Author: Wenbo Lyu

# Geographical Pattern Causality

#### Wenbo Lyu

####
Last update: 2026-04-03
Last run: 2026-04-05

## Methodological Background

Geographical Pattern Causality (GPC) infers causal relations from spatial cross-sectional data by reconstructing a symbolic approximation of the underlying spatial dynamical system.

Let \(x(s)\) and \(y(s)\) denote two spatial cross-sections over spatial units \(s \in \mathcal{S}\).

(1) Spatial Embedding

For each spatial unit \(s_i\), GPC constructs an embedding vector

\[ \mathbf{E}_{x(s_i)} = \big( x(s_{i}^{(1)}), x(s_{i}^{(2)}), \dots, x(s_{i}^{(E\tau)}) \big), \]

where \(s_{i}^{(k)}\) denotes the \(k\)-th spatially lagged value of the spatial unit \(s_i\), determined by embedding dimension \(E\) and lag \(\tau\). This yields two reconstructed state spaces \(\mathcal{M}_x, \mathcal{M}_y \subset \mathbb{R}^E\).

(2) Symbolic Pattern Extraction

Local geometric transitions in each manifold are mapped to symbols

\[ \sigma_x(s_i),; \sigma_y(s_i) \in \mathcal{A}, \]

encoding increasing, decreasing, or non-changing modes. These symbolic trajectories summarize local pattern evolution.

(3) Cross-Pattern Mapping

Causality from \(x \to y\) is assessed by predicting:

\[ \hat{\sigma}_y(s_i) = F\big( \sigma_x(s_j): s_j \in \mathcal{N}_k(s_i) \big), \]

where \(\mathcal{N}_k\) denotes the set of \(k\) nearest neighbors in \(\mathcal{M}_x\). The agreement structure between \(\hat{\sigma}_y(s_i)\) and \(\sigma_y(s_i)\) determines the causal mode:

- Positive: \(\hat{\sigma}_y = \sigma_y\)
- Negative: \(\hat{\sigma}_y = -\sigma_y\)
- Dark: neither agreement nor opposition

(4) Causal Strength

The global causal strength is the normalized consistency of symbol matches:

\[ C_{x \to y} = \frac{1}{|\mathcal{S}|} \sum_{s_i \in \mathcal{S}} \mathbb{I}\big[ \hat{\sigma}_y(s_i) \bowtie \sigma_y(s_i) \big], \]

where \(\bowtie\) encodes positive, negative, or dark matching rules.

## Usage examples

### Example of spatial vector data

Load the `spEDM` package and its columbus spatial analysis data:

    library(spEDM)

    columbus = sf::read_sf(system.file("case/columbus.gpkg", package="spEDM"))
    columbus
    ## Simple feature collection with 49 features and 6 fields
    ## Geometry type: POLYGON
    ## Dimension:     XY
    ## Bounding box:  xmin: 5.874907 ymin: 10.78863 xmax: 11.28742 ymax: 14.74245
    ## Projected CRS: Undefined Cartesian SRS with unknown unit
    ## # A tibble: 49 × 7
    ##    hoval   inc  crime  open plumb discbd                                    geom
    ##    <dbl> <dbl>  <dbl> <dbl> <dbl>  <dbl>                               <POLYGON>
    ##  1  80.5 19.5  15.7   2.85  0.217   5.03 ((8.624129 14.23698, 8.5597 14.74245, …
    ##  2  44.6 21.2  18.8   5.30  0.321   4.27 ((8.25279 14.23694, 8.282758 14.22994,…
    ##  3  26.4 16.0  30.6   4.53  0.374   3.89 ((8.653305 14.00809, 8.81814 14.00205,…
    ##  4  33.2  4.48 32.4   0.394 1.19    3.7  ((8.459499 13.82035, 8.473408 13.83227…
    ##  5  23.2 11.3  50.7   0.406 0.625   2.83 ((8.685274 13.63952, 8.677577 13.72221…
    ##  6  28.8 16.0  26.1   0.563 0.254   3.78 ((9.401384 13.5504, 9.434411 13.69427,…
    ##  7  75    8.44  0.178 0     2.40    2.74 ((8.037741 13.60752, 8.062716 13.60452…
    ##  8  37.1 11.3  38.4   3.48  2.74    2.89 ((8.247527 13.58651, 8.2795 13.5965, 8…
    ##  9  52.6 17.6  30.5   0.527 0.891   3.17 ((9.333297 13.27242, 9.671007 13.27361…
    ## 10  96.4 13.6  34.0   1.55  0.558   4.33 ((10.08251 13.03377, 10.0925 13.05275,…
    ## # ℹ 39 more rows

The false nearest neighbours (FNN) method helps identify the appropriate minimal embedding dimension for reconstructing the state space of a time series or spatial cross-sectional data.

    spEDM::fnn(columbus, "crime", E = 1:10, eps = stats::sd(columbus$crime))
    ## [spEDM] Output 'E:i' corresponds to the i-th valid embedding dimension.
    ## [spEDM] Input E values exceeding max embeddable dimension were truncated.
    ## [spEDM] Please map output indices to original E inputs before interpretation.
    ##        E:1        E:2        E:3        E:4        E:5        E:6        E:7
    ## 0.59183673 0.04081633 0.04081633 0.10204082 0.00000000 0.00000000 0.00000000
    ##        E:8
    ## 0.00000000

The false nearest neighbours (FNN) ratio decreased to `0` when the embedding dimension E reached 5, and remained relatively stable thereafter. Therefore, we adopted \(E = 5\) as the minimal embedding dimension for subsequent parameter search.

Then, search optimal parameters:

    # determine the type of causality using correlation
    stats::cor.test(columbus$hoval,columbus$crime)
    ##
    ##  Pearson's product-moment correlation
    ##
    ## data:  columbus$hoval and columbus$crime
    ## t = -4.8117, df = 47, p-value = 1.585e-05
    ## alternative hypothesis: true correlation is not equal to 0
    ## 95 percent confidence interval:
    ##  -0.7366777 -0.3497978
    ## sample estimates:
    ##        cor
    ## -0.5744867

    # since the correlation is -0.574, negative causality is selected as the metric to maximize in the optimal parameter search
    spEDM::pc(columbus, "crime", "hoval", E = 5:9, k = 7:10, tau = 1, maximize = "negative")
    ## The suggested E,k,tau for variable hoval is 7, 9 and 1

Run geographical pattern causality analysis

    spEDM::gpc(columbus, "crime", "hoval", E = 7, k = 9)
    ##       type   strength      direction
    ## 1 positive 0.00000000 crime -> hoval
    ## 2 negative 0.34035656 crime -> hoval
    ## 3     dark 0.02786836 crime -> hoval
    ## 4 positive 0.00000000 hoval -> crime
    ## 5 negative 0.00000000 hoval -> crime
    ## 6     dark 0.05337361 hoval -> crime

Convergence diagnostics

    crime_convergence = spEDM::gpc(columbus, "crime", "hoval",
                                   libsizes = seq(9, 50, by = 1),
                                   E = 7, k = 9, progressbar = FALSE)
    crime_convergence
    ##     libsizes     type    strength      direction
    ## 1         10 positive 0.000000000 crime -> hoval
    ## 2         11 positive 0.000000000 crime -> hoval
    ## 3         12 positive 0.000000000 crime -> hoval
    ## 4         13 positive 0.000000000 crime -> hoval
    ## 5         14 positive 0.000000000 crime -> hoval
    ## 6         15 positive 0.000000000 crime -> hoval
    ## 7         16 positive 0.000000000 crime -> hoval
    ## 8         17 positive 0.000000000 crime -> hoval
    ## 9         18 positive 0.000000000 crime -> hoval
    ## 10        19 positive 0.000000000 crime -> hoval
    ## 11        20 positive 0.000000000 crime -> hoval
    ## 12        21 positive 0.000000000 crime -> hoval
    ## 13        22 positive 0.000000000 crime -> hoval
    ## 14        23 positive 0.000000000 crime -> hoval
    ## 15        24 positive 0.000000000 crime -> hoval
    ## 16        25 positive 0.000000000 crime -> hoval
    ## 17        26 positive 0.000000000 crime -> hoval
    ## 18        27 positive 0.000000000 crime -> hoval
    ## 19        28 positive 0.000000000 crime -> hoval
    ## 20        29 positive 0.000000000 crime -> hoval
    ## 21        30 positive 0.000000000 crime -> hoval
    ## 22        31 positive 0.000000000 crime -> hoval
    ## 23        32 positive 0.000000000 crime -> hoval
    ## 24        33 positive 0.000000000 crime -> hoval
    ## 25        34 positive 0.000000000 crime -> hoval
    ## 26        35 positive 0.000000000 crime -> hoval
    ## 27        36 positive 0.000000000 crime -> hoval
    ## 28        37 positive 0.000000000 crime -> hoval
    ## 29        38 positive 0.000000000 crime -> hoval
    ## 30        39 positive 0.000000000 crime -> hoval
    ## 31        40 positive 0.000000000 crime -> hoval
    ## 32        41 positive 0.000000000 crime -> hoval
    ## 33        42 positive 0.000000000 crime -> hoval
    ## 34        43 positive 0.000000000 crime -> hoval
    ## 35        44 positive 0.000000000 crime -> hoval
    ## 36        45 positive 0.000000000 crime -> hoval
    ## 37        46 positive 0.000000000 crime -> hoval
    ## 38        47 positive 0.000000000 crime -> hoval
    ## 39        48 positive 0.000000000 crime -> hoval
    ## 40        49 positive 0.000000000 crime -> hoval
    ## 41        10 negative 0.000000000 crime -> hoval
    ## 42        11 negative 0.000000000 crime -> hoval
    ## 43        12 negative 0.000000000 crime -> hoval
    ## 44        13 negative 0.000000000 crime -> hoval
    ## 45        14 negative 0.000000000 crime -> hoval
    ## 46        15 negative 0.000000000 crime -> hoval
    ## 47        16 negative 0.000000000 crime -> hoval
    ## 48        17 negative 0.000000000 crime -> hoval
    ## 49        18 negative 0.000000000 crime -> hoval
    ## 50        19 negative 0.079758388 crime -> hoval
    ## 51        20 negative 0.095663420 crime -> hoval
    ## 52        21 negative 0.104704018 crime -> hoval
    ## 53        22 negative 0.083390936 crime -> hoval
    ## 54        23 negative 0.155933157 crime -> hoval
    ## 55        24 negative 0.160468454 crime -> hoval
    ## 56        25 negative 0.159841305 crime -> hoval
    ## 57        26 negative 0.153965990 crime -> hoval
    ## 58        27 negative 0.211917428 crime -> hoval
    ## 59        28 negative 0.186256958 crime -> hoval
    ## 60        29 negative 0.174366043 crime -> hoval
    ## 61        30 negative 0.172561491 crime -> hoval
    ## 62        31 negative 0.189445238 crime -> hoval
    ## 63        32 negative 0.212568790 crime -> hoval
    ## 64        33 negative 0.192410926 crime -> hoval
    ## 65        34 negative 0.193216971 crime -> hoval
    ## 66        35 negative 0.192956642 crime -> hoval
    ## 67        36 negative 0.209433064 crime -> hoval
    ## 68        37 negative 0.203325807 crime -> hoval
    ## 69        38 negative 0.203325807 crime -> hoval
    ## 70        39 negative 0.205654746 crime -> hoval
    ## 71        40 negative 0.205127658 crime -> hoval
    ## 72        41 negative 0.209042309 crime -> hoval
    ## 73        42 negative 0.209042309 crime -> hoval
    ## 74        43 negative 0.226222782 crime -> hoval
    ## 75        44 negative 0.226700257 crime -> hoval
    ## 76        45 negative 0.226904376 crime -> hoval
    ## 77        46 negative 0.238064699 crime -> hoval
    ## 78        47 negative 0.312430855 crime -> hoval
    ## 79        48 negative 0.340356565 crime -> hoval
    ## 80        49 negative 0.340356565 crime -> hoval
    ## 81        10     dark 0.000000000 crime -> hoval
    ## 82        11     dark 0.000000000 crime -> hoval
    ## 83        12     dark 0.000000000 crime -> hoval
    ## 84        13     dark 0.000000000 crime -> hoval
    ## 85        14     dark 0.000000000 crime -> hoval
    ## 86        15     dark 0.000000000 crime -> hoval
    ## 87        16     dark 0.000000000 crime -> hoval
    ## 88        17     dark 0.000000000 crime -> hoval
    ## 89        18     dark 0.000000000 crime -> hoval
    ## 90        19     dark 0.000000000 crime -> hoval
    ## 91        20     dark 0.000000000 crime -> hoval
    ## 92        21     dark 0.000000000 crime -> hoval
    ## 93        22     dark 0.000000000 crime -> hoval
    ## 94        23     dark 0.000000000 crime -> hoval
    ## 95        24     dark 0.000000000 crime -> hoval
    ## 96        25     dark 0.000000000 crime -> hoval
    ## 97        26     dark 0.000000000 crime -> hoval
    ## 98        27     dark 0.000000000 crime -> hoval
    ## 99        28     dark 0.000000000 crime -> hoval
    ## 100       29     dark 0.000000000 crime -> hoval
    ## 101       30     dark 0.000000000 crime -> hoval
    ## 102       31     dark 0.000000000 crime -> hoval
    ## 103       32     dark 0.000000000 crime -> hoval
    ## 104       33     dark 0.000000000 crime -> hoval
    ## 105       34     dark 0.000000000 crime -> hoval
    ## 106       35     dark 0.000000000 crime -> hoval
    ## 107       36     dark 0.000000000 crime -> hoval
    ## 108       37     dark 0.000000000 crime -> hoval
    ## 109       38     dark 0.000000000 crime -> hoval
    ## 110       39     dark 0.000000000 crime -> hoval
    ## 111       40     dark 0.000000000 crime -> hoval
    ## 112       41     dark 0.003273597 crime -> hoval
    ## 113       42     dark 0.009965908 crime -> hoval
    ## 114       43     dark 0.017923335 crime -> hoval
    ## 115       44     dark 0.019163588 crime -> hoval
    ## 116       45     dark 0.019163588 crime -> hoval
    ## 117       46     dark 0.026656692 crime -> hoval
    ## 118       47     dark 0.026656692 crime -> hoval
    ## 119       48     dark 0.027868360 crime -> hoval
    ## 120       49     dark 0.027868360 crime -> hoval
    ## 121       10 positive 0.000000000 hoval -> crime
    ## 122       11 positive 0.000000000 hoval -> crime
    ## 123       12 positive 0.000000000 hoval -> crime
    ## 124       13 positive 0.000000000 hoval -> crime
    ## 125       14 positive 0.000000000 hoval -> crime
    ## 126       15 positive 0.000000000 hoval -> crime
    ## 127       16 positive 0.000000000 hoval -> crime
    ## 128       17 positive 0.000000000 hoval -> crime
    ## 129       18 positive 0.000000000 hoval -> crime
    ## 130       19 positive 0.000000000 hoval -> crime
    ## 131       20 positive 0.000000000 hoval -> crime
    ## 132       21 positive 0.000000000 hoval -> crime
    ## 133       22 positive 0.000000000 hoval -> crime
    ## 134       23 positive 0.000000000 hoval -> crime
    ## 135       24 positive 0.000000000 hoval -> crime
    ## 136       25 positive 0.000000000 hoval -> crime
    ## 137       26 positive 0.000000000 hoval -> crime
    ## 138       27 positive 0.000000000 hoval -> crime
    ## 139       28 positive 0.000000000 hoval -> crime
    ## 140       29 positive 0.000000000 hoval -> crime
    ## 141       30 positive 0.000000000 hoval -> crime
    ## 142       31 positive 0.000000000 hoval -> crime
    ## 143       32 positive 0.000000000 hoval -> crime
    ## 144       33 positive 0.000000000 hoval -> crime
    ## 145       34 positive 0.000000000 hoval -> crime
    ## 146       35 positive 0.000000000 hoval -> crime
    ## 147       36 positive 0.000000000 hoval -> crime
    ## 148       37 positive 0.000000000 hoval -> crime
    ## 149       38 positive 0.000000000 hoval -> crime
    ## 150       39 positive 0.000000000 hoval -> crime
    ## 151       40 positive 0.000000000 hoval -> crime
    ## 152       41 positive 0.000000000 hoval -> crime
    ## 153       42 positive 0.000000000 hoval -> crime
    ## 154       43 positive 0.000000000 hoval -> crime
    ## 155       44 positive 0.000000000 hoval -> crime
    ## 156       45 positive 0.000000000 hoval -> crime
    ## 157       46 positive 0.000000000 hoval -> crime
    ## 158       47 positive 0.000000000 hoval -> crime
    ## 159       48 positive 0.000000000 hoval -> crime
    ## 160       49 positive 0.000000000 hoval -> crime
    ## 161       10 negative 0.000000000 hoval -> crime
    ## 162       11 negative 0.000000000 hoval -> crime
    ## 163       12 negative 0.000000000 hoval -> crime
    ## 164       13 negative 0.000000000 hoval -> crime
    ## 165       14 negative 0.000000000 hoval -> crime
    ## 166       15 negative 0.000000000 hoval -> crime
    ## 167       16 negative 0.000000000 hoval -> crime
    ## 168       17 negative 0.000000000 hoval -> crime
    ## 169       18 negative 0.000000000 hoval -> crime
    ## 170       19 negative 0.000000000 hoval -> crime
    ## 171       20 negative 0.000000000 hoval -> crime
    ## 172       21 negative 0.000000000 hoval -> crime
    ## 173       22 negative 0.000000000 hoval -> crime
    ## 174       23 negative 0.000000000 hoval -> crime
    ## 175       24 negative 0.000000000 hoval -> crime
    ## 176       25 negative 0.000000000 hoval -> crime
    ## 177       26 negative 0.000000000 hoval -> crime
    ## 178       27 negative 0.000000000 hoval -> crime
    ## 179       28 negative 0.000000000 hoval -> crime
    ## 180       29 negative 0.000000000 hoval -> crime
    ## 181       30 negative 0.000000000 hoval -> crime
    ## 182       31 negative 0.000000000 hoval -> crime
    ## 183       32 negative 0.000000000 hoval -> crime
    ## 184       33 negative 0.000000000 hoval -> crime
    ## 185       34 negative 0.000000000 hoval -> crime
    ## 186       35 negative 0.000000000 hoval -> crime
    ## 187       36 negative 0.000000000 hoval -> crime
    ## 188       37 negative 0.000000000 hoval -> crime
    ## 189       38 negative 0.000000000 hoval -> crime
    ## 190       39 negative 0.000000000 hoval -> crime
    ## 191       40 negative 0.000000000 hoval -> crime
    ## 192       41 negative 0.000000000 hoval -> crime
    ## 193       42 negative 0.000000000 hoval -> crime
    ## 194       43 negative 0.000000000 hoval -> crime
    ## 195       44 negative 0.000000000 hoval -> crime
    ## 196       45 negative 0.000000000 hoval -> crime
    ## 197       46 negative 0.000000000 hoval -> crime
    ## 198       47 negative 0.000000000 hoval -> crime
    ## 199       48 negative 0.000000000 hoval -> crime
    ## 200       49 negative 0.000000000 hoval -> crime
    ## 201       10     dark 0.050816175 hoval -> crime
    ## 202       11     dark 0.043383185 hoval -> crime
    ## 203       12     dark 0.041382131 hoval -> crime
    ## 204       13     dark 0.040519557 hoval -> crime
    ## 205       14     dark 0.048272151 hoval -> crime
    ## 206       15     dark 0.040230136 hoval -> crime
    ## 207       16     dark 0.044464149 hoval -> crime
    ## 208       17     dark 0.044665447 hoval -> crime
    ## 209       18     dark 0.042914041 hoval -> crime
    ## 210       19     dark 0.041468145 hoval -> crime
    ## 211       20     dark 0.038849769 hoval -> crime
    ## 212       21     dark 0.038819148 hoval -> crime
    ## 213       22     dark 0.038858502 hoval -> crime
    ## 214       23     dark 0.039451135 hoval -> crime
    ## 215       24     dark 0.040338936 hoval -> crime
    ## 216       25     dark 0.038705221 hoval -> crime
    ## 217       26     dark 0.036890764 hoval -> crime
    ## 218       27     dark 0.037987604 hoval -> crime
    ## 219       28     dark 0.039793512 hoval -> crime
    ## 220       29     dark 0.037765229 hoval -> crime
    ## 221       30     dark 0.038233561 hoval -> crime
    ## 222       31     dark 0.036447303 hoval -> crime
    ## 223       32     dark 0.039415083 hoval -> crime
    ## 224       33     dark 0.037622935 hoval -> crime
    ## 225       34     dark 0.038474108 hoval -> crime
    ## 226       35     dark 0.037545521 hoval -> crime
    ## 227       36     dark 0.041351817 hoval -> crime
    ## 228       37     dark 0.039936088 hoval -> crime
    ## 229       38     dark 0.044526449 hoval -> crime
    ## 230       39     dark 0.041276856 hoval -> crime
    ## 231       40     dark 0.044994931 hoval -> crime
    ## 232       41     dark 0.044496909 hoval -> crime
    ## 233       42     dark 0.049654135 hoval -> crime
    ## 234       43     dark 0.050765691 hoval -> crime
    ## 235       44     dark 0.052651619 hoval -> crime
    ## 236       45     dark 0.053726028 hoval -> crime
    ## 237       46     dark 0.053726628 hoval -> crime
    ## 238       47     dark 0.053373606 hoval -> crime
    ## 239       48     dark 0.053373606 hoval -> crime
    ## 240       49     dark 0.053373606 hoval -> crime
    plot(crime_convergence, ylimits = c(-0.01,0.61),
         xlimits = c(9,50), xbreaks = seq(10, 50, 5))

Figure 1. Convergence curves of causal strengths among house value and crime.

### Example of spatial raster data

Load the `spEDM` package and its farmland NPP data:

    library(spEDM)

    npp = terra::rast(system.file("case/npp.tif", package = "spEDM"))
    # To save the computation time, we will aggregate the data by 3 times
    npp = terra::aggregate(npp, fact = 3, na.rm = TRUE)
    npp
    ## class       : SpatRaster
    ## size        : 135, 161, 5  (nrow, ncol, nlyr)
    ## resolution  : 30000, 30000  (x, y)
    ## extent      : -2625763, 2204237, 1867078, 5917078  (xmin, xmax, ymin, ymax)
    ## coord. ref. : CGCS2000_Albers
    ## source(s)   : memory
    ## names       :      npp,        pre,      tem,      elev,         hfp
    ## min values  :   187.50,   390.3351, -47.8194, -110.1494,  0.04434316
    ## max values  : 15381.89, 23734.5330, 262.8576, 5217.6431, 42.68803711

    # Check the validated cell number
    nnamat = terra::as.matrix(npp[[1]], wide = TRUE)
    nnaindice = which(!is.na(nnamat), arr.ind = TRUE)
    dim(nnaindice)
    ## [1] 6920    2

Determining minimal embedding dimension:

    spEDM::fnn(npp, "npp", E = 1:15,
               eps = stats::sd(terra::values(npp[["npp"]]),na.rm = TRUE))
    ## [spEDM] Output 'E:i' corresponds to the i-th valid embedding dimension.
    ## [spEDM] Input E values exceeding max embeddable dimension were truncated.
    ## [spEDM] Please map output indices to original E inputs before interpretation.
    ##          E:1          E:2          E:3          E:4          E:5          E:6
    ## 0.9813070569 0.5309427415 0.1322254335 0.0167630058 0.0017341040 0.0000000000
    ##          E:7          E:8          E:9         E:10         E:11         E:12
    ## 0.0001445087 0.0000000000 0.0000000000 0.0000000000 0.0002890173 0.0000000000
    ##         E:13         E:14
    ## 0.0001445087 0.0001445087

At \(E = 6\), the false nearest neighbor ratio stabilizes approximately at 0.0001 and remains constant thereafter. Therefore, \(E = 6\) is selected as minimal embedding dimension for the subsequent GPC analysis.

Then, search optimal parameters:

    stats::cor.test(~ pre + npp,
                    data = terra::values(npp[[c("pre","npp")]],
                                         dataframe = TRUE, na.rm = TRUE))
    ##
    ##  Pearson's product-moment correlation
    ##
    ## data:  pre and npp
    ## t = 108.74, df = 6912, p-value < 2.2e-16
    ## alternative hypothesis: true correlation is not equal to 0
    ## 95 percent confidence interval:
    ##  0.7855441 0.8029426
    ## sample estimates:
    ##       cor
    ## 0.7944062

    g1 = spEDM::pc(npp, "pre", "npp", E = 6:10, k = 7:12, tau = 1:5, maximize = "positive")
    g1
    ## The suggested E,k,tau for variable npp is 6, 9 and 5

Run geographical pattern causality analysis

    spEDM::gpc(npp, "pre", "npp", E = 6, k = 9, tau = 5)
    ##       type  strength  direction
    ## 1 positive 0.5222514 pre -> npp
    ## 2 negative 0.3180282 pre -> npp
    ## 3     dark 0.3795653 pre -> npp
    ## 4 positive 0.4491962 npp -> pre
    ## 5 negative 0.6663956 npp -> pre
    ## 6     dark 0.4303680 npp -> pre

Convergence diagnostics

    npp_convergence = spEDM::gpc(npp, "pre", "npp",
                                 libsizes = matrix(rep(seq(10,80,10),2), ncol = 2),
                                 E = 6, k = 9, tau = 5, progressbar = FALSE)
    npp_convergence
    ##    libsizes     type   strength  direction
    ## 1       100 positive 0.13573984 pre -> npp
    ## 2       400 positive 0.18147283 pre -> npp
    ## 3       900 positive 0.24614666 pre -> npp
    ## 4      1600 positive 0.33710096 pre -> npp
    ## 5      2500 positive 0.40395843 pre -> npp
    ## 6      3600 positive 0.45524137 pre -> npp
    ## 7      4900 positive 0.48874459 pre -> npp
    ## 8      6400 positive 0.51020746 pre -> npp
    ## 9       100 negative 0.00000000 pre -> npp
    ## 10      400 negative 0.00000000 pre -> npp
    ## 11      900 negative 0.00000000 pre -> npp
    ## 12     1600 negative 0.00000000 pre -> npp
    ## 13     2500 negative 0.05496496 pre -> npp
    ## 14     3600 negative 0.12023142 pre -> npp
    ## 15     4900 negative 0.23867121 pre -> npp
    ## 16     6400 negative 0.30815296 pre -> npp
    ## 17      100     dark 0.06048052 pre -> npp
    ## 18      400     dark 0.11029673 pre -> npp
    ## 19      900     dark 0.16639601 pre -> npp
    ## 20     1600     dark 0.22696194 pre -> npp
    ## 21     2500     dark 0.27885166 pre -> npp
    ## 22     3600     dark 0.31852011 pre -> npp
    ## 23     4900     dark 0.35338378 pre -> npp
    ## 24     6400     dark 0.37870193 pre -> npp
    ## 25      100 positive 0.13945164 npp -> pre
    ## 26      400 positive 0.19473737 npp -> pre
    ## 27      900 positive 0.25254635 npp -> pre
    ## 28     1600 positive 0.31327780 npp -> pre
    ## 29     2500 positive 0.36524631 npp -> pre
    ## 30     3600 positive 0.41042253 npp -> pre
    ## 31     4900 positive 0.43152740 npp -> pre
    ## 32     6400 positive 0.45137261 npp -> pre
    ## 33      100 negative 0.16639620 npp -> pre
    ## 34      400 negative 0.24974175 npp -> pre
    ## 35      900 negative 0.49955110 npp -> pre
    ## 36     1600 negative 0.66653262 npp -> pre
    ## 37     2500 negative 0.83295414 npp -> pre
    ## 38     3600 negative 0.99940859 npp -> pre
    ## 39     4900 negative 0.78807485 npp -> pre
    ## 40     6400 negative 0.66641031 npp -> pre
    ## 41      100     dark 0.08290872 npp -> pre
    ## 42      400     dark 0.14873362 npp -> pre
    ## 43      900     dark 0.22827942 npp -> pre
    ## 44     1600     dark 0.28941655 npp -> pre
    ## 45     2500     dark 0.32906970 npp -> pre
    ## 46     3600     dark 0.36571275 npp -> pre
    ## 47     4900     dark 0.39113087 npp -> pre
    ## 48     6400     dark 0.41925841 npp -> pre
    plot(npp_convergence, ylimits = c(-0.01,1.05),
         xlimits = c(0,6500), xbreaks = seq(100, 6400, 500))

Figure 2. Convergence curves of causal strengths among precipitation and NPP.