Notebook

Mapping All Collisions with Pedestrians and Cyclists in NYC¶

___NYC residents and visitors may be surprised to see how many vehicle collisions injured or killed a pedestrian or cyclist where they live, work, and travel___

Definitions¶

Collision: A motor vehicle collision involving injuries, deaths, or a significant amount of property damage (~> $1000) reported on a New York State form, MV104-AN

Data Sources¶

Collision data obtained from https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95
Data was processed by running process_raw.data.py which saves processed data to local directory specified in process_raw.data.py script
Note that ~10% of collisions are missing lat-long coordinates. These collisions are excluded from the following geographic analyses.

In [1]:

from datetime import datetime
import os.path
import pandas as pd
from src import visualizations as viz

Parameters¶

In [2]:

PROCESSED_CRASH_DATA = "data/processed/crashes.pkl"
IMG_DIR = "output/maps"

In [3]:

crashes = pd.read_pickle(PROCESSED_CRASH_DATA)

In [4]:

pedestrian = crashes[crashes["valid_lat_long"] & crashes["pedestrian"]]
cyclist = crashes[crashes["valid_lat_long"] & crashes["cyclist"]]

In [5]:

pedestrian_cols = [
    "LAT",
    "LONG",
    "DATE",
    "TIME",
    "PEDESTRIAN INJURED",
    "PEDESTRIAN KILLED",
]
pedestrian_format = (
    "'DATE: ' + row[2] + '<br>' +"
    "'TIME: ' + row[3] + '<br>' +"
    "'PEDESTRIANS INJURED: ' + row[4] + '<br>' +"
    "'PEDESTRIANS KILLED: ' + row[5]};"
)
cyclist_cols = [
    "LAT",
    "LONG",
    "DATE",
    "TIME",
    "CYCLIST INJURED",
    "CYCLIST KILLED",
]
cyclist_format = (
    "'DATE: ' + row[2] + '<br>' +"
    "'TIME: ' + row[3] + '<br>' +"
    "'CYCLISTS INJURED: ' + row[4] + '<br>' +"
    "'CYCLISTS KILLED: ' + row[5]};"
)

Map of all Collisions with Pedestrians 2018 - 2023 (inclusive)¶

Zoom in to see serious collisions and the date, time, number injured, and number killed

In [6]:

start = datetime(year=2018, month=1, day=1)
end = datetime(year=2024, month=1, day=1)
map_data = pedestrian[pedestrian["datetime"].between(start, end, inclusive="left")][
    pedestrian_cols
]

pedestrian_map = viz.make_marker_map(map_data, pedestrian_format)
pedestrian_map.save(os.path.join(IMG_DIR, "pedestrian_map_18_23.html"))
pedestrian_map

Out[6]:

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Map of all Collisions with Cyclist 2018 - 2023 (inclusive)¶

Zoom in to see serious collisions and the date, time, number injured, and number killed

In [7]:

start = datetime(year=2018, month=1, day=1)
end = datetime(year=2024, month=1, day=1)
map_data = cyclist[cyclist["datetime"].between(start, end, inclusive="left")][
    cyclist_cols
]

cyclist_map = viz.make_marker_map(map_data, cyclist_format)
cyclist_map.save(os.path.join(IMG_DIR, "cyclist_map_18_23.html"))
cyclist_map

Out[7]:

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