Update for clarity

Author: turukawa

Diff for: COVID-19 Impact on Structural Occupation and Labour Force Distribution.ipynb

@@ -4,19 +4,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Start of analysis\n",
+    "# COVID-19 Impact on Structural Occupation and Labour Force Distribution\n",
     "\n",
-    "Calculate whether each geocode is a net loser or gainer of employment.\n",
+    "I have spent the last four years building https://sqwyre.com, which is an open data commercial location data explorer. Every quarter, we update (partially via FOI) all local authorities in England and Wales.\n",
     "\n",
-    "If loser: \n",
+    "These data give me the current occupation status of all commercial properties, and – derived from that – that m2 per employee which permits this calculation to work:\n",
     "\n",
-    "- Categories lost in m2 and units\n",
+    "- Sqwyre gives me the total actively-used floor area of commercial property in each Standard Industry Code (SIC);\n",
+    "- ONS publish employment data by place-of-work;\n",
+    "- Divide the one by the other and … we have m2/job;\n",
     "\n",
-    "If gainer:\n",
+    "However, ONS publish employment by both place-of-work and place-of-residence. The difference – bluntly and fuzzily – is an indication of those who commute from home to work outside of their local authority: \n",
     "\n",
-    "- Categories required in m2 and units\n",
+    "- [Earnings and hours worked, place of residence by local authority: ASHE Table 8](https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/placeofresidencebylocalauthorityashetable8)\n",
+    "- [Earnings and hours worked, place of work by local authority: ASHE Table 7](https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/placeofworkbylocalauthorityashetable7)\n",
     "\n",
-    "First get winners/losers, then calculate ratio of employment"
+    "Combine these and apply, and you get the dataseries I developed. Run a scenario – say 10% of those currently commuting stop doing so, set the proportions by their current area of occupation (Retail, Office, Leisure, Industrial) – and you have a rough guide to the implications."
    ]
   },
   {
@@ -40,9 +43,9 @@
     "#from tqdm import tqdm\n",
     "#https://stackoverflow.com/questions/42855972/printing-on-the-same-line-on-a-jupyter-notebook\n",
     "\n",
-    "dfe = pd.read_csv(\"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/employment_by_place_of_residence.csv\")\n",
-    "dfs = pd.read_csv(\"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/report_summary.csv\")\n",
-    "dfa = pd.read_csv(\"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/report_summary_all_quarters.csv\")"
+    "dfe = pd.read_csv(\"~/Custom_draws/employment_by_place_of_residence.csv\")\n",
+    "dfs = pd.read_csv(\"~/Custom_draws/report_summary.csv\")\n",
+    "dfa = pd.read_csv(\"~/Custom_draws/report_summary_all_quarters.csv\")"
    ]
   },
   {
@@ -865,7 +868,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df.to_excel(\"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/covid-19-impact.xlsx\", index=False)"
+    "df.to_excel(\"~/Custom_draws/covid-19-impact.xlsx\", index=False)"
    ]
   },
   {
@@ -955,7 +958,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_excel(\"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/covid-19-impact.xlsx\")"
+    "df = pd.read_excel(\"~/covid-19-impact.xlsx\")"
    ]
   },
   {
@@ -977,7 +980,7 @@
     "\n",
     "google_source = \"https://www.google.com/covid19/mobility/\"\n",
     "google_url = \"https://www.gstatic.com/covid19/mobility/Global_Mobility_Report.csv\"\n",
-    "google_file = \"C:/Users/Turukawa/OneDrive/Whythawk/Projects/Pikhaya/Custom_draws/google-mobility-report.csv\"\n",
+    "google_file = \"~/google-mobility-report.csv\"\n",
     "\n",
     "clr = {\n",
     "    \"red\": \"#D33826\",\n",