使用 gps 坐标进行数据分析

Question

我有这样的数据，包含Timestamp，longitude，latitude和tripId，

我是否可以仅从这些数据中找到交叉路口的等待时间，或者我需要其他数据？我可以从这类数据中得到哪些信息？

"timestamp","tripId","longitude","latitude" "2021-07-05 10:35:04","1866491","8.167035","53.160473" "2021-07-05 10:35:03","1866491","8.167023","53.160469" "2021-07-05 10:35:02","1866491","8.167007","53.160459" "2021-07-05 10:35:01","1866491","8.166987","53.160455" "2021-07-05 10:35:00","1866491","8.166956","53.160448" "2021-07-05 10:34:20","1866491","8.167286","53.15919" "2021-07-05 10:34:19","1866491","8.167328","53.15918" "2021-07-05 10:34:18","1866491","8.16735","53.159165" "2021-07-05 10:34:17","1866491","8.167371","53.159148" "2021-07-05 10:34:16","1866491","8.167388","53.159124" "2021-07-05 10:34:15","1866491","8.167399","53.159105" "2021-06-30 20:25:30","1862861","8.211288","53.150848" "2021-06-30 20:25:29","1862861","8.211264","53.150851" "2021-06-30 20:25:28","1862861","8.211269","53.150842" "2021-06-30 20:25:27","1862861","8.211273","53.150836" "2021-06-30 20:25:26","1862861","8.211279","53.150836" "2021-06-30 20:25:25","1862861","8.211259","53.150848" "2021-06-30 20:25:24","1862861","8.211263","53.15085" "2021-06-30 20:25:21","1862861","8.211455","53.150782" "2021-06-30 20:25:20","1862861","8.211453","53.150786" "2021-06-30 20:25:19","1862861","8.211449","53.150792"

Answer 1

这个问题的答案：

which informations can i get from this kind of data

你有一个timestamp、一个tripId和一个坐标（longitude和latitude）。

因此，您知道此人或车辆在 his/its 行程 1866491 期间位于 8.166987 / 53.160455 2021-07-05 10:35:01.此外，您还可以计算行程持续时间。

您还可以通过按时间戳顺序连接行程的所有坐标来创建线。然后，您可以找出这些行程在哪个位置相互交叉。

根据线路特征，您可以计算出它们的长度（行程的距离）。连同行程的持续时间，您还可以计算出平均 步行或行驶速度。

（尽管您的示例数据没有相互交叉的行程。我不确定您所说的等待时间是什么意思。）

下面是一个示例脚本，说明如何将点转换为线并找到行程与彼此路径交叉的点：

from itertools import combinations

import pandas as pd
import geopandas as gpd

from shapely.geometry import LineString

# Read CSV File
df = pd.read_csv("trips.csv")

# Create Points
points = gpd.GeoDataFrame(
    df,
    geometry=gpd.points_from_xy(df.longitude, df.latitude),
    crs="EPSG:4326"
)
points = points.drop(columns=["latitude", "longitude"])

# Make sure Points are ordered (important)
points = points.sort_values(["tripId", "timestamp"])

# Create Lines
tolist = lambda x: LineString(x.tolist())
lines = points.groupby(["tripId"], as_index=False)["geometry"].apply(tolist)
lines = gpd.GeoDataFrame(lines, geometry="geometry", crs="EPSG:4326")

查找行程相互交叉的点：

# Get Intersection Points
template={"tripA":[], "tripB":[], "geometry":[]}
intersection_points = gpd.GeoDataFrame(template, geometry="geometry")
for index in combinations(lines.index, 2):
    
    combination = lines.loc[index,:]
    
    geometries = combination["geometry"].tolist()
    point = geometries[0].intersection(geometries[1])

    if point:  # LINESTRING EMPTY evaluates to false

        trips = combination["tripId"].tolist()
        row = pd.Series([trips[0], trips[1], point], index=intersection_points.columns)
        intersection_points = intersection_points.append(row, ignore_index=True)

将intersection_points写入CSV文件：

intersection_points["longitude"] = intersection_points.geometry.x
intersection_points["latitude"] = intersection_points.geometry.y

columns = ["tripA", "tripB", "latitude", "longitude"]
intersection_points.to_csv("intersections.csv", columns=columns)

将创建的线和交点写入形状文件：

# Write Shape Files
lines.to_file("trips.shp")
intersection_points.to_file("intersections.shp")

两个行程相互交叉的示例数据（基于问题的示例数据）：

"timestamp","tripId","longitude","latitude"
"2021-07-05 10:35:04","1866491","8.167035","53.160473"
"2021-07-05 10:35:03","1866491","8.167023","53.160469"
"2021-07-05 10:35:02","1866491","8.167007","53.160459"
"2021-07-05 10:35:01","1866491","8.166987","53.160455"
"2021-07-05 10:35:00","1866491","8.166956","53.160448"
"2021-07-05 10:34:20","1866491","8.167286","53.15919"
"2021-07-05 10:34:19","1866491","8.167328","53.15918"
"2021-07-05 10:34:18","1866491","8.16735","53.159165"
"2021-07-05 10:34:17","1866491","8.167371","53.159148"
"2021-07-05 10:34:16","1866491","8.167388","53.159124"
"2021-07-05 10:34:15","1866491","8.167399","53.159105"
"2021-06-30 20:25:30","1862861","8.211288","53.150848"
"2021-06-30 20:25:29","1862861","8.211264","53.150851"
"2021-06-30 20:25:28","1862861","8.211269","53.150842"
"2021-06-30 20:25:27","1862861","8.211273","53.150836"
"2021-06-30 20:25:26","1862861","8.211279","53.150836"
"2021-06-30 20:25:25","1862861","8.211259","53.150848"
"2021-06-30 20:25:24","1862861","8.211263","53.15085"
"2021-06-30 20:25:21","1862861","8.211455","53.150782"
"2021-06-30 20:25:19","1862861","8.211449","53.150792"
"2021-06-30 20:25:20","1862861","8.211453","53.150786"
"2021-06-30 20:25:18","1862861","8.166607","53.159654"

使用 gps 坐标进行数据分析

data analysis using gps coordinates

python

gps

analysis

data-analysis

jupyter-notebook