Quick Start Guide

This guide will help you get started with movr quickly.

Loading the Package

First, load the movr package and example data:

library(movr)
data(movement)

# View the structure of the example data
str(movement)
head(movement)

Basic Trajectory Visualization

Create a simple 3D trajectory plot:

# Basic 3D trajectory visualization
plot_traj3d(movement,
            x = "lon", y = "lat", z = "timestamp",
            color_by = "user_id",
            alpha = 0.7)

Flow Map Analysis

Create and visualize flow maps:

# Create flow map from mobility data
flow_data <- flowmap(movement,
                     from = "origin_cell",
                     to = "destination_cell",
                     weight = "flow_count")

# Visualize with custom styling
plot_flowmap(flow_data,
             node_size = "population",
             edge_width = "flow_strength",
             color_scheme = "viridis")

Spatial Analysis

Calculate radius of gyration and spatial correlations:

# Calculate radius of gyration
rog <- radius_of_gyration(movement,
                         x = "lon", y = "lat",
                         id = "user_id")

# Spatial correlation analysis
spatial_corr <- spatial.corr(movement,
                            x = "lon", y = "lat",
                            time_window = "daily")

Temporal Analysis

Analyze time-of-day patterns and generate sessions:

# Time-of-day analysis
tod_data <- hour2tod(movement$timestamp)

# Generate mobility sessions
sessions <- gen_sessions(movement,
                        id = "user_id",
                        time_threshold = 3600)  # 1 hour

# Calculate temporal entropy
temp_entropy <- entropy.spacetime(movement,
                                 id = "user_id",
                                 time_bins = 24)

Data Quality Assessment

Assess the quality of your mobility data:

# Comprehensive data quality check
dq_result <- dq.traj(movement,
                     id = "user_id",
                     time = "timestamp",
                     x = "lon", y = "lat")

# Point-level quality assessment
point_quality <- dq.point(movement,
                         x = "lon", y = "lat",
                         time = "timestamp")

Advanced Visualizations

Create more complex visualizations:

# Voronoi tessellation in 3D
voronoi_result <- voronoi3d(movement,
                           x = "lon", y = "lat", z = "timestamp")

# Interactive 3D map visualizations
map3d_result <- map3d(movement,
                      x = "lon", y = "lat", z = "timestamp",
                      terrain = TRUE,
                      buildings = TRUE)

Next Steps

Now that you’ve completed the quick start:

  1. Explore the examples section for more detailed examples

  2. Check the api section for complete function documentation

  3. Read the research section to understand potential applications

  4. Visit the GitHub repository for the latest updates

Getting Help

If you need help:

  • Use ?function_name for detailed function documentation

  • Check the vignettes with vignette(package = “movr”)

  • Report issues on GitHub