{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week 8: Unrest via Sparse Credit Card Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Contains the data from the paper being analysed\n",
    "data = pd.read_excel('A1_data.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction and Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of this report is to re-analyse the data presented in the paper by Dong et al.<sup>1</sup>, which investigates the effect that protests (as an example of disruptive social behaviours in general) have on consumer behaviours. Dong et al.<sup>1</sup> hypothesise that protests decrease consumer behaviour in the surrounding area of the event, and suggest that consumer spending could be used as an additional non-traditional economic indicator and as a gauge of consumer sentiment. Consumer spending was analysed using credit card transaction data from a metropolitan area within a country that is part of The Organisation for Economic Co-operation and Development (OECD). Although Dong et al.<sup>1</sup> investigate temporal and spatial effects on consumer spending, for the purposes of this analysis, only the spatial effect of variables (with relation to the geographical distance from the event) is considered. The dataset consists of variables measured as a function of the distance from the event (in km), including: the number of customers, the median spending amount, the number of transactions, and the total sales amount.\n",
    "\n",
    "The re-analysis is conducted on the data provided in the paper<sup>1</sup>, using Python in conjunction with packages such as pandas, matplotlib, numpy and seaborn, to process and visualise the data. As aformentioned, only spatial data and the variables mentioned above are considered, for the reference days and the change occuring Day 62 (day of first socially disruptive event). The distribution of the difference between the reference period and Day 62 is visualised by plotting a histogram for each variable. Since the decrease of each the variables from the reference period to Day 62 is provided, the mean and the median of these distributions can be used to perform a one-sample (as we have are given the difference) hypothesis test to assess whether the protests on Day 62 had a discernable effect.\n",
    "\n",
    "Assuming the mean of each variable over the reference period is the midpoint between their respective maximum and minimum values, we can reconstruct approximate actual values for Day 62 (given the decrease in value on Day 62 from the reference period). By comparing these value to the range over the reference period, another assessment can be made to determine whether the data presents a discernible effect on consumer spending as a result of social discuption, scaling with distance.\n",
    "\n",
    "Although time series data was not explicitely provided, by extrapolating information from a graph in Dong et al.<sup>1</sup> we can quantify the decrease in number of customers and median spending on Day 62 using information about the reference days (from 43 to 61). After collecting the values for each of the reference days (43-61), the mean and standard deviation of this sample can be calculated. Assuming a normal distribution of the data, we can calculate a z-score for each observation on Day 62, and use this to assess the original hypothesis.\n",
    "\n",
    "By performing each of the above test, a re-analysis will be conducted on Dong et al.<sup>1</sup>'s paper hypothesising that consumer spending decreases as a result of social events such as protests. In the Results section, we will perform the statistical analyses described above. The results of these tests will then be explored in the Discussion section, along with assumptions and limitations of the tests and what can be conclused from them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the variables in the given data (number of customers, median spending amount, number of transactions, and sales totals) we construct a histogram of the decrease of each (on Day 62). We then compute the mean and median of the data so we can proceed to perform a one-sample hypothesis test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75681c2cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"cust_dec_62\"].mean(), data[\"cust_dec_62\"].median())\n",
    "spend_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"spend_dec_62\"].mean(), data[\"spend_dec_62\"].median())\n",
    "trans_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"trans_dec_62\"].mean(), data[\"trans_dec_62\"].median())\n",
    "sales_mm = 'Mean decrease: {:.2f} \\nMedian decrease: {:.2f}'.format(data[\"sales_dec_62\"].mean(), data[\"sales_dec_62\"].median())\n",
    "\n",
    "fig1 = plt.figure()\n",
    "fig1.set_figheight(8)\n",
    "fig1.set_figwidth(10)\n",
    "\n",
    "plt.subplot(221)\n",
    "cust_dist = sns.distplot(data[\"cust_dec_62\"], kde = True, label=cust_mm)\n",
    "cust_dist.set(xlabel='Decrease in customers', ylabel='Normalised count', title='Distribution of customers')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(222)\n",
    "spend_dist = sns.distplot(data[\"spend_dec_62\"], kde = True, label=spend_mm)\n",
    "spend_dist.set(xlabel='Decrease in median spending', ylabel='Normalised spending', title='Distribution of median spending')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(223)\n",
    "trans_dist = sns.distplot(data[\"trans_dec_62\"], kde = True, label=trans_mm)\n",
    "trans_dist.set(xlabel='Decrease in number of transactions', ylabel='Normalised number of transactions', title='Distribution of number of transactions')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(224)\n",
    "sales_dist = sns.distplot(data[\"sales_dec_62\"], kde = True, label=sales_mm)\n",
    "sales_dist.set(xlabel='Decrease in total sales amount', ylabel='Normalised sales', title='Distribution of total sales amount')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout(pad=0.4, w_pad=0.6, h_pad=1.0)\n",
    "fig1.text(0.5,-0.05,\n",
    "          \"Figure 1: Distribution of each of the variables recorded in the data, as a function of the distance from an event\",\n",
    "          size=12, ha=\"center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a mean/median of the reference period, obtained by taking the midpoint of the minimum and maximum values over for each distance measure, a value can be reconstructed for the measurement on Day 62 (for each location) using:\n",
    "\n",
    "\\begin{equation}\n",
    "\\textrm{value} = \\frac{\\textrm{min} + \\text{max}}{2} - \\textrm{decrease.}\n",
    "\\tag{1}\n",
    "\\end{equation}\n",
    "\n",
    "We can then plot the maximum and minimum values for the reference period, as well as the reconstructed Day 62 variables to observe the behaviour of consumer spending after the event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = data[\"distance\"]\n",
    "\n",
    "cust_min = data[\"cust_ref_min\"]\n",
    "cust_max = data[\"cust_ref_max\"]\n",
    "cust_recon = data[\"recon_cust_val\"]\n",
    "\n",
    "spend_min = data[\"spend_ref_min\"]\n",
    "spend_max = data[\"spend_ref_max\"]\n",
    "spend_recon = data[\"recon_spend_val\"]\n",
    "\n",
    "trans_min = data[\"trans_ref_min\"]\n",
    "trans_max = data[\"trans_ref_max\"]\n",
    "trans_recon = data[\"recon_trans_val\"]\n",
    "\n",
    "sales_min = data[\"sales_ref_min\"]\n",
    "sales_max = data[\"sales_ref_max\"]\n",
    "sales_recon = data[\"recon_sales_val\"]\n",
    "\n",
    "fig1 = plt.figure()\n",
    "fig1.set_figheight(12)\n",
    "fig1.set_figwidth(12)\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(dist, cust_min, label='Min. # of Customers (Ref.)')\n",
    "plt.plot(dist, cust_max, label='Max. # of Customers (Ref.)')\n",
    "plt.plot(dist, cust_recon, label='Day 62 # of Customers')\n",
    "plt.xlabel('Distance from event (km)')\n",
    "plt.ylabel('Customers')\n",
    "plt.title('Effect on Number of Customers on Day 62')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(412)\n",
    "plt.plot(dist, spend_min, label='Min. Median Spending Amt.(Ref.)')\n",
    "plt.plot(dist, spend_max, label='Max. Median Spending Amt. (Ref.)')\n",
    "plt.plot(dist, spend_recon, label='Day 62 Median Spending Amt.')\n",
    "plt.xlabel('Distance from event (km)')\n",
    "plt.ylabel('Median Spending Amount')\n",
    "plt.title('Effect on Median Spending on Day 62')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(413)\n",
    "plt.plot(dist, trans_min, label='Min. # of Transactions (Ref.)')\n",
    "plt.plot(dist, trans_max, label='Max. # of Transactions (Ref.)')\n",
    "plt.plot(dist, trans_recon, label='Day 62 # of Transactions')\n",
    "plt.xlabel('Distance from event (km)')\n",
    "plt.ylabel('Transactions')\n",
    "plt.title('Effect on Number of Transactions on Day 62')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(414)\n",
    "plt.plot(dist, sales_min, label='Min. Sales Amt. (Ref.)')\n",
    "plt.plot(dist, sales_max, label='Max. Sales Amt. (Ref.)')\n",
    "plt.plot(dist, sales_recon, label='Day 62 Sales Amt.')\n",
    "plt.xlabel('Distance from event (km)')\n",
    "plt.ylabel('Total Sales Amt.')\n",
    "plt.title('Effect on Total Sales Amount on Day 62')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout(pad=0.4, w_pad=0.6, h_pad=1.0)\n",
    "fig1.text(0.5,-0.05,\n",
    "          \"Figure 2: The reconstructed values for Day 62 of each variable plotted against their respective minimums and\\n maximums over the reference period\",\n",
    "          size=12, ha=\"center\",transform=ax1.transAxes)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the data recorded, for each of the three distance recorded, the mean and standard deviation of the reference period can be calculated. The z-score for each observed value on Day 62 can be computed using:\n",
    "\n",
    "\\begin{equation}\n",
    "\\textrm{Z} = \\frac{\\textrm{X} - \\mu}{\\sigma},\n",
    "\\tag{2}\n",
    "\\end{equation}\n",
    "\n",
    "where X is the observed value, \\mu and \\sigma are the mean and standard deviation (respectively) of the reference period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing all the z scores\n",
    "z_cust_r1 = (data[\"r1_cust_62\"][0] - data[\"r1_cust_change\"].mean())/data[\"r1_cust_change\"].std()\n",
    "z_cust_r2 = (data[\"r2_cust_62\"][0] - data[\"r2_cust_change\"].mean())/data[\"r2_cust_change\"].std()\n",
    "z_cust_r3 = (data[\"r3_cust_62\"][0] - data[\"r3_cust_change\"].mean())/data[\"r3_cust_change\"].std()\n",
    "z_spend_r1 = (data[\"r1_spend_62\"][0] - data[\"r1_spend_change\"].mean())/data[\"r1_spend_change\"].std()\n",
    "z_spend_r2 = (data[\"r2_spend_62\"][0] - data[\"r2_spend_change\"].mean())/data[\"r2_spend_change\"].std()\n",
    "z_spend_r3 = (data[\"r3_spend_62\"][0] - data[\"r3_spend_change\"].mean())/data[\"r3_spend_change\"].std()\n",
    "\n",
    "print(tabulate([['Customers', '< 2km', data[\"r1_cust_62\"][0], z_cust_r1],\n",
    "                ['Customers', '2km - 4km', data[\"r2_cust_62\"][0], z_cust_r2],\n",
    "                ['Customers', '> 4km', data[\"r3_cust_62\"][0], z_cust_r3],\n",
    "                ['Median Spending', '< 2km', data[\"r1_spend_62\"][0], z_spend_r1],\n",
    "                ['Median Spending', '2km - 4km', data[\"r2_spend_62\"][0], z_spend_r2],\n",
    "                ['Median Spending', '> 4km', data[\"r3_spend_62\"][0], z_spend_r3]],\n",
    "               headers=['Variable', 'Distance', 'X', 'Z']))\n",
    "print(\"\\nFigure 3: The z score computed using equation 2 and the temporal data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As shown in each of the subplots of Figure 1, the mean and median values of the decrease in each of the distributions are greater than zero (note: higher values of the decrease variable indicate a larger decrease/negative change). These mean and median values can be used to perform a one-sample hypothesis tests, which finds that since each of the mean/median values is greater than zero, we can infer that the event had a net decreasing affect on the number of customers, median spending amount, number of transactions, and total sales amount.\n",
    "\n",
    "In Figure 2 values were approximated for each variable on Day 62, using Equation 1, and plotted against the minimum and maximum values of the respective variables. This allows us to visually assess whether the reconstructed value for Day 62 lies outside the range of recorded values for the reference period, and presents uncharacteristic behaviour. A decrease is evident in each of the variables after the event has occured (on Day 62) within a distance of approximately 2 km, and appears to stabilise thereafter. This provides support to the authors'<sup>1</sup> hypothesis that consumer spending is affected by socially disruptive events, and also provides evidence to the notion of spatial scaling of this effect (based on the event location). It is important to note that the approximation used in this technique is subject to a level of error due to the ideal calculation of the mean/median of the reference data as the midpoint between the minimum and maximum values provided.\n",
    "\n",
    "Extrapolating data from a graph in Dong et al.<sup>1</sup> provided time series data (divided into three radius') to analyse. This data was collected by visually estimating the values from the graph which will inherently introduce a source of error. However, by computing the z-score as described in Equation 2, the table provided in Figure 3 was constructed. Each of the z-score values in the table are negative, indicating a decrease in both the number of customers and median spending on Day 62. The much larger magnitude of z-scores for the <2km distance ring for both variables is in agreement with earlier discussion, strengthening the hypothesis of the spatial correlation of consumer spending.\n",
    "\n",
    "Each of the above tests have agreed on the spatial and temporal correlation of consumer spending and socially disruptive events. With the limited data available, we can therefore concur with the hypothesis of Dong et al. that consumer spending decreases in the area around disruptive social behaviour, after finding the temporal correlation on Day 62, as well as the spatially decreasing effect further from the event. However, the original study lacks robustness (with only credit card data from two events in the same geographic area), and generalisability (only one type of disruptive behaviour was studied)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "1. Dong, X., Meyer, J., Shmueli, E., Bozkaya, B., & Pentland, A. (2018). Methods for quantifying effects of social unrest using credit card transaction data. EPJ Data Science, 7(1), 8. https://doi.org/10.1140/epjds/s13688-018-0136-x"
   ]
  }
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