# Divide each road to 30m long and assign unique id to all points in the segments [closed]

I have a big csv that contains this information of trip data for each point in 1s interval: `lon`, `lat`, `timestamp` and `street_id`. My aim is to segment street to 30m long segments and assign a unique segment id to each point based on its position.

Here is an example of what my data looks like using `df.head()`:

``````    timestamp           Lat           Lon           street_id
0   4/1/2014 0:11:00    40.7694320    -73.9544329   140
1   4/1/2014 0:17:00    40.7264327    -74.0343245   50
``````

I'm using `dask` as follows to get the data:

``````from dask.distributed import Client
client = Client()

``````

I just don't know how to begin, I know I have to group by `street_id` then calculate the distance between each point and the beginning of the street, but I've failed to find how to find how to do so

The expected result should look like:

``````    timestamp           Lat           Lon           street_id    segment_id
0   4/1/2014 0:11:00    40.7693330    -73.9533349   140          5
1   4/1/2014 0:17:00    40.7263337    -74.0333345   50           1
``````

`Segment_id` should be a unique identifier for each street segment of 30m long.

• Your question is a bit too difficult to answer because it deals with several different problems at once. Instead of asking "how to I achieve the final product I'm looking for", try to conceptualize the steps you need to take and then tackle each one of those steps individually. Ex: 1) sort the dataset by `street_id` and `timestamp`; 2) generate a line connecting all the points from each unique `street_id`; 3) split the street lines into uniform 30m segments. So try tacking the each step at a time and, if you get stuck, post what you've tried, what you expected and what happened. May 3, 2021 at 23:56