MapChecking
This tool helps you estimate and fact-check the maximum number of people standing in a given area.
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This tool helps you estimate and fact-check the maximum number of people standing in a given area.
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This tool helps users estimate the maximum number of people standing in a particular area. MapChecking is a crowd-counting tool designed to assist in visualizing and analyzing the area covered by a specific event or situation, typically protests, demonstrations, or other gatherings, whether ongoing or planned. The tool allows users to input parameters such as the area being analyzed, the shape of the area (by drawing a polygon), and the estimated density of the crowd.
Based on the steps below, MapChecking can calculate the estimated density of participants in the overall space they cover on a map. The tool allows users to do all the steps on one webpage.
Input Location
Draw a Polygon and Map Visualization
Matching and Double-Checking
Determine Crowd Density Per Square Meter
Calculate Total Estimated Crowd Size
For this reason, open-source investigators may find MapChecking a valuable tool for verification.
Input the address of an already verified location into the map interface. For simplicity of this demonstration, we will use only the image above of the event to demonstrate the tool’s features. However, you must do due diligence by looking at multiple images of the event you are investigating.
We geolocated this area and found it in Democracy Square in Tel Aviv, at the intersection of Eli'ezer Kaplan Street and Giv'at HaTah̠moshet Street. The coordinates are 32.073368, 34.790295.
TIP: Why is it important to look at multiple images?
When analyzing images, especially those depicting large crowds, combining multiple source images taken from different angles is often beneficial. This is helpful to capture the full scope of the event and to avoid blind spots caused by obstructions.
Remember: Even with combined images, obstructions like buildings or bridges can limit visibility. Always be mindful of these factors and consider the potential limitations of the visual data.
The next step is to delineate the area of interest by drawing a polygon. The defined area is automatically overlaid on a map.
Double-check whether your area of interest in the source image matches the polygon drawn within the MapChecking tool. To do so, you can draw a polygon on the source image in a separate program and put both pictures next to each other (as shown in the picture below).
The next step consists of estimating crowd density per square meter, which helps determine the total estimated number of people in a specific area. Does the crowd appear light, crowded or packed?
First, it can help to gather reference images. Find close-up crowd photos. If possible, look for high-resolution images or verified footage from the event that show the crowd density up close. This helps assess how packed different sections are.
Second, compare that same close up reference image to the visual guides the tool provides. This step may sound complicated at first but fortunately, users can utilize resources like the research by Keith Still (linked below). These guides show what different crowd densities may look like (e.g., 1 person per square meter, 2 people per square meter).
Remember: Even with reference images, obstructions like buildings or bridges can limit your ability to see the entire crowd. Be mindful of these limitations when making your estimations.
The final step is to drag the "Crowd density" slider until it matches the estimated density as defined in the previous step. In this case, we drag the slider to around 4.25 people per square meter since the estimated crowd density is somewhere between 4 and 4.5 square meter.
The estimated total crowd count for the area of interest is 57,917 (shown in bold letters on the tool).
Internet Connection
Modern browser
Field of View Restrictions
The tool works better if you can clearly delineate the area visible in the image. There could be more people outside the camera's view or areas where the camera did not pan. Any such exclusions should be clearly mentioned.
Snapshot in Time
The estimate is only valid for the specific point in time when the image was captured. Any changes in crowd size or movement before or after that moment are not reflected.
Static vs. Dynamic Crowds
Exclusions for Low Visibility Areas: Certain areas of the source image may have low visibility or fewer people, which can affect the overall count. Highlighting these areas can improve the transparency of the estimate.
Transparency in Methodology: It is essential to be transparent about the limitations of the tool, especially regarding its reliance on clear images and fixed points in time. Users must communicate that the estimate provided is based on specific conditions and may not reflect the total count, particularly if areas are outside the camera's view or are affected by poor visibility.
Accuracy and Validation: Ethical use of MapChecking requires combining it with multiple methods and sources to arrive at a more accurate estimate. Relying solely on one tool without cross-referencing other data (e.g., additional images, videos, or on-the-ground reports) may lead to underestimating or overestimating crowd sizes.
Dynamic Nature of Crowds: Since crowds are dynamic and people move around, counting static points may not capture the real-time fluctuations of the crowd size. Ethical considerations require informing users and stakeholders that the count is a snapshot in time and may not fully reflect the dynamic nature of demonstrations or events.
Density Variability: Since crowd density is not uniform ethical practice encourages users to analyze density variations carefully and to provide separate estimates for different areas where appropriate.
Possibility of Misrepresentation: In the context of politically sensitive events, such as demonstrations, overestimating or underestimating crowd sizes can have significant implications. Ethical use of Mapchecking is encouraged.
Afton
The number of crowds can often be a between . Crowd sizes can also serve as evidence of how many people participated in a demonstration and or reach of the event. Crowd counting is not only relevant for open source researchers. According to , “[e]stimation of crowd size for large gatherings is an indispensable metric for [...] local authorities, and emergency management.”
Traditional crowd counting methods are either, such as entrance censors/turnstiles and ticket sales, or are a that includes cumbersome manual processes such as using people counters.
For this tool description, we take the as an example.
Our source image is a clip from a . The post mentions "tens of thousands" of protesters in Tel Aviv.
Note: can be used if you have a "static" crowd (mostly stationary). For moving crowds ("crowd flow"), users may use the reference on this link:
Have a look at this . Compare this to a clear photo of the reference image if possible from the event in question. Looking at both images can assist users in making estimations on crowd density.
In this example, we used (at 0:14 seconds), to find a clip that shows the density in the area we are interested in. By comparing Keith Still’s image reference, we concluded that the protest crowd maybe somewhere between 4 people per square meter and 4.5 people per square meter.
You can also read through Nixintel's article to see how MapChecking was used to estimate the crowds of the same protest independently from (and before) our own test.
The tool is less effective forsince crowd locations can shift rapidly. It works best when counting static crowds at a given moment. Uneven Crowd Density
Crowd density across all areas of an image. Some patches may have fewer people, while others may be densely packed. As seen in the source image above, there are patches in the protest area that are loosely packed and there are patches that are fully packed. For greater accuracy, it may be beneficial to draw separate polygons for areas with lower and higher crowd densities.
Nixintel: .
Aware Online: .
, France
