Plant ch 01

HARUG! 2023-01-11

Author

Ed Harris

Spatial Data in R


Plant 2019

Outline


  • Ch 01:
    • Overview
    • The datasets

Spatial data advancements


  • Increase in sensors, increase in available data

  • Statistical methods and software

  • So-called spatial data

  • subtleties, but basically geolocated, x and y coordinates

  • CRS (Coordinate Reference System[s])

Sensors and satellites

Special problems


Statistical properties of spatial data

  1. Spatial data has a lot of data points, so power is large even for tiny effect sizes, thus the Null is always rejected (even if practically meaningless)

  2. Spatial data points near each other are almost never independent, violating the common assumption that they are (a/k/a spatial autocorrelation)

Special problems


Ecological properties of spatial data

  • Low ‘ecological resolution’

  • High ‘data resolution’

  • Complex relationships

(next slide shows Soil moisture, Veg reflectance, Yield…)

Special problems

Cressie’s classification


  1. Geo-statistical data x-y point data with a continuous measure (like soil moisture). Extrapolation between measured points is a goal.

  2. Areal data points or polygons representing a uniform unit of measure (like the crop planted within a field boundary)

  3. Point pattern data what is the spatial pattern (like whether pest outbreaks are random or spatially explained by some feature)

Geostatistical versus Areal

Components of spatial data


  1. Spatial component (x-y)

  2. Attribute component (something measured or classified)

  3. Scale and sample size (for measuring earthworms, is 1m or 1000m better to sample?)

  4. Vector data versus Raster data

Dataset 1

Yellow billed cuckoo habitat

Dataset 1

What spatial featured are associated with presence in this species? (Data has shapes and attributes)

Dataset 2

Oak woodland habitat characteristics

Dataset 2

Why does young oak “recruitment” vary?

Data are rows and columns with x-y coords

Infer where habitat is suitable for oak population growth.

Dataset 3

Rice farming, flooding and crop rotation

Dataset 3

Spatial orientation of fields

Yield, different farmers

What factors affect yield (e.g. why do some farmers do better than others?)

Dataset 4

Spatial comparison of yield in 2 fields with identical management and crop history

Precision agriculture

What factors affect within-field variation in yield?

Coding