R is particularly powerful for spatial statistical analysis and quantitative researchers in particular may find R more useful than GIS desktop applications. As data becomes more geographical, there is a growing necessity to make spatial data more accessible and easy to process. While there are plenty of tools out there that can make your life much easier when processing spatial data (e.g. QGIS and ArcMap) using R to conduct spatial analysis can be just as easy.
Introduction In this post, we explore the analysis of local spatial autocorrelation statistics, focusing on the concept and its most common implementation in the form of the Local Moran statistic. We explore how it can be utilized to discover hot spots and cold spots in the data, as well as spatial outliers. To illustrate these techniques, we will use the catch data from Deep Sea fishing authority.
Moran’s I Moran’s I statistic is arguably the most commonly used indicator of global spatial autocorrelation.
OpenStreetMap is a collaborative project to create a free editable geographic database of the world. Open street Map (OSM) is name refer is a global open access mapping project. The data from OSM can be used in various ways including production of paper maps and electronic maps, geocoding of address and place names, and route planning.
To easy access of OSM spatial data, Mark Padgham and Robin Lovelace developed osmdata, which is an R package for downloading data from OSM.
