How to Analyze Geospatial Data using Power BI

In this Power BI article, we will learn about How to analyze Geospatial Data using Power BI. Discover what geospatial data analysis is, the different types of geospatial data, and how to analyze geospatial data using Power BI.

From retail and e-commerce to environmental sustainability, military, and natural disasters, geospatial data is crucial to enrich our data analyses.

In this post, we discuss what geospatial data analysis is, the benefits, the types of geospatial data, and how it's used. Lastly, we close off with a walkthrough of geospatial data analysis in Power BI.

What is Geospatial Data Analysis?

Geospatial data describes things in terms of their location on Earth. These could be people, animals, objects, natural phenomena, or business outcomes. 

Geospatial data analysis goes beyond just describing things and also explains where and why they occur. This process involves collecting, transforming, and visualizing geospatial data.

Many industries can use geospatial data analysis, from retail and e-commerce to environmental stability, and even governments for tracking resources, weather, disease, and defense.

Benefits of Using Geospatial Data in Analytics

Using geospatial data in analytics offers many benefits to organizations. Most importantly, this type of location-based data provides a multi-dimensional context to your data. We discuss the three most significant benefits of geospatial data analysis.

Gain deeper insight into patterns and trends

Geospatial data makes it easier to identify patterns and visualize trends in location-based data compared to other methods, like bar charts or tables. Furthermore, we can easily infer these patterns and trends based on their relative geographical proximity.

We can then segment and filter the data based on these patterns to obtain even deeper insights into why they may occur.

Obtain a better representation of the real world

By visualizing geographical data, we can better represent the real world. In addition, having real-time data (or even close to real-time) can significantly enhance our understanding of business data to drive decision-making.

For example, suppose a retail business owner wants to set up a new shop in their area. They can use geospatial data to answer questions that help them determine the best location for their shop. 

Some examples of these questions are:

  • Where are my competitor's shops located?
  • How are competitors' shops distributed? Do they cluster around specific locations, or are they more spread out?
  • Where do my target customers live in the area?

Develop targeted products and solutions

Geospatial data analysis helps organizations visualize which locations are more successful than others.

Suppose a business understands the relationship between its products or services and the location they offer them. In that case, they can target those products to those locations or develop new products with a proven track record of success in a particular location.

Types of Geospatial Data

There are two main types of geospatial data: vector and raster data. Additionally, the third type of data typically accompanies geographical data: attributes. 

The type of data we are dealing with informs what tools we should use to analyze and then visualize the data. We will discuss each of these data types below. 

An additional point to note is that all types of geospatial data must be accompanied by coordinate reference system (CRS) information. This CRS information tells the software we use to conduct geospatial data analysis where the geospatial data is located on Earth.

When sharing geospatial data, if you use a different CRS system than someone else, the data will show up in a different geographic location when analyzed. Unfortunately, CRS systems are beyond the scope of this article (and you will see later how ArcGIS for Power BI simplifies it for us). However, we have a course on working with geospatial data that explores CRS systems in depth, among other things.

Vector Data

Vector data describes the features of geographic locations on Earth through the use of points, lines, and polygons.

  • Points describe individual locations, like a building or a landmark.
  • Lines are a series of connected points describing things like roads or streams.
  • Polygons are a series of connected points that form an enclosed shape, such as the boundaries of a country.

Raster Data

Raster data is pixel- or grid-based data describing geographic location characteristics. Each pixel can be either a continuous value (such as an elevation value) or a categorical classification (such as land cover classifications). You might already be familiar with pixel-based data types, as this is how images are typically represented in machine learning models, such as in computer vision applications.

The resolution of the digital images used for raster data is essential because it determines the area of the ground that each pixel covers.

Attributes

Attributes are descriptive (non-spatial) data combined with other geospatial data to enhance visualizations and analytics. For example, when dealing with sales and customer data, these attributes can be information on the purchases that customers made or on the customers themselves.

How is Geospatial Data Used?

One of the most recent public applications of geospatial data analysis is for tracking COVID-19 around the world and determining the subsequent environmental and social impacts of COVID-19. This involves a combination of population data, real-time updates, maps, environmental factors (like CO2 ommissions), and so on.

For example, the US Department of Transportation conducted a geospatial data analysis of CO2 emissions from transportation from April 2019 to April 2020. Their visualizations show a significant drop in CO2 emissions over time, which they calculated to be as much as 40%. While it is possible to state a value like this, it does not have the same impact as the visualization on a map.

Here are a few more geospatial data examples:

  • Sales and customer insights - visualizing and analyzing patterns in customer behavior and understanding their preferences for better inventory management, store location, and even when choosing the optimal location for factories, warehouses, and distribution centers.
  • Logistics and route optimization - in the case of courier companies, optimizing routes can lead to massive time and cost savings, ultimately leading to higher customer satisfaction.
  • Military - Optimizing resource allocations and predicting maintenance requirements.
  • Monitoring natural disasters and weather-related conditions - predicting and responding to extreme weather conditions can improve response times and provide timely evacuation alerts.
  • Human population forecasting - the population growth rate is essential for many industries, such as law enforcement, energy, housing, and telecommunications. Understanding population growth is a crucial requirement for effective planning and better management of resources.

Geospatial Data Analysis in Power BI

Geospatial data can be analyzed in popular programming languages like R and Python. In addition, there are several packages designed specifically for handling geospatial data. Check out these DataCamp courses to learn more about these:

ArcGIS for Power BI is a tool for geospatial data analysis and visualization. ArcGIS is a tool for creating maps from geospatial data, but when combined with Power BI, you can quickly build powerful reports to generate insights and drive decision-making.

This section will introduce you to ArcGIS for Power BI and show you how to create ArcGIS maps in Power BI. If you want to follow along with this section, we will be exploring the Airbnb listing data found on DataCamp Workspaces. You will also need Power BI Desktop installed on your PC. For more information on how to download and install Power BI, see this install guide.

Preparing your data

Geospatial data can only be visualized effectively if the locations in the data are properly geocoded. This refers to the process of transforming the description of a location into a recognized location on Earth.

ArcGIS for Power BI offers two services for mapping location data onto a location on the map. The first is the ArcGIS World Geocoding Service which detects point locations (such as addresses and cities). The second is the ArcGIS GeoEnrichment Service which detects boundary locations (such as ZIP Codes and countries).

However, it is also possible to improve the accuracy of your location data with a few extra data preparation steps.

You can add detailed address information to the ArcGIS map, such as an address, city, state, province, ZIP code, country, etc. However, to achieve this, the ArcGIS visualization on Power BI can only add a single column from your dataset to the Location field. If you have separate columns for each element of this address information in your data, you will need to combine these into a single column.

You can create a calculated column to combine each of these elements using a formula like this:

Column = [Address] & ", " & [City] & ", " & [Country] & ", " & [Postal Code]

If you do not have detailed address information but rather more general data like country or US state, then ArcGIS can recognize this information and map a wider boundary (this is like the polygon data type from vector data mentioned above).

One of the easiest, and most accurate ways, of adding location information to an ArcGIS map is to use separate longitude and latitude columns added to their respective fields in the visualization options. Using this type of information is recommended if it is available in your data.

Creating a Map Using ArcGIS for Power BI

ArcGIS is a built-in visual in Power BI. You can find it among all other visualization choices. We have a course on data visualization in Power BI if you'd like extra guidance on visualizing data in Power BI and the best practices.

ArcGIS for Power BI Visualization Options

Click the ArcGIS icon to add it to the Power BI canvas. In the options on the right-hand side, you will see the types of fields that this visual supports. We add the neighborhood column to the Location field and the latitude and longitude columns to their respective fields. We want to visualize the prices of Airbnb listings in Los Angeles, so we add the price column to the Size field.

This produces the following result:

ArcGIS for Power BI Visualization
In addition to the information in our dataset, we add an additional infographic card to the chart. This is a feature offered by ArcGIS, and it dynamically changes based on the items we can see - in this case, we have zoomed into Los Angeles. Since we are dealing with property data, we added the average home value. This adds some context to the relative prices of Airbnb listings we see in this visual.

Based on the size of the circles, we can see that Malibu has some of the most expensive Airbnb listings!

Conclusion

Geospatial data analysis is a powerful tool for generating deeper insights and offers an additional layer of context to our data. This improves decision-making in organizations across many industries. 

As you have seen in this post, using ArcGIS with Power BI dramatically simplifies the preparation of geospatial data and allows you to create dynamic visualizations quickly and easily.

Original article sourced at: https://www.datacamp.com

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How to Analyze Geospatial Data using Power BI
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Is Power BI Actually Useful?

The short answer, for most of you, is no. However, the complexity and capability of the products could be beneficial depending on what type of position or organization you work in.
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In my effort to answer this common question about Power BI I researched the following:
– Power BI Desktop Gateway
– Syncing on-prem SQL server data
– Syncing SharePoint Online list data
– Syncing data from an Excel workbook
– Building, and sharing a dashboard
– Inserting a Power BI visualization into PowerPoint

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

The feature spread above gave me the opportunity to explore the main features of Power BI which break down as:
– Ingesting data, building a data set
– Creating dashboard or reports with visualizations based on that data

In a nutshell Power BI is a simple concept. You take a data set, and build visualizations that answer questions about that data. For example, how many products have we sold in Category A in the last month? Quarter? Year? Power BI is especially powerful when drilling up or down in time scale.
And there are some interesting ways to visualize that data:
However, there are a number of drawbacks to the current product that prevented me from being able to fold these visualizations into our existing business processes.

  1. Integration with PowerPoint is not free. This shocked me.

The most inspiring Power BI demo I saw at a Microsoft event showed a beautiful globe visualization within a PowerPoint presentation. It rendered flawlessly within PowerPoint and was a beautiful, interactive way to explore a geographically disparate data set. I was able to derive conclusions about the sales data displayed without having to look at an old, boring chart.

During the demo, nothing was mentioned about the technology required to make this embedded chart a reality. After looking into the PowerPoint integration I learned that not only was the add-in built by a third party, it was not free, and when I signed up for a free trial the add-in could barely render my Power BI visualization. The data drill up/down functionality was non-existent and not all of the visualizations were supported. Learn more from Power bi online course

  1. Only Dashboards can be shared with other users, and cannot be embedded in our organization’s community on SharePoint.

Folks in our organization spent 50% of their time in Outlook, and the rest in SharePoint, OneNote, Excel, Word, and the other applications needed for producing documents, and other work. Adding yet another destination to that list to check on how something is doing was impossible for us. Habits are extremely hard to change, and I see that consistently in our client’s organizations as well.

Because I was not able to fold in the visualizations with the PowerPoint decks we use during meetings, I had to stop presentations in the middle, navigate to Internet Explorer (because the visualizations only render well in that browser), and then go back to PowerPoint once we were done looking at the dashboard.

This broke up the flow of our meetings, and led to more distractions. I also followed up with coworkers after meetings to see if they ever visited the dashboard themselves at their desk. None of them had ever navigated to a dashboard outside of a meeting.

  1. The visualizations aren’t actually that great.

Creating visualizations that cover such a wide variety of data sets is difficult. But, the Excel team has been working on this problem for over 15 years. When I import my SharePoint or SQL data to Excel I’m able to create extremely customized Pivot Tables and Charts that show precisely the data I need to see.

I was never able to replicate visualizations from Excel in Power BI, to produce the types of visualizations I actually needed. Excel has the ability to do conditional formatting, and other customizations in charts and tables that is simply not possible with Power BI. Because of how generic the charts are, and the limited customization it looks “cool” without being functional.

In conclusion, if you have spare time and want to explore Power BI for your organization you should. However, if you are seriously thinking about how you can fold this product into your work processes, challenge yourself to build a dashboard and look at it once a week. See if you can keep that up for a month, and then think about how that change affected your work habits and whether the data analysis actually contributed value each time. At least half of you will realize that this gimmicky product is fancy, but not actually useful.

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Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

Explore your JIRA Data with Power BI

JIRA Software provides bug tracking, issue tracking, and project management capabilities for teams and organizations. The JIRA content pack for Power BI helps you quickly import JIRA data so you can get an instant dashboard to analyze workloads, see how quickly you’re resolving issues, visualize velocity over time, and more. Power BI helps you quickly filter by project or component to generate new insights into your JIRA data.

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To connect to the JIRA content pack, simply choose JIRA from the list of available content packs. You will be asked to provide your JIRA URL and credentials. Once the connection has completed, Power BI will automatically create an out-of-the-box dashboard, report, and dataset with data from JIRA.

dashboard

The dashboard for the JIRA content pack shows you key metrics about your workloads, velocity over time, and the breakdown of your bugs and issues by status and assignee. Clicking on any of the dashboard tiles will open the JIRA content pack report, where you can interact and explore your data.

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You can slice the data in the report using the ‘Item Type’ slicer on the top, so you can find exactly the items you are looking for. Power BI reports are interactive, so if you select a ‘Fix Version’, a ‘Component’, or an ‘Assignee’, the rest of the report page will update per the selection.Learn more from Power BI online course

The report has a second page that focuses on the velocity of creating and resolving issues, and also shows the list of recently resolved items.

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After the initial import, the dashboard and the reports continue to update daily so you are always seeing up-to-date data. You can control the refresh schedule on the dataset, and configure it to refresh at the exact times you choose.
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