis-of-covid-19-daily-confirmed-cases/  I hope this helps!  Lauren ... View more

Hi, If you choose Entire Cube, the tool conceptually compares the mean of each bin in the space time cube to the mean for all bins in the space time cube and determines if the means are significantly different (it's a bit more complicated than that, taking the number of features and variance into account, not just the mean). Check out this video, especially beginning at 5:37. https://www.youtube.com/watch?v=9VDRYBvOoDI&list=PLGZUzt4E4O2LuV0vuH74WN6j9nxv0jUty&index=5 That video is part of a learning path about space time analysis, in case it's useful: https://learn.arcgis.com/en/paths/spatio-temporal-analysis-of-covid-19-daily-confirmed-cases/  Best wishes, Lauren ... View more