docs/5_UrbanModel.ipynb

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    "![UCL](https://github.com/profLewis/Geog2021_Coursework/blob/master/images/ucl_logo.png?raw=true)\n",
    "\n",
    "\n",
    "# Modelling Land Use change with socio-economic drivers\n",
    "\n",
    "## Summary of requirements\n",
    "\n",
    "The write-up for the modelling section has 15% of the total marks.\n",
    "\n",
    "Using the data derived from Landsat data over Shenzhen, calibrate a model that describes urban land use change as a function of a set of socioeconomic factors, following the approach of Seto et al. (2002, 2003) (of primary importance)\n",
    "\n",
    "![](images/comp.png)\n",
    "\n",
    "Test the model to assess uncertainty (of secondary importance)\n",
    "\n",
    "## The model\n",
    "\n",
    "![class_movie4](images/class_movie4.gif)\n",
    "\n",
    "Having derived a dataset of urban land use, we can calculate urban land use change per year.\n",
    "\n",
    "### Pre-requisites\n",
    "\n",
    "You will need:\n",
    "\n",
    "* `R` and `Rstudio` \n",
    "* required codes and datasets\n",
    "\n",
    "See [this section](3_RunningROnYourOwnComputer.ipynb) for details on that.\n",
    "\n",
    "\n",
    "### The Seto model\n",
    "\n",
    "Following the general approach of [Seto & Kaufmann (2003)](reading/SetoandKaufmann2003.pdf), we will build a multi-linear model to attempt to describe the urban land use change per year (the 'y' variable) as a function of a number of key socioeconomic factors (e.g. capital investment, land productivity, population, wage rates, etc) (the '$x$' variables). \n",
    "\n",
    "We suppose that we have observation ('$y$') data for $N_{years}$ and $N_{params} - 1$ of socioeconomic factors for each of these years.\n",
    "\n",
    "So, we will derive a (multi)*linear* model that we could phrase as:\n",
    "\n",
    "**Equation 1:**\n",
    "\n",
    "$$\n",
    "y = p_0 + p_1 x_1 + p_2 x_2 + + p_3 x_3 + p_4 x_4 + p_5 x_5\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "where we supply the following information:\n",
    "\n",
    "* $y$ is a column vector of length $N_{years} - 1$ with urban land use change per year. The minus 1 is because we must calculate land use *change* from one year to the next.\n",
    "* $p_n$ are the model parameters, or 'weightings' with $0 \\le n \\lt N_{params}$, with one parameter per socioeconomic factor, plus an 'offset' ($p_0$) that doesn't depend on these factors.\n",
    "* $x_n$ are the socioeconomic data per year, with $0 \\lt n \\lt N_{params}$ for  $N_{years} - 1$ years (one entry for each year, for each socioeconomic factor). The minus 1 associated with $N_{params}$ is because we have an additional parameter, $p_0$.\n",
    "\n",
    "So there are a total of $N_{params}$ *unknown* values (the model parameters $p_n$) that we need to estimate to calibrate' the model.\n",
    "\n",
    "Following the example in the paper, we will use the following in $x$:\n",
    "\n",
    "* $x_1$: Investment in capital construction / population\n",
    "* $x_2$: value of gross agricultural output / agriculture population\n",
    "* $x_3$: log(wages in non-state, non-collective units)\n",
    "* $x_4$: log(average total wage)\n",
    "* $x_5$: (value of gross agricultural output/Agricultural land) / (value of gross industrial output / Urban land)\n",
    "\n",
    "This will give a model with 6 parameters that we need to estimate (i.e. 6 unknowns) that we could call $p_0, p_1, p_2, p_3, p_4, p_5$. \n",
    "\n",
    "Note that the final term ($x_5$) requires that we have data for Agricultural and Urban land, which we will need to derive from the remote sensing data for each year of observation.\n",
    "\n",
    "The rest of the data will come from official statistics. An example for the years 1987 to 2000 is given below to give you a feel for the sort of information you will be using:\n",
    "\n",
    "There are many freely available online database for downloading important socio-economic data. Some examples are the Center for International Earth Science Information Network (CIESIN) at Columbia University and the World Bank. The data we will be using here are from the [Guangdong Statistical Yearbook](https://www.stats.gov.cn/sj/ndsj/2023/indexeh.htm).\n",
    "\n",
    "![page](images/page.png)\n",
    "\n",
    "If you installed the R project from github then all you need is within the `docs/files/UrbanModel.zip` file, so no more are required.\n",
    "\n",
    "A file with relevant data has been compiled and can be downloaded [here](files/Guangdong-Yearbook-2019.csv).\n",
    "\n",
    "An example set of classification results are available in the file [results-2019.csv](files/results-2019.csv) that you can also download.\n",
    "\n",
    "Note that the classification results in the file you can download are the lecturers' results and **must not** be used in student analyses (i.e. you must put in your own classification results).\n",
    "\n",
    "You will only be able to run the model for years that you have driving (socioeconomic) and Landsat data."
   ]
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   "source": [
    "## Software\n",
    "\n",
    "To be compatible with the statistics you learn in year 1 (in The Geography Department), we will make use of the language `R`. \n",
    "\n",
    "As a reminder to you, the notes from the relevant 1st year practical are available to you on moodle.\n",
    "\n",
    "If you are using a UCL PC or UCL Geography (linux) system, you can  use the package `Rstudio` for statistics. On the UCL Geography (linux) system, we ccan also use `R` at the command line.\n",
    "\n",
    "**If you are using your own computer**, you will probably need to install software for these. \n",
    "\n",
    "In particular, you need:\n",
    "\n",
    "1. Download and install R from [https://cran.rstudio.com](https://cran.rstudio.com)\n",
    "2. Download and install RStudio from [https://www.rstudio.com](https://www.rstudio.com/products/rstudio/download/#download)\n",
    "\n",
    "**If you wish to use UCL Desktop**, RStudio is already installed. There are a few extra packages needed from the default setup, the code to install them is included below.\n",
    "\n",
    "Make yourself aware of which computer system you are using, and which choice is appropriate for you."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download data files \n",
    "\n",
    "Download the file [Guandong_Yearbook_2019.csv](files/Guangdong-Yearbook-2019.csv) or from and **save it in the area where you are doing your work.** \n",
    "\n",
    "**Remember the location where you are doing your work! (note it down).**\n",
    "\n",
    "You must also copy in a file of your classification results (a .csv file) to the local directory. This might typically be called `results-2019.csv` or a similar name. You will have generated it when performing the classification part of the practical.\n",
    "\n",
    "It should be a `csv` format file, and should contain *at least* columns labelled `year` and `urban_land`:\n",
    "\n",
    "Your file should look something like this:\n",
    "\n",
    "        index,year,urban_land\n",
    "        0,1987.0,511000.0\n",
    "        1,1988.0,520000.0\n",
    "        2,1989.0,595000.0\n",
    "        3,1990.0,656244.0\n",
    "        4,1991.0,725000.0\n",
    "        5,1992.0,780000.0\n",
    "        6,1993.0,842833.0\n",
    "        7,1994.0,938065.0\n",
    "        8,1995.0,1003711.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Using `Rstudio`\n",
    "\n",
    "If you are using `Rstudio`, run the software, and locate your data directory. \n",
    "\n",
    "This must have the files `Guangdong-Yearbook-2019.csv` and `results-2019.csv`\n",
    "\n",
    "![r123](images/r123.png)\n",
    "\n",
    "You should then set this as the 'working directory'\n",
    "\n",
    "\n",
    "![rset1](images/rset1.png)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Using `R`\n",
    "\n",
    "If you are using `R` directly:\n",
    "    \n",
    "    * change directory to where you are doing your work, e.g.:\n",
    "        \n",
    "            cd ~DATA/GEOG0027/gee\n",
    "            \n",
    "you should be able to simply type `R` in the terminal:\n",
    "\n",
    "            R\n",
    "    "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling in R\n",
    "\n",
    "Whether you are using `R` or `Rstudio`, the steps are now the same. In `R`, you type commands at the prompt. In `Rstudio`, you type commands in the `console`.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will develop `R` codes to read and manipulate the data in the cells below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################\n",
    "# First, change directory to where\n",
    "# your (csv) files are \n",
    "############################\n",
    "\n",
    "# change working directory to\n",
    "# where our files are\n",
    "#\n",
    "# Be careful with the setwd command\n",
    "# and check where you are first\n",
    "print('I am in:')\n",
    "print(getwd())\n",
    "\n",
    "# test for this file\n",
    "test = \"Guangdong-Yearbook-2019.csv\"\n",
    "\n",
    "# somewhere else it might be\n",
    "# if its not here \n",
    "# (put something appropriate!!)\n",
    "sub = 'files'\n",
    "\n",
    "if (test %in% list.files('.','*.csv')){\n",
    "    print(paste('found',test))\n",
    "}else if (test %in% list.files(sub,'*.csv')){\n",
    "    print(paste('found',test,'in',sub))\n",
    "    \n",
    "    setwd(sub)\n",
    "    \n",
    "    print('I have moved to:')\n",
    "    print(getwd())\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  index = col_double(),\n",
      "  year = col_double(),\n",
      "  investment = col_double(),\n",
      "  population = col_double(),\n",
      "  private_wage = col_double(),\n",
      "  agr_output = col_double(),\n",
      "  indust_output = col_double(),\n",
      "  agr_pop = col_double(),\n",
      "  avg_wage = col_double()\n",
      ")\n",
      "Parsed with column specification:\n",
      "cols(\n",
      "  index = col_double(),\n",
      "  year = col_double(),\n",
      "  urban_land = col_double()\n",
      ")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"inserting synthetic agricultural land data\"\n",
      "# A tibble: 14 x 4\n",
      "   index  year urban_land agr_land\n",
      "   <dbl> <dbl>      <dbl>    <dbl>\n",
      " 1     0  1987     511000  3229000\n",
      " 2     1  1988     520000  3220000\n",
      " 3     2  1989     595000  3145000\n",
      " 4     3  1990     656244  3083756\n",
      " 5     4  1991     725000  3015000\n",
      " 6     5  1992     780000  2960000\n",
      " 7     6  1993     842833  2897167\n",
      " 8     7  1994     938065  2801935\n",
      " 9     8  1995    1003711  2736289\n",
      "10     9  1996    1122459  2617541\n",
      "11    10  1997    1250000  2490000\n",
      "12    11  1998    1450000  2290000\n",
      "13    12  1999    1650000  2090000\n",
      "14    13  2000    1870000  1870000\n",
      "# A tibble: 14 x 9\n",
      "   index  year investment population private_wage agr_output indust_output\n",
      "   <dbl> <dbl>      <dbl>      <dbl>        <dbl>      <dbl>         <dbl>\n",
      " 1     0  1987       251.      5832.         4.12       349.          878.\n",
      " 2     1  1988       354.      5928.         7.93       474.         1319.\n",
      " 3     2  1989       347.      6025.        13.3        549.         1647.\n",
      " 4     3  1990       381.      6246.        18.3        601.         1902.\n",
      " 5     4  1991       478.      6349.        28.2        655.         2524.\n",
      " 6     5  1992       922.      6463.        40.3        737.         3479.\n",
      " 7     6  1993      1630.      6582.        70.6        899.         5237.\n",
      " 8     7  1994      2141.      6691.       104.        1151.         7274.\n",
      " 9     8  1995      2327.      6789.       151.        1445.         9721.\n",
      "10     9  1996      2328.      6897.       159.        1578.        10531.\n",
      "11    10  1997      2298.      7014.       198.        1656.        12373.\n",
      "12    11  1998      2668.      7116.       264.        1705.        13799.\n",
      "13    12  1999      3028.      7299.       301.        1745.        15303.\n",
      "14    13  2000      3234.      7499.       342.        1701.        16904.\n",
      "# … with 2 more variables: agr_pop <dbl>, avg_wage <dbl>\n"
     ]
    }
   ],
   "source": [
    "############################\n",
    "#\n",
    "# Second, load the datasets:\n",
    "#\n",
    "# stats_file: files/Guangdong-Yearbook-2019.csv\n",
    "# result_file: files/results-2019.csv\n",
    "#\n",
    "# N.B. YOU need to supply your own result_file\n",
    "# DO NOT use the one provided here!!\n",
    "#\n",
    "############################\n",
    "\n",
    "\n",
    "# The name of the file with year, urban_land and possibly agr_land\n",
    "result_file <- 'results-2019.csv'\n",
    "# the name of the supplied data file with Guandong stats\n",
    "stats_file  <- \"Guangdong-Yearbook-2019.csv\"\n",
    "\n",
    "\n",
    "# load library\n",
    "library(readr)\n",
    "\n",
    "# read the datasets\n",
    "Guangdong_Yearbook_2019 <- read_csv(stats_file)\n",
    "input                   <- read_csv(result_file)\n",
    "\n",
    "# fix the input dataset in case arg_land doesnt exist\n",
    "if('agr_land' %in% names(input)){\n",
    "    print(paste('using agricultural land data from',results))\n",
    "}else{\n",
    "    # If no ag land variable, insert one\n",
    "    print('inserting synthetic agricultural land data')\n",
    "    input$agr_land = 2 * max(input$urban_land) - input$urban_land\n",
    "}\n",
    "\n",
    "# The years in Guangdong_Yearbook_2019$year and \n",
    "# input$year need to match\n",
    "# so we force this with the match function in R\n",
    "overlap <- match(Guangdong_Yearbook_2019$year,input$year,nomatch=0)\n",
    "input <- input[overlap,]\n",
    "Guangdong_Yearbook_2019 <- Guangdong_Yearbook_2019[overlap,]\n",
    "\n",
    "# print the datasets to visualise and check it looks ok\n",
    "print(input)\n",
    "print(Guangdong_Yearbook_2019)\n"
   ]
  },
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   "source": [
    "#######\n",
    "#\n",
    "# The above code may fail on UCL Desktop, \n",
    "# or the first time you use the UNIX system.\n",
    "# You may need to run the below to install packages\n",
    "# \n",
    "#######\n",
    "\n",
    "install.packages(c(\"Rcpp\", \"readr\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################\n",
    "#\n",
    "# 3rd, \n",
    "# form a data frame with the X datasets\n",
    "# that we want (look at the formula)\n",
    "# Note the use of input$agr_land \n",
    "# and input$urban_land in here\n",
    "#\n",
    "############################\n",
    "\n",
    "\n",
    "X = data.frame(year=Guangdong_Yearbook_2019$year,\n",
    "               x1=Guangdong_Yearbook_2019$investment/Guangdong_Yearbook_2019$population,\n",
    "               x2=Guangdong_Yearbook_2019$agr_output/Guangdong_Yearbook_2019$agr_pop,\n",
    "#                x3=log(Guangdong_Yearbook_2019$private_wage),\n",
    "               x4=log(Guangdong_Yearbook_2019$avg_wage),\n",
    "#                x5=(Guangdong_Yearbook_2019$agr_output/input$agr_land)/\n",
    "#                   (Guangdong_Yearbook_2019$indust_output/input$urban_land)\n",
    "              )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################\n",
    "#\n",
    "# 4th, our model needs the change in urban area\n",
    "# with resect to year, which we might call du_dy\n",
    "#\n",
    "# to get this, we need to calculate du and dy\n",
    "# i.e. the change ('delta') in urban area and year\n",
    "# \n",
    "# We will pack all of these datasets into a dataframe\n",
    "# called model_data\n",
    "#\n",
    "############################\n",
    "\n",
    "# Calculate the delta in year for observation data\n",
    "# This will mostly be 1, but could be more\n",
    "# if you have missing years\n",
    "dy <- diff(input$year,differences = 1) \n",
    "\n",
    "# Calculate the delta in urban land for observation data\n",
    "du <-  diff(input$urban_land,differences = 1)\n",
    "\n",
    "# The rate of change of urban land per year\n",
    "du_dy =  du / dy\n",
    "\n",
    "# reconstruct the observation year\n",
    "# from the cumulative sum of dyear\n",
    "# This should give a 'year' dataset\n",
    "# with one fewer entry\n",
    "obs_year <- data.frame(year=input$year[c(1)] + cumsum(dy))\n",
    "\n",
    "# Now select all columns of X that match obs_year\n",
    "# and call it model_data\n",
    "overlap <- match(input$year,obs_year$year,nomatch=0)\n",
    "model_data <- X[overlap,]\n",
    "\n",
    "# and add in du_dy and dy to dataset\n",
    "model_data$du_dy <- du_dy\n",
    "model_data$dyear <- dy\n",
    "model_data$urban_land <- input$urban_land[overlap]\n",
    "\n",
    "# save this file\n",
    "write.csv(model_data, file = \"model-2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Missing column names filled in: 'X1' [1]”Parsed with column specification:\n",
      "cols(\n",
      "  X1 = col_double(),\n",
      "  year = col_double(),\n",
      "  x1 = col_double(),\n",
      "  x2 = col_double(),\n",
      "  x3 = col_double(),\n",
      "  x4 = col_double(),\n",
      "  x5 = col_double(),\n",
      "  du_dy = col_double(),\n",
      "  dyear = col_double(),\n",
      "  urban_land = col_double()\n",
      ")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Call:\n",
      "lm(formula = du_dy ~ x1 + x2 + x3 + x4 + x5, data = model_data)\n",
      "\n",
      "Residuals:\n",
      "   Min     1Q Median     3Q    Max \n",
      "-23328 -18491  -3506   7522  38790 \n",
      "\n",
      "Coefficients:\n",
      "            Estimate Std. Error t value Pr(>|t|)\n",
      "(Intercept)  1346051    2575068   0.523    0.617\n",
      "x1             16800     337936   0.050    0.962\n",
      "x2            802897     710485   1.130    0.296\n",
      "x3             72200     103742   0.696    0.509\n",
      "x4           -213235     367635  -0.580    0.580\n",
      "x5           1761121     993690   1.772    0.120\n",
      "\n",
      "Residual standard error: 27580 on 7 degrees of freedom\n",
      "Multiple R-squared:  0.8961,\tAdjusted R-squared:  0.8218 \n",
      "F-statistic: 12.07 on 5 and 7 DF,  p-value: 0.002475\n",
      "\n"
     ]
    }
   ],
   "source": [
    "############################\n",
    "#\n",
    "# 5th, we do the linear modelling:\n",
    "# multi linear fit of du_dy as function of X\n",
    "# using the function lm and the x and y data\n",
    "# stored in model_data\n",
    "#\n",
    "############################\n",
    "\n",
    "\n",
    "# next time, you could skip all of the \n",
    "# above and just load the model_data ...\n",
    "\n",
    "library(readr)\n",
    "model_data <- read_csv(\"model-2019.csv\")\n",
    "\n",
    "fit <- lm(du_dy ~ x1 + x2 + x3 + x4 + x5, data=model_data)\n",
    "\n",
    "print(summary(fit))"
   ]
  },
  {
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   "execution_count": 127,
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lAEXUiQghanPQQQQAABBBCIrUCH2V5KjhZrat2NszbNDIrttSSk4yRIMbqRU9VX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\nhOIuK4bwVKINUBkBBBBAAAEEEECgpwIkSD3lZ+d5ErgnhMLao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r6+q9lznBjUql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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################\n",
    "#\n",
    "# 6th, lets predict the du_dy from\n",
    "# the model, and plot the measured and modelled\n",
    "# values\n",
    "#\n",
    "############################\n",
    "\n",
    "# predict from model\n",
    "model_du_dy <- predict(fit, model_data)\n",
    "\n",
    "plot(model_data$du_dy,model_du_dy\n",
    "     ,xlab='observed du_dy / pixels',\n",
    "     ,ylab='modelled du_dy / pixels') + abline(0,1,col=\"red\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XKxoutHh9tu+u8Kirftk8pKykWKP+9Wy34sqfm9vJ2VBxQvk4YAAggggEAuBKbo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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################\n",
    "#\n",
    "# 6th, lets predict the du_dy from\n",
    "# the model, and plot the measured and modelled\n",
    "# values\n",
    "#\n",
    "############################\n",
    "\n",
    "# Now reconstruct urban area from du_dy\n",
    "modelled <- data.frame(year=model_data$year+dy[c(1)],\n",
    "                       y=model_data$urban_land[c(1)] - model_du_dy[c(1)] + cumsum(model_du_dy))\n",
    "measured <- data.frame(year=model_data$year,\n",
    "                       y=model_data$urban_land)\n",
    "\n",
    "# and plot the urban area measured and modelled\n",
    "plot(modelled$year,modelled$y,pch=0\n",
    "     ,xlab='year',\n",
    "     ,ylab='urban land / pixels') \n",
    "lines(modelled$year,modelled$y,col=\"red\")\n",
    "points(measured$year,measured$y,pch=1) \n",
    "lines(measured$year,measured$y,,col=\"black\")\n",
    "legend(1988, 1800000, legend=c(\"modelled\",\"measured\"),\n",
    "       col=c(\"red\", \"black\"), lty=1:2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>1</dt>\n",
       "\t\t<dd>-21062.0664341169</dd>\n",
       "\t<dt>2</dt>\n",
       "\t\t<dd>38789.8712455914</dd>\n",
       "\t<dt>3</dt>\n",
       "\t\t<dd>3908.36436144725</dd>\n",
       "\t<dt>4</dt>\n",
       "\t\t<dd>-5431.76383748358</dd>\n",
       "\t<dt>5</dt>\n",
       "\t\t<dd>-21424.3020238974</dd>\n",
       "\t<dt>6</dt>\n",
       "\t\t<dd>-3474.84365337407</dd>\n",
       "\t<dt>7</dt>\n",
       "\t\t<dd>28146.5427634795</dd>\n",
       "\t<dt>8</dt>\n",
       "\t\t<dd>-18490.9519028512</dd>\n",
       "\t<dt>9</dt>\n",
       "\t\t<dd>-12560.864062646</dd>\n",
       "\t<dt>10</dt>\n",
       "\t\t<dd>-23327.9740440934</dd>\n",
       "\t<dt>11</dt>\n",
       "\t\t<dd>30912.4809957813</dd>\n",
       "\t<dt>12</dt>\n",
       "\t\t<dd>-3506.26475077546</dd>\n",
       "\t<dt>13</dt>\n",
       "\t\t<dd>7521.77134293876</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[1] -21062.0664341169\n",
       "\\item[2] 38789.8712455914\n",
       "\\item[3] 3908.36436144725\n",
       "\\item[4] -5431.76383748358\n",
       "\\item[5] -21424.3020238974\n",
       "\\item[6] -3474.84365337407\n",
       "\\item[7] 28146.5427634795\n",
       "\\item[8] -18490.9519028512\n",
       "\\item[9] -12560.864062646\n",
       "\\item[10] -23327.9740440934\n",
       "\\item[11] 30912.4809957813\n",
       "\\item[12] -3506.26475077546\n",
       "\\item[13] 7521.77134293876\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "1\n",
       ":   -21062.06643411692\n",
       ":   38789.87124559143\n",
       ":   3908.364361447254\n",
       ":   -5431.763837483585\n",
       ":   -21424.30202389746\n",
       ":   -3474.843653374077\n",
       ":   28146.54276347958\n",
       ":   -18490.95190285129\n",
       ":   -12560.86406264610\n",
       ":   -23327.974044093411\n",
       ":   30912.480995781312\n",
       ":   -3506.2647507754613\n",
       ":   7521.77134293876\n",
       "\n"
      ],
      "text/plain": [
       "         1          2          3          4          5          6          7 \n",
       "-21062.066  38789.871   3908.364  -5431.764 -21424.302  -3474.844  28146.543 \n",
       "         8          9         10         11         12         13 \n",
       "-18490.952 -12560.864 -23327.974  30912.481  -3506.265   7521.771 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit$residuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation\n",
    "\n",
    "The codes above allow you to fit the multi-linear model to the 'observed' values of change in urban area. These observations come from monitoring urban area using satellite (Landsat) data, then claculating the change per year (`du_dy`). \n",
    "\n",
    "There are a few new functions, but most of the functions (including `lm`) should be familiar to you from the stats you did in the 1st year.\n",
    "\n",
    "You should first familiarise yourself with the code blocks, and make sure the code works properly on the dataset you have generated.\n",
    "\n",
    "The *main* result at this point is, in many ways, simply the plots and the statistics generated by `print(summary(fit))` above. But rather than just 'running' this and pasting the results into your report, you will need to demonstrate that you understand what is going on. This means you must provide a full description of what you are doing, along with the `R` code. You should interpret the statistics. Given output such as:\n",
    "\n",
    "    Residual standard error: 27580 on 7 degrees of freedom\n",
    "    Multiple R-squared:  0.8961,\tAdjusted R-squared:  0.8218 \n",
    "    F-statistic: 12.07 on 5 and 7 DF,  p-value: 0.002475\n",
    "    \n",
    "    \n",
    "    Residuals:\n",
    "       Min     1Q Median     3Q    Max \n",
    "    -23328 -18491  -3506   7522  38790 \n",
    "\n",
    "    Coefficients:\n",
    "                Estimate Std. Error t value Pr(>|t|)\n",
    "    (Intercept)  1346051    2575068   0.523    0.617\n",
    "    x1             16800     337936   0.050    0.962\n",
    "    x2            802897     710485   1.130    0.296\n",
    "    x3             72200     103742   0.696    0.509\n",
    "    x4           -213235     367635  -0.580    0.580\n",
    "    x5           1761121     993690   1.772    0.120\n",
    "\n",
    "### The sort of questions you should ask of the data / results\n",
    "\n",
    "What are the numbers we should be looking at to interpret different features of the model? \n",
    "\n",
    "First, let's consider:\n",
    "\n",
    "    What does `Adjusted R-squared` tell us? \n",
    "\n",
    "You may need to look this up to get a formal definition, but you should at least note that it is a measure of goodness of fit of the model to the observations, that has some account taken of the number of model parameters.\n",
    "\n",
    "    Why do you need to take account of the number of parameters?\n",
    "    \n",
    "An interesting question following on from thinking about `Adjusted R-squared` is:\n",
    "\n",
    "    Can we form a model with fewer parameters, but a lower Adjusted R-squared?\n",
    "    \n",
    "An example of this is:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lm(formula = du_dy ~ x1 + x2 + x3 + x4 + x5, data = model_data)\n",
      "[1] 0.8218426\n",
      "lm(formula = du_dy ~ x2 + x3 + 0, data = model_data)\n",
      "[1] 0.9356219\n"
     ]
    }
   ],
   "source": [
    "# original model\n",
    "fit <- lm(du_dy ~ x1 + x2 + x3 + x4 + x5, data=model_data)\n",
    "model_du_dy <- predict(fit, model_data)\n",
    "print(fit$call)\n",
    "print(summary(fit)$adj.r.squared)\n",
    "\n",
    "# lets try another model\n",
    "fit1 <- lm(du_dy ~ x2 + x3 +  0 , data=model_data)\n",
    "model_du_dy1 <- predict(fit1, model_data)\n",
    "print(fit1$call)\n",
    "print(summary(fit1)$adj.r.squared)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the model `du_dy = p2 x2 + p3 x3` is seemingly 'better' than the full model.\n",
    "\n",
    "This suggests that (in this case) rather than simply accept the full model, we should try to develop an improved, reduced model.\n",
    "\n",
    "With different input values, you will likely get different results for the 'best' model, but you will still need to experiment with the model terms to justify a final model choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Additional questions:\n",
    "\n",
    "    What do the parameters tell us? \n",
    "    \n",
    "    What is their 'physical' meaning? (and units)\n",
    "    \n",
    "    Are the paremeter values statistically significant (at some given level)?\n",
    "\n",
    "    What is the standard error of the parameters? \n",
    "\n",
    "    What does `Pr(>|t|)` tell us?\n",
    "\n",
    "    What are the `F-statistic` and `p-value` terms telling us? \n",
    "\n",
    "Relate this to what you can see in any graphs of the data and model. \n",
    "\n",
    "You should also look at residuals (`fit$residuals`) to see if there might be any 'outliers' in your data (e.g. values of urban area that just don't fit the general pattern and are likely to be errors in interpretation of the Landsat data). If you wish to discard any points from the analysis (e.g. outliers) make sure you provide sufficient justification (e.g. show the classified images to demonstrate why a particular data is 'wrong' (a mis-interpretation).\n",
    "\n",
    "### Additonal experiments\n",
    "\n",
    "So long as you have covered the basic requirements, you can aim for higher marks by completing some additional analysis. The amount of extra marks would be a function of the relevance of the experiments (so, explain the relevance) and how well you have executed and described them. \n",
    "\n",
    "One example that you might consider:\n",
    "\n",
    "#### Improved model testing\n",
    "\n",
    "The model we have developed so far used all of the data points for model calibration. The statistics we looked at above can tell us something about the performance and value of the model, but it is often better to seek independent confirmation of model performance.\n",
    "\n",
    "In this case, that would mean, for example, taking the first `N` years of data to calibrate the model, and using the last `M` years to test the model, where `N + M = number of years of data`.\n",
    "\n",
    "Ther code below shows how to set up fitting and testing for sub-datasets (the first `N` years for calibration and the remainder for validation). We leave it to the student to quantify the goodness of fit of the validation data, which should clearly be part of any assessment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################\n",
    "#\n",
    "# We can take a subset of data \n",
    "# from model_data\n",
    "#\n",
    "#########################\n",
    "\n",
    "# a utility function: last + first elements\n",
    "last <- function(x) { tail(x, n = 1) }\n",
    "first <- function(x) { head(x, n = 1) }\n",
    "\n",
    "subber <- function(N,model_data){\n",
    "    \n",
    "    ystart = first(model_data$year)\n",
    "    yend = last(model_data$year)\n",
    "    M = yend - ystart - N\n",
    "    # sub-dataset for year from ystart for count years\n",
    "    train = model_data[model_data$year %in% seq(ystart,ystart+N-1),]\n",
    "    test  = model_data[model_data$year %in% seq(ystart+N+1,yend),]\n",
    "    #########################\n",
    "    #\n",
    "    # train\n",
    "    #\n",
    "    #########################\n",
    "    fits <- lm(du_dy ~ x1 + x2 + x3 + x4 + x5,train)\n",
    "    \n",
    "    # or another model?\n",
    "    # fits <- lm(du_dy ~  x2  ,train)\n",
    "\n",
    "    my_list <- list('train'=train,'test'=test,'fits'=fits)\n",
    "    return(my_list)\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
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    },
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4lOUFyg8PEnKZJSmQAky0cz7dFON1bs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\nrXFkLS0W6LOew5UY3Vmwwm8etP53b2O2osWbYHUIIIAAAggggAACCCAQUwF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      "text/plain": [
       "Plot with title “Calibration and validation”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# N needs to be *at least* the number\n",
    "# of parameters + 1 and more like 2 x\n",
    "# the number of parameters or more\n",
    "N = 9\n",
    "# get subset\n",
    "sub <- subber(N,model_data)\n",
    "\n",
    "# training\n",
    "train_du_dy <- predict(sub$fits, sub$train)\n",
    "\n",
    "# validation\n",
    "test_du_dy <- predict(sub$fits, sub$test)\n",
    "\n",
    "# max value for plotting\n",
    "m = max(c(train_du_dy,test_du_dy,sub$train$du_dy,sub$test$du_dy))\n",
    "\n",
    "# plotting in R\n",
    "plot(sub$train$du_dy,train_du_dy\n",
    "     ,xlim=c(0,m),ylim=c(0,m)\n",
    "     ,xlab='observed du_dy / pixels',\n",
    "     ,ylab='modelled du_dy / pixels') + abline(0,1,col=\"red\")\n",
    "title('Calibration and validation')\n",
    "points(sub$test$du_dy,test_du_dy,col='green',pch=2)\n",
    "legend(0, m*4/6, legend=c(\"calibration\",\"validation\"),\n",
    "       col=c( \"black\", 'green'),pch=c(1,2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should note the validation behaviour as you increase/decrease `N`.\n",
    "\n",
    "It is also important for you to understand what happens at low `N`: \n",
    "\n",
    "    If `N` is less than the number of model parameters, what happens?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this section, we have phrased the linear model of Seto et al. within `R`'s `lm` function:\n",
    "\n",
    "    du_dy ~ x1 + x2 + x3 + x4 + x5\n",
    "    \n",
    "The model relates socio-economic variables (constant, plus `x1, x2, x3, x4, x5`), weighted by model parameters (`p0, p1, p2, p3, p4, p5`) to predict the rate if change of urban area per year (`du_dy`).\n",
    "\n",
    "We have taken a set of observations of `du_dy`, derived from Landsat land cover classifications for the years 1986 to present. Along with estimates of the `x` variables from the Guangdong yearbook, we have then seen how to produce an esrimate of the model parameters (the `p` terms).\n",
    "\n",
    "This  forms the basis of the modelling section of this coursework: As noted above, you need to perform a model calibration, plot results, and describe **and interpret** summary statistics. Your interpretation of the statistics is vital here as it will show your understanding of the terms printed. Your plots should be neatly done, with full axis labelling, titles etc, noting any units or scaling factors used.\n",
    "\n",
    "We have given you a set of questions to help guide your statistical interpretation.\n",
    "\n",
    "You are then required to see if you can come up with a model with fewer parameters. The original model has 6 parameters, but it could well be the case that we can develop a more robust model with fewer parameters. One way we can judge 'better' here is to take a measure of goodness of fit that accounts for the model degrees of freedom: 'better' then is a balance of these things. \n",
    "\n",
    "You are free to perform additional experiment, with the expectation of higher marks, provided (i) you have done the basic requirements well enough, and (ii) you show clarity of thoiught and understanding of what you are doing in your experiments."
   ]
  }
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