data-for-change / anyway-data-science Goto Github PK

Use Case Investigation Guidelines

1. learn the case - read/watch related news reports; read related sections of Vision Zero & The Israel National Road Safety Authority reports; what information did the media focus on? what did it not focus on?
2. was there a solution to the case? could there be other solution? if a solution was implemented, has the situation improved since then?
3. visualize the most important data about the case
4. what cases (new news flashes) could be a trigger to similar visualizations as suggested in section 3?

the end goal is to suggest a set of new infographics (visualizations) that will be shown to the reporters based on specific news flashes (triggers need to be chosen!).

when visualizing sections 1-4 and the end goal, don't forget to add verbal explanations to your work.

If you want to start working on this task, please assign yourself to it. If some people are already assigned to the task, you may contact them and ask if more help is needed :-)

please feel free to ask any other data science volunteer for assistance!

Nomi will work on this use case

Edited by Ela:

Part 1 - data wrangling:

• learn the "Gader Hafrada" data
• create a written summary (tetx, visualizations) of the data
• include any issues in data

Part 2 - infographics query:

• compare road accidents in areas where separation exists are where it doesn't
• are road accidents more lethal when there is no separation?
• can we visualize the differences?
• end goal: suggest a set of infographics to show when an accident happens in a road with no separation.

Since 2015 7 people were killed in this road, after a decade with no fatal accidents. Nowadays the road is being constructed.

1. Add intro information about the project
2. Add useful links and info for all (redash, data directory, etc)
3. Add useful links and info for the data team (github directory, notebooks, etc)

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.

1. Pedestrians (inside a city)
2. A heavy vehicle
3. outside the city
4. any other idea that comes to mind

Methodology:

Temporal Analysis: Compare car accident rates and characteristics before, during, and after the war. Identify any significant changes in frequency, severity, types of accidents, and affected geographical areas.
Spatial Analysis: Map the spatial distribution of accidents before, during, and after the war. Look for changes in accident hotspots and how they relate to wartime factors like military installations, infrastructure damage, and population displacement.
Driver Analysis: Assess if driver demographics affected by the war (e.g., younger/older drivers, military personnel, displaced populations) experienced different accident patterns.
Behavioral Analysis: Explore potential changes in driver behavior due to factors like heightened stress, increased fatigue.

In the last csv version of involved_hebrew_markers.csv there are 3 columns found with problematic values:

Found 3 columns that load wrong:

1. late_deceased_hebrew
   • \tפטירה רגילה\t
   • \tפטירה מאוחרת\t'
2. safety_measures_use_hebrew:
   • \t כן\t
   • '\t לא\t
   • '\t לא ידוע\t
3. 'vehicle_status_hebrew' has one value with "quotation marks", while the others are without:
   • val1: השכרה
   • val2: לימוד נהיגה
   • val3: "הסעת ילדים"

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.

מחלף השריון - מחלף חולון

When running the next query:

Select markers.longitude,markers.latitude
From involved_markers_hebrew,markers
where markers.id = involved_markers_hebrew.accident_id and involved_markers_hebrew.road_segment_number= 80
and involved_markers_hebrew.road1=1
limit 100

all lat and long are pretty much the same except two that are off limit,
Write small alg that can find and correct anomalies like that

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.

• Analyze and create a table for accidents per street, and a table for accidents per intersection in cities (start with Tel Aviv).

  • Tables should include both severity level 1 (lethal) and severity level 2 (injured) (without light injuries).

  • Add column with nohal prat score (https://github.com/data-for-change/anyway/blob/dev/anyway/parsers/schools_2023.ipynb, named calc_prat_score ).

  • Add column with sum of injured of ONLY severity 1, and 2, (no light injuries).

  • Make sure to count also light injuries into the nohal prat score but exclude light injuries from the aggregated sum of injured.

    • Remember to add severity 3 to the data so the nohal prat will be correct) to each intesection and streets .

  • Tables should be structured as follows:
    עיר | רחוב | הרוגים | פצועים קשה | פצועים קל | נוהל פרט כללי | נוהל פרט של הרוגים ופצועים קשה | הרוגים+פצועים קשה | עמודות של כל קטגוריות סוגי הנפגעים אחרי קיבוץ לפי הקוד הקיים של סכימה של הרוגים ופצועים קשה בלבד (ללא קל)

• Find inspiring anomalities in the data for publishing

• Make a stack bar of different injured vehecile categories (bicycle, trucks, pedestrians etc.) per street, and per intersection, and their nohal prat, and sum of injureds.

• Notice the duplications of injuries or accidents of intersections that also appears in the streets, And think how to approach this issue.

• Need to examine and decide on what columns to leave in and what columns to exclude from the new aggregated prat score by intersection excel file, and their desired order, currently:
  • צומת | פרת_כולל | פצועים_קל | פרת_קל | פצועים_קשה | פרת_קשה | הרוגים | פרת_הרוגים | הרוגים_ופצועים_קשה | פרת_הרוגים_ופצועים_קשה | אוטובוס ואוטובוס זעיר-קשה | אופנוע-קשה | אופניים חשמליים-קשה | אופניים-קשה | הולך רגל-קשה | קורקינט חשמלי-קשה | רכב כבד-קשה | רכב פרטי ומונית-קשה | אחר ולא ידוע-קשה | אופנוע-הרוגים | אופניים חשמליים-הרוגים | אופניים-הרוגים | הולך רגל-הרוגים | קורקינט חשמלי-הרוגים | אחר ולא ידוע-הרוגים

Both for English speakers and graphs creations in notebook

This task needs further planning and specification

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.

Description:

1. Create the data query
2. Create the data plot
3. Drill down to account for statistical methodology.
   • normalization
   • significance
   • deeper analysis to understand data source with respect to visualization type
   • etc.