{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "# Spatial Dependency Index\n", "\n", "In this tutorial, we will learn how to estimate the **spatial dependency index**. Algorithm is based on the work:\n", "\n", "> [1] CAMBARDELLA, C.A.; MOORMAN, T.B.; PARKIN, T.B.; KARLEN, D.L.; NOVAK, J.M.; TURCO, R.F.; KONOPKA, A.E. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal, v. 58, n. 5, p. 1501-1511, 1994.\n", "\n", "## Prerequisites\n", "\n", "- **Domain**:\n", " - semivariance and covariance functions\n", "- **Package**:\n", " - `TheoreticalVariogram`\n", "- **Programming**:\n", " - Python basics\n", "\n", "## Table of contents\n", "\n", "1. What is the spatial dependency index?\n", "2. Why do we use spatial dependency index?\n", "3. Example: The comparison of different Spatial Dependence Index over the same extent but different elements.\n", "4. API links.\n", "\n", "## What is the spatial dependency index?\n", "\n", "The spatial dependency index (SDI) measures the strength of a spatial process we are modeling. SDI is normalized to the interval between 0 and 1. Therefore, we can transform it into percentages and assign an order of spatial dependency from weak to strong.\n", "\n", "The SDI is a ratio of the nugget to the total variance (sill) of a model:\n", "\n", "$$SDI = \\frac{nugget}{sill} * 100$$\n", "\n", "Whenever we fit a theoretical variogram with the pyinterpolate package, SDI is calculated, and we will take advantage of it in the examples. Two values represent SDI:\n", "\n", "- **numeric**, a ratio of nugget and sill in percent,\n", "- **categorical**, a description of a spatial dependency strength.\n", "\n", "There are four levels of spatial dependency.\n", "\n", "| Lower Limit (included) | Upper Limit (excluded) | Strength |\n", "|------------------------------|------------------------|-----------------------|\n", "| 0 | 25 | strong |\n", "| 25 | 75 | moderate |\n", "| 75 | 95 | weak |\n", "| 95 | inf | no spatial dependence |\n", "\n", "**The lower the ratio, the more substantial is spatial dependence**. If the ratio is greater than 75 percent, we should be cautious with spatial modeling because spatial similarities at analysed scale may not explain the process.\n", "\n", "\n" ], "id": "6e96876692e3fa89" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 2. Why do we use Spatial Dependency Index?\n", "\n", "In a world where Tobler’s Law can be applied to every spatial phenomenon, we might always use kriging without consideration. We know that spatial dependence exists, and close neighbors are always similar.\n", "\n", "This world is not our world! Not every process follows Tobler’s Law. We can find different elements and chemical compounds sampled over the same area and in the same scale, but their concentrations might be distributed randomly, without any spatial dependency. The example can be seen in publication [1].\n", "\n", "The spatial dependence index level is the first indicator that we can use to decide what to do next with a spatial dataset:\n", "\n", "- Strong: just krige it!\n", "- Moderate: there might be some other thing that explains process variation.\n", "- Weak: the other non-spatial process has more influence on data than spatial similarities.\n", "- No spatial dependence: the process is random, or spatial dependencies cannot explain variance.\n", "\n", "**Note**: Be careful! The last two points are red flags, BUT sometimes processes with a low variogram variability at one scale may be explained with spatial relations at a changed scale. A practical example is a comparison of rental apartment pricing: if you look at the scale of hundreds of kilometers - multiple cities within a country, the spatial dependence may be very weak or none. On the other hand, the spatial similarity between prices of apartments close to each other (up to 10 kilometers, 6 miles) tends to show a classic variogram curve. The reason is simple: most managers and algorithms use information about pricing of the closest neighbors, and neighborhood prices are affected by the same external objects or events." ], "id": "242242c5dc49ca8b" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 3. Example: Spatial Dependence over the same study extent but for different elements\n", "\n", "We will compare the spatial dependence index of four elements: cadmium, copper, lead, and zinc. We use the meuse dataset. The dataset comes from:\n", "\n", "```Pebesma, Edzer. (2009). The meuse data set: a tutorial for the gstat R package -> [link to the publication](https://cran.r-project.org/web/packages/gstat/vignettes/gstat.pdf)```" ], "id": "4600b58224287df6" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:56:55.949429Z", "start_time": "2025-12-21T14:56:52.665312Z" } }, "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import pyinterpolate as ptp" ], "id": "2341aa78e62d7ccb", "outputs": [], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:56:56.119320Z", "start_time": "2025-12-21T14:56:56.115842Z" } }, "cell_type": "code", "source": [ "MEUSE_FILE = '../data/meuse/meuse.csv'\n", "\n", "# Variogram parameters\n", "STEP_SIZE = 100\n", "MAX_RANGE = 1600\n", "ALLOWED_MODELS = 'safe'\n", "\n", "\n", "# Elements\n", "ELEMENTS = ['cadmium', 'copper', 'zinc', 'lead']\n", "COLS = ['x', 'y']\n", "COLS.extend(ELEMENTS)" ], "id": "2114c4c981cd72f0", "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:56:56.164797Z", "start_time": "2025-12-21T14:56:56.141165Z" } }, "cell_type": "code", "source": [ "df = pd.read_csv(MEUSE_FILE, usecols=COLS)\n", "df.head()" ], "id": "7efad3523a0a4ee5", "outputs": [ { "data": { "text/plain": [ " x y cadmium copper lead zinc\n", "0 181072 333611 11.7 85 299 1022\n", "1 181025 333558 8.6 81 277 1141\n", "2 181165 333537 6.5 68 199 640\n", "3 181298 333484 2.6 81 116 257\n", "4 181307 333330 2.8 48 117 269" ], "text/html": [ "
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" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 3 }, { "metadata": {}, "cell_type": "markdown", "source": "Data is skewed for every element in the table. Before we start variogram modeling, we must transform data to reduce skewness, and make data distribution closer to normal. We will use a logarithmic transform, pass data into an experimental variogram, and then create theoretical models. We will use grid search to find the best possible nugget value.", "id": "ee9efc4a89fcd859" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:57:00.021339Z", "start_time": "2025-12-21T14:56:56.368717Z" } }, "cell_type": "code", "source": [ "for element in ELEMENTS:\n", " # Prepare data\n", " ds = df[['x', 'y', element]].copy()\n", " ds[element] = np.log(ds[element])\n", " arr = ds.values\n", "\n", " # Get experimental variogram\n", " exp_var = ptp.build_experimental_variogram(values=ds[element],\n", " geometries=ds[['x', 'y']],\n", " step_size=STEP_SIZE,\n", " max_range=MAX_RANGE)\n", "\n", " # Find optimal theoretical model\n", " optimal_model = ptp.TheoreticalVariogram()\n", " optimal_model.autofit(experimental_variogram=exp_var, models_group=ALLOWED_MODELS, max_nugget=0.8)\n", " print('Optimal model for element {} has {:.2f}% of nugget to sill ratio. Spatial Dependence is {}.'.format(\n", " element, optimal_model.spatial_dependency_ratio, optimal_model.spatial_dependency_strength\n", " ))\n", " optimal_model.plot()\n", "\n", " print('\\n---\\n')" ], "id": "4526739f05341efb", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimal model for element cadmium has 30.54% of nugget to sill ratio. Spatial Dependence is moderate.\n" ] }, { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "---\n", "\n", "Optimal model for element copper has 20.69% of nugget to sill ratio. Spatial Dependence is strong.\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "---\n", "\n", "Optimal model for element zinc has 1.08% of nugget to sill ratio. Spatial Dependence is strong.\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "---\n", "\n", "Optimal model for element lead has 12.54% of nugget to sill ratio. Spatial Dependence is strong.\n" ] }, { "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "---\n", "\n" ] } ], "execution_count": 4 }, { "metadata": {}, "cell_type": "markdown", "source": "The strength of spatial dependence differs mostly between Cadmium and other elements.", "id": "ab52b5df2f5f7629" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 4. API\n", "\n", "The spatial dependence index may be calculated directly with the `calculate_spatial_dependence_index()` function that takes two parameters: `nugget` and `sill`. It returns `Tuple` with spatial dependence ratio and spatial dependence strength.\n", "\n", "Another way is to calculate `TheoreticalVariogram` with a grid search option for nugget (just leave the `nugget` parameter as `None` and the algorithm will find the optimal `nugget` value)." ], "id": "f0dad4806cf97bc9" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Changelog\n", "\n", "| Date | Changes | Author |\n", "|------------|----------------------------------------------|----------------------------------|\n", "| 2025-11-07 | Used **values** and **geometries** parameters instead of **ds** in the experimental variogram initialization | @SimonMolinsky (Szymon Moliński) |\n", "| 2025-04-23 | Tutorial has been adapted to the 1.0 release | @SimonMolinsky (Szymon Moliński) |" ], "id": "fadf128b15d26841" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }