improved markdown

notebooks/02_baseline_network_analysis.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Analysis of baseline mobility network\n",
     "\n",
-    "We analyse the properties of the directed, weighted baseline UK mobility network and we obtain multiscale flow communities with Markov Stability analysis."
+    "We analyse the properties of the directed, weighted baseline UK mobility network, and we obtain multiscale flow communities with Markov Stability analysis."
    ]
   },
   {
@@ -356,7 +356,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Corellations of $\\pi$ with other quantities"
+    "### Correlations of $\\pi$ with other quantities"
    ]
   },
   {
@@ -683,7 +683,7 @@
    "source": [
     "## Markov Stability (MS) analysis\n",
     "\n",
-    "We perform MS analyis on a server using the python script `src/ms_analysis.py`"
+    "We perform MS analysis on a server using the python script `src/ms_analysis.py`"
    ]
   },
   {

notebooks/03_tiles_to_nuts.ipynb

@@ -141,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For each region within the geographic map file, identify the tiles intersecting with the region, and their area. As the areas are exhaustive with repsect to the country or region of interest, the total tile area covered can be taken as the total 'landed' area of the tile.\n",
+    "For each region within the geographic map file, identify the tiles intersecting with the region, and their area. As the areas are exhaustive with respect to the country or region of interest, the total tile area covered can be taken as the total 'landed' area of the tile.\n",
     "\n",
     "There is a little limitation here. For land borders, we do not know how much of a tile's activity is from either side of the border. This way we take it as all falling within the area of interest. It's an edge case and probably unimportant\n",
     "\n",

notebooks/04_compare_ms_nuts_baseline.ipynb

@@ -5,9 +5,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Comparison of MS partitions and NUTS at baseline\n",
+    "# Comparing the intrinsic mobility scales at baseline to administrative NUTS regions\n",
     "\n",
-    "We compare MS partitions with NUTS partitions of the UK and first establish correspondences between MS and NUTS on three levels of hierarchy, fine, medium and coarse. Then we evalute the partitions with the Coverage and Nodal Containment measures."
+    "We compare MS partitions with NUTS partitions of the UK and first establish correspondences between MS and NUTS on three levels of hierarchy, fine, medium and coarse. Then we evaluate the partitions with the Coverage and Nodal Containment measures."
    ]
   },
   {

notebooks/06_tiles_to_ttwa.ipynb → notebooks/05_tiles_to_ttwa.ipynb

@@ -5,9 +5,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Mapping Bing Maps tiles used in the Facebook Movement Maps to TTWA's\n",
+    "# Mapping Bing Maps tiles used in the Facebook Movement Maps to TTWAs\n",
     "\n",
-    "We map the Bing Maps tiles given by quadkeys to TTWA's: https://geoportal.statistics.gov.uk/datasets/ons::ttwa-dec-2001-super-generalised-clipped-boundaries-in-the-uk/explore?location=56.236636%2C-0.935209%2C6.46"
+    "We map the Bing Maps tiles given by quadkeys to the UK Travel to Work Areas (TTWAs): https://geoportal.statistics.gov.uk/datasets/ons::ttwa-dec-2001-super-generalised-clipped-boundaries-in-the-uk/explore?location=56.236636%2C-0.935209%2C6.46"
    ]
   },
   {
@@ -142,7 +142,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For each region within the geographic map file, identify the tiles intersecting with the region, and their area. As the areas are exhaustive with repsect to the country or region of interest, the total tile area covered can be taken as the total 'landed' area of the tile.\n",
+    "For each region within the geographic map file, identify the tiles intersecting with the region, and their area. As the areas are exhaustive with respect to the country or region of interest, the total tile area covered can be taken as the total 'landed' area of the tile.\n",
     "\n",
     "There is a little limitation here. For land borders, we do not know how much of a tile's activity is from either side of the border. This way we take it as all falling within the area of interest. It's an edge case and probably unimportant\n",
     "\n",

notebooks/07_compare_ms_ttwa_baseline.ipynb → notebooks/06_compare_ms_ttwa_baseline.ipynb

@@ -5,9 +5,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Comparison of MS partitions and NUTS at baseline\n",
+    "# Comparing the fine mobility scale at baseline to labour-related Travel to Work Areas (TTWAs)\n",
     "\n",
-    "We compare MS partitions with NUTS partitions of the UK and first establish correspondences between MS and NUTS on three levels of hierarchy, fine, medium and coarse. Then we evalute the partitions with the Coverage and Nodal Containment measures."
+    "The MS flow communities in the fine scale partition match well the UK Travel to Work Areas (TTWAs) but also capture mobility patterns beyond commuting to work"
    ]
   },
   {

notebooks/05_response_ms_partitions.ipynb → notebooks/07_response_ms_partitions.ipynb

@@ -5,9 +5,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Comparison of MS partitions and NUTS over time\n",
+    "# The contraction of UK mobility under lockdown and its relation to the baseline mobility multiscale network\n",
     "\n",
-    "We compare the coverage of MS and NUTS at partition and community level over the course of the first UK lockdown."
+    "We examine the evolution of mobility under lockdown, and show that mobility first reverted towards fine scale flow communities already found in the pre-lockdown data, and then expanded back towards coarser flow communities as restrictions were lifted. The improved coverage induced by lockdown is well captured by a linear decay shock model, which allows us to quantify regional differences both in the strength of the effect and the recovery time from the lockdown shock."
    ]
   },
   {