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.
The Global Forecasting System (GFS) atmospheric model is a dataset from the National Oceanic and Atmospheric Administration (NOAA) and National Centers for Environmental Prediction (NCEP). In this database, wind is stored as velocity vector components (U: eastward_wind and V: northward_wind) at 10 m above the Earth’s surface. The resolution of the data is 0.5 degrees, approximately 50 km. Wind velocities have been registered six times per day (00:00 – 03:00 – 06:00 – 09:00 – 12:00 – 15:00 – 18:00 – 21:00 (UTC)), since 6th May 2011 and is updated daily.
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.
