Data & Functions for Working with US ZIP Codes

Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating ZIP codes to Census data, geocoding & distance calculations.

Client for 'Mapzen' and Related Map APIs

Provides an interface to 'Mapzen'-based APIs (including geocode.earth, Nextzen, and NYC GeoSearch) for geographic search and geocoding, isochrone calculation, and vector data to draw map tiles. See < https://www.mapzen.com/documentation/> for more information. The original Mapzen has gone out of business, but 'rmapzen' can be set up to work with any provider who implements the Mapzen API.

Preprocessing Plant Trait Data

Designed to support the application of plant trait data providing easy applicable functions for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns and rows, excluding trait data according to different attributes, geocoding, long- to wide-table transformation, and data export. 'rtry' was initially developed as part of the TRY R project to preprocess trait data received via the TRY database.

Search and Retrieve Spatial Data from 'GUGiK'

Automatic open data acquisition from resources of Polish Head Office of Geodesy and Cartography ('Główny Urząd Geodezji i Kartografii') (< https://www.gov.pl/web/gugik>). Available datasets include various types of numeric, raster and vector data, such as orthophotomaps, digital elevation models (digital terrain models, digital surface model, point clouds), state register of borders, spatial databases, geometries of cadastral parcels, 3D models of buildings, and more. It is also possible to geocode addresses or objects using the geocodePL_get() function.

An Implementation of Crime Analysis Methods

An implementation of functions for the analysis of crime incident or records management system data. The package implements analysis algorithms scaled for city or regional crime analysis units. The package provides functions for kernel density estimation for crime heat maps, geocoding using the 'Google Maps' API, identification of repeat crime incidents, spatio-temporal map comparison across time intervals, time series analysis (forecasting and decomposition), detection of optimal parameters for the identification of near repeat incidents, and near repeat analysis with crime network linkage.

Visualisation and Annotation of 360 Degree Imagery

Provides a customisable R 'shiny' app for immersively visualising, mapping and annotating panospheric (360 degree) imagery. The flexible interface allows annotation of any geocoded images using up to 4 user specified dropdown menus. The app uses 'leaflet' to render maps that display the geo-locations of images and panellum < https://pannellum.org/>, a lightweight panorama viewer for the web, to render images in virtual 360 degree viewing mode. Key functions include the ability to draw on & export parts of 360 images for downstream applications. Users can also draw polygons and points on map imagery related to the panoramic images and export them for further analysis. Downstream applications include using annotations to train Artificial Intelligence/Machine Learning (AI/ML) models and geospatial modelling and analysis of camera based survey data.

Who are You? Bayesian Prediction of Racial Category Using Surname, First Name, Middle Name, and Geolocation

Predicts individual race/ethnicity using surname, first name, middle name, geolocation, and other attributes, such as gender and age. The method utilizes Bayes' Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" Political Analysis and Imai, Olivella, and Rosenman (2022) "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" . The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity data for first, middle, and surnames" .