Installation | Installation for DEM downloads | Functions | Elevation | Slope calculation | Plotting | Helper functions | Examples | Add elevation to linestrings or points | Calculate slope | Plot elevation profile | Working with segments | By vertex (native segments of the linestring) | By fixed length (using stplanr)

Introduction | Case studies | Roads in the Netherlands | Comparison of DEM-derived and survey-grade Z values | DEM for eu: | Test it's installed: | Find location of aws cli from powershell (equivalent of which aws on Linux): | Get-Command aws | Select-Object -ExpandProperty Source | C:\Program Files\Amazon\AWSCLIV2\aws.exe | Now aws should work | Download DEM for Brussels 6 km from center | Calculate slopes with the slopes package: | Add elevation to cycle network segments | check the extents of both: | Calculate slopes for each segment | cycle_net_xyz$slope = slope_xyz(cycle_net_xyz, lonlat = TRUE, fun = slope_matrix_mean) | Convert to percentage: | Convert to factor with greater than 5 being "5+" | Drop z dimension | install cran version | Save with tmap | url of the file: | References

Performance

Introduction | Load the package | Equivalence test with 2024 data | Comparing record counts and fatal counts | Using waldo for deep comparison | Working with large datasets (1979-latest)

Note | Introduction | Prerequisites | R and RStudio | Projects and scripts | Writing and running code | Viewing Objects | Autocompletion | Getting help | Commenting Code | The global environment | Debugging Code | Saving R objects | Manipulating R objects | Subsetting by index or name | Subsetting by values | Dealing with NAs and recoding | Changing class | Recoding values | Now you are ready to use R | R Packages | What are packages? | ggplot2 | dplyr and pipes | Temporal data | Spatial data | sf objects | Reading and writing spatial data | sf polygons | Spatial subsetting and sf plotting | Geographic joins | CRSs | Buffers | Attribute operations on sf objects | Matching roads to crashes | Visualising spatial datasets | Analysing point data from stats19 | Analysing crash data on road networks | Bonus exercises | References

Why v4.0.0? The "Breaking" Changes | Key Improvements | 1. Zero-Warning, High-Precision Loading | 2. Standardized Missing Values | 3. Faster Performance with readr Edition 2 | 4. New Research Tools: Costs and Cleaning | Getting Started

Make and model data

Introduction | Short history and affiliations | What is STATS19 anyway? | The road crash data in stats19 | Note | How stats19 works | Creating geographic crash data | The big picture: road safety | Conclusion | References

1. Main findings | 2. Pedestrian traffic and reported casualties | 3. How far do pedestrians travel? | 4. Casualty rates per mile travelled | 5. Sex and age comparisons | 6. Which vehicles are involved in collisions with pedestrians? | 7. Time of day of collisions | 8. What type of road? | 9. Vehicle movement on the road

Introduction | Functions | Download STATS19 data | Read STATS19 data | Crash data | Format STATS19 data | Casualties data | Vehicles data | Creating geographic crash data | Joining tables | Time series analysis | Further work | References

Introduction | Installing and Loading Packages | Choosing a region | Fetching PCT Data | Data Cleaning and Transformation | Download OSM building data | Using Abstr to Generate Scenarios | Importing scenario files into A/B Street | Conclusions and next steps

Introduction | Example | Load packages | Fetch Montlake Polygon | Parsing zones | Generate OD Matrix and zones table | Fetching OSM building data | Generate A/B Street scenarios using abstr | Importing scenario files into A/B Street

Introduction | Calculating slopes | Measures of route hilliness | Segments in a route: Cumulative slope | References

Extract the OSM network from geofabrik | Clean the road network | Filter the unconnected segments | Break the segments on vertices | Get slope values for each segment | Result: | Other examples

What are SIMs? | Implementation in R | Validation | References

Published paper | Introduction | The ClockBoard zoning system | Annuli radii | Number of segments | ClockBoard zones for segmenting urban areas | Using the ClockBoard zoning system | The zonebuilder R package | The zonebuilder Rust crate | Interactive zonebuilder web application | Applications | Navigation and location | Exploring city scale data | Inter-city comparison of geographically variable phenomena | Discussion and conclusion | References

Time | 2 day course agenda | Day 1: An introduction to R and RStudio for spatial and temporal data | Day 2 road safety analysis with R

Vehicle level variables in the STATS19 datasets | Relationships between vehicle type and crash severity

Prerequisites | Input data | Preparing a SIM | An unconstrained SIM | A production constrained SIM | Training a SIM | References

Note | Introduction | Package structure and functionality | Core functions and classes | Accessing and processing transport data | Creating geographic desire lines | Allocating flows to the transport network | Modelling travel catchment areas | Modelling and visualisation | Modelling mode choice | Models of travel behaviour | Visualisation | Future directions of travel | References

Target network preprocessing

Introduction: what is OD data? | The importance of OD data | The od package | Representing origin-destination data | A minimal od dataset | OD data representing travel to work | Origin-destination pairs (long form) | Origin destination matrices | Inter and intra-zonal flows | Bidirectional aggregation | Desire lines | Non-matching IDs | Aggregating and disaggregating OD data | Further reading | References

Introduction: what is OD data? | The importance of OD data | An example OD dataset | Origin-destination pairs (long form) | Origin destination matrices | Inter and intra-zonal flows | Oneway lines | Desire lines | Non-matching IDs | A larger example: commuter trips in London | Plotting origin-destination data | Summaries by origin and destination | Further reading | Summary | References

Introduction | Installing stplanr | OD data to desire lines and routes | Converting OD data to desire lines with R | Motivations | Further resources | Contributing | References

Introduction | Creating route networks from overlapping routes | Identifying route network groups | Routing on route networks | Adding new nodes | Other approaches

Getting the region of interest | Getting cycling potential accross local authorities | Cycling mode share | Cycling potential nationwide | Considerations | Challenge

Prerequisites | Estimating distance cycled for a region based on 2011 census data | Comparing Regions | Using PCT models to visualize potential cycle uptake | Conclusions and next steps

Introduction | Why this package? | Installation | Get PCT data | Comparing downloaded data with the PCT web app | Getting Census data | Estimate cycling uptake | References

Prerequisites | Picking a study area | Part A) Choosing a region | Part A) Picking a Local Authority | Plotting Current Cycle Uptake | Plotting New Demand Based On PCT Models | Comparing Two Cities | Conclusions and next steps

Introduction | Input data | Creating desire lines | Estimating cycling uptake | Routing | Next steps

Getting started | Installing R | Installing A/B Street | Using abstr | Importing scenario files into A/B Street | Projects supporting abstr | Context

Introduction | Minimal example | Importing into A/B Street | Intermediate example: Sao Paulo

Introduction | OSRM

Introduction | Setup | Explore results of automated zoning system | Generate zones for different cities | How many supermarkets in different zones of the city? | Explore how mode and distance of travel changes depending on city zones | Demo Dutch cities and commuting

With old route_cyclestreets function | With new route function | With new route function in parallel | In parallel with quietness plan | Tests

Creating events