Introduction

The lightsf package offers a curated and diverse collection of georeferenced and spatial datasets from various domains, enabling researchers, educators, and analysts to easily explore spatial patterns and perform geostatistical analysis in R.

This package consolidates datasets from multiple open and trusted sources, including Kaggle, spData, adespatial, chopin, and bivariateLeaflet, to provide a unified resource for spatial data exploration and visualization.

The datasets included in lightsf cover a broad spectrum of topics such as urban studies, housing markets, environmental monitoring, transportation networks, and socio-economic indicators. Each dataset is carefully formatted and documented to support both educational purposes and applied spatial analysis.

lightsf provides data in multiple spatial formats —including point patterns, polygons, socio-economic data frames, and network-like structures— allowing users to perform tasks ranging from basic exploratory mapping to advanced spatial modeling.

By centralizing geospatial datasets in a single package, lightsf simplifies the workflow for those who wish to learn, teach, or apply spatial data science techniques without the need to gather and preprocess data from multiple sources.

Dataset Suffixes

Each dataset in the lightsf package uses a suffix to indicate the type of spatial data it contains:

_pts: Refers to point-based datasets that include georeferenced locations, usually represented by latitude and longitude coordinates.
_poly: Refers to polygon-based datasets, typically representing areas, administrative boundaries, or spatial zones.
_points: Refers to point datasets similar to _pts, often derived from other spatial sources or including additional spatial or attribute information.

These suffixes help users quickly identify the geometric structure and spatial representation of each dataset included in the lightsf package.

Example Datasets

Below are selected example datasets included in the lightsf package:

nc_points: Mildly clustered georeferenced points representing locations in North Carolina, United States.
dc_poly: Polygon-based spatial dataset containing Washington D.C. census tract data, suitable for creating choropleth maps and exploring demographic or spatial patterns.
afcon_poly: Polygon dataset representing spatial patterns of conflict in Africa (1966–1978), useful for studying regional clustering and spatial heterogeneity.

Data Visualization with lightsf Data

Spatial Patterns of Conflict in Africa (1966–1978)


# Basic exploration of the dataset
names(afcon_poly)
#> [1] "x"      "y"      "totcon" "name"   "id"
class(afcon_poly)
#> [1] "data.frame"
length(afcon_poly)
#> [1] 5
str(afcon_poly)
#> 'data.frame':    42 obs. of  5 variables:
#>  $ x     : num  9.56 2.63 -6.32 18.02 29.77 ...
#>  $ y     : num  34.1 28.2 31.9 27 26.6 ...
#>  $ totcon: num  1363 1421 1861 2355 5246 ...
#>  $ name  : Factor w/ 42 levels "ALGERIA","ANGOLA",..: 38 1 24 20 11 23 22 26 9 33 ...
#>  $ id    : num  2040 2039 2038 2041 2043 ...

# Ensure the dataset is a data frame
afcon_df <- as.data.frame(afcon_poly)

# Create a scatter plot of coordinates colored by total conflicts
ggplot(afcon_df, aes(x = x, y = y)) +
  geom_point(aes(color = totcon, size = totcon), alpha = 0.8) +
  scale_color_gradient(low = "lightyellow", high = "darkred") +
  labs(
    title = "Spatial Patterns of Conflict in Africa (1966–1978)",
    x = "Longitude",
    y = "Latitude",
    color = "Total Conflicts",
    size = "Conflict Intensity"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5),
    legend.position = "right"
  )

Conclusion

The lightsf package provides a curated and diverse collection of georeferenced and spatial datasets designed to support spatial data analysis, visualization, and education in R.
It brings together datasets from multiple open sources, offering ready-to-use spatial data covering topics such as urban studies, housing markets, environmental monitoring, transportation, and socio-economic indicators.