dbmss

Distance Based Measures of Spatial Structures

https://github.com/ericmarcon/dbmss

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Distance Based Measures of Spatial Structures

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%", 
  message = FALSE
)
```

# Distance Based Measures of Spatial Structures 


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dbmss is an R package for simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's *K* and others) and more recent ones used by spatial economists (Duranton and Overman's *Kd*, Marcon and Puech's *M*). It relies on spatstat for some core calculation.

## Installation

You can install the current release of the package from CRAN or the development version of dbmss from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("EricMarcon/dbmss")
```

## Main functions

The main functions of the package are designed to calculate distance-based measures of spatial structure.
Those are non-parametric statistics able to summarize and test the spatial distribution (concentration, dispersion) of points.

The classical, topographic functions such as Ripley's _K_ are provided by the _spatstat_ package and supported by _dbmss_ for convenience.

Relative functions are available in _dbmss_ only.
These are the $M$ and $m$ and $K_d$ functions.

The bivariate $M$ function can be calculated for _Q. Rosea_ trees around _V. Americana_ trees:

```{r m}
library(dbmss)
autoplot(
  Mhat(
    paracou16, 
    ReferenceType = "V. Americana", 
    NeighborType = "Q. Rosea"
  ), 
  main = ""
)
```


## Confidence envelopes

Confidence envelopes of various null hypotheses can be calculated.
The univariate distribution of _Q. Rosea_ is tested against the null hypothesis of random location.

```{r}
autoplot(
  KdEnvelope(paracou16, ReferenceType = "Q. Rosea", Global = TRUE), 
  main = ""
)
```

Significant concentration is detected between about 10 and 20 meters.


## Maps

Individual values of some distance-based measures can be computed and mapped.

```{r}
# Calculate individual intertype M(distance) value
ReferenceType <- "V. Americana"
NeighborType <- "Q. Rosea"
fvind <- Mhat(
  paracou16, 
  r = c(0, 30), 
  ReferenceType = ReferenceType, 
  NeighborType = NeighborType, 
  Individual = TRUE
)
# Plot the point pattern with values of M(30 meters)
p16_map <- Smooth(
  paracou16, 
  fvind = fvind, 
  distance = 30,
  # Resolution
  Nbx = 512, 
  Nby = 512
)
par(mar = rep(0, 4))
plot(p16_map, main = "")
# Add the reference points to the plot
is.ReferenceType <- marks(paracou16)$PointType == ReferenceType
points(
  x = paracou16$x[is.ReferenceType], 
  y = paracou16$y[is.ReferenceType], 
  pch = 20
)
# Add contour lines
contour(p16_map, nlevels = 5, add = TRUE)
```

## Vignettes

A quick [introduction](https://EricMarcon.github.io/dbmss/) is in `vignette("dbmss")`.

A [full documentation](https://ericmarcon.github.io/dbmss/articles/articles/reference.html) is available on the package website.
It is a continuous update of the paper published in the Journal of Statistical Software ([Marcon et al., 2015](https://doi.org/10.18637/jss.v067.c03)).

## Reference

Marcon, E., Traissac, S., Puech, F. and Lang, G. (2015). Tools to Characterize Point Patterns: dbmss for R. 
*Journal of Statistical Software*. 67(3): 1-15.

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