Repository: usds/justice40-tool (language: python)

Issue:
Census tracts surrounded by DACs should also be considered DACs
This issue seeks to address "donut holes" in our DAC map. This will involve the following:
1. For all census tracts that have at least one DAC neighbor, calculate the share of neighbors that are DACs
2. Over a certain threshold, those tracts will also become DACs

In analysis, I did this analysis first within state, and then, nationally for all tracts with 50% or more DAC neighbors within state. 

Some caveats:
1. First, we will have to move up some of the geocoding / reading geojson work from `etl_score_geo` to `etl_score`, since there are now parts of the score that depends on neighboring tracts
2. The exact neighbor share used depends on final analysis; we will start with a threshold of 85%


Discussion / hints:
Waiting for CEQ approval to implement this

Fix patch:
diff --git a/data/data-pipeline/data_pipeline/content/config/csv.yml b/data/data-pipeline/data_pipeline/content/config/csv.yml
index 4f074df58..1d70cc7bb 100644
--- a/data/data-pipeline/data_pipeline/content/config/csv.yml
+++ b/data/data-pipeline/data_pipeline/content/config/csv.yml
@@ -20,9 +20,21 @@ fields:
   - score_name: Total categories exceeded
     label: Total categories exceeded
     format: int64
-  - score_name: Definition M (communities)
+  - score_name: Definition N (communities)
     label: Identified as disadvantaged
     format: bool
+  - score_name: Definition N (communities) (including adjacency index)
+    label: Identified as disadvantaged (including adjacency index)
+    format: bool
+  - score_name: Is the tract surrounded by disadvantaged communities?
+    label: Is the tract surrounded by disadvantaged communities?
+    format: bool
+  - score_name: Meets the less stringent low income criterion for the adjacency index?
+    label: Meets the less stringent low income criterion for the adjacency index?
+    format: bool
+  - score_name: Definition N (communities) (average of neighbors)
+    label: Share of neighbors that are identified as disadvantaged
+    format: percentage
   - score_name: Total population
     label: Total population
     format: float
diff --git a/data/data-pipeline/data_pipeline/content/config/excel.yml b/data/data-pipeline/data_pipeline/content/config/excel.yml
index 0659eddb3..5ef93582f 100644
--- a/data/data-pipeline/data_pipeline/content/config/excel.yml
+++ b/data/data-pipeline/data_pipeline/content/config/excel.yml
@@ -24,9 +24,21 @@ sheets:
       - score_name: Total categories exceeded
         label: Total categories exceeded
         format: int64
-      - score_name: Definition M (communities)
+      - score_name: Definition N (communities)
         label: Identified as disadvantaged
         format: bool
+      - score_name: Definition N (communities) (including adjacency index)
+        label: Identified as disadvantaged (including adjacency index)
+        format: bool
+      - score_name: Is the tract surrounded by disadvantaged communities?
+        label: Is the tract surrounded by disadvantaged communities?
+        format: bool
+      - score_name: Meets the less stringent low income criterion for the adjacency index?
+        label: Meets the less stringent low income criterion for the adjacency index?
+        format: bool
+      - score_name: Definition N (communities) (average of neighbors)
+        label: Share of neighbors that are identified as disadvantaged
+        format: percentage
       - score_name: Total population
         label: Total population
         format: float
@@ -314,5 +326,4 @@ sheets:
         format: percentage
       - score_name: Does the tract have at least 35 acres in it?
         label: Does the tract have at least 35 acres in it?
-        format: bool
-      
\ No newline at end of file
+        format: bool
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py
index 50e523cf2..512c8822a 100644
--- a/data/data-pipeline/data_pipeline/etl/score/constants.py
+++ b/data/data-pipeline/data_pipeline/etl/score/constants.py
@@ -207,7 +207,8 @@
     field_names.M_POLLUTION: "M_PLN",
     field_names.M_HEALTH: "M_HLTH",
     # temporarily update this so that it's the Narwhal score that gets visualized on the map
-    field_names.SCORE_N_COMMUNITIES: "SM_C",
+    # The NEW final score value INCLUDES the adjacency index.
+    field_names.SCORE_N_COMMUNITIES + field_names.ADJACENT_MEAN_SUFFIX: "SM_C",
     field_names.SCORE_N_COMMUNITIES
     + field_names.PERCENTILE_FIELD_SUFFIX: "SM_PFS",
     field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD: "EPLRLI",
@@ -305,6 +306,9 @@
     + field_names.PERCENTILE_FIELD_SUFFIX: "WF_PFS",
     field_names.HIGH_FUTURE_FLOOD_RISK_FIELD: "FLD_ET",
     field_names.HIGH_FUTURE_WILDFIRE_RISK_FIELD: "WF_ET",
+    field_names.ADJACENT_TRACT_SCORE_ABOVE_DONUT_THRESHOLD: "ADJ_ET",
+    field_names.SCORE_N_COMMUNITIES
+    + field_names.ADJACENCY_INDEX_SUFFIX: "ADJ_PFS",
     field_names.TRACT_PERCENT_NON_NATURAL_FIELD_NAME
     + field_names.PERCENTILE_FIELD_SUFFIX: "IS_PFS",
     field_names.NON_NATURAL_LOW_INCOME_FIELD_NAME: "IS_ET",
@@ -364,6 +368,7 @@
     field_names.FUTURE_FLOOD_RISK_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
     field_names.FUTURE_WILDFIRE_RISK_FIELD
     + field_names.PERCENTILE_FIELD_SUFFIX,
+    field_names.SCORE_N_COMMUNITIES + field_names.ADJACENCY_INDEX_SUFFIX,
     field_names.TRACT_PERCENT_NON_NATURAL_FIELD_NAME
     + field_names.PERCENTILE_FIELD_SUFFIX,
 ]
diff --git a/data/data-pipeline/data_pipeline/ipython/explore_adjacency.ipynb b/data/data-pipeline/data_pipeline/ipython/explore_adjacency.ipynb
new file mode 100644
index 000000000..e0e5191ca
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/ipython/explore_adjacency.ipynb
@@ -0,0 +1,715 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8d2dd0fc-5bd6-4d28-9ef3-f7cf7403f0be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join(\"../..\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from data_pipeline.config import settings\n",
+    "from data_pipeline.etl.sources.geo_utils import add_tracts_for_geometries, get_tract_geojson\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f36fe7ef-2717-48be-ae94-a3f0aa33acc1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext lab_black"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "207209d1-f95a-4b84-bc34-d37235a6dab4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-08-17 11:45:46,587 [data_pipeline.etl.sources.geo_utils] INFO     Loading tract geometry data from census ETL\n",
+      "2022-08-17 11:45:46,588 [data_pipeline.etl.sources.geo_utils] DEBUG    Loading existing tract geojson\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = get_tract_geojson()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a6cd76db-9dff-4e18-aa26-bab9a841ce1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "center = \"6030.04\"\n",
+    "center_GEOID10_TRACT = \"24027603004\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ce9487d8-5e27-4d07-8f9e-467ac7cdf7e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10</th>\n",
+       "      <th>COUNTYFP10</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>NAME10</th>\n",
+       "      <th>NAMELSAD10</th>\n",
+       "      <th>MTFCC10</th>\n",
+       "      <th>FUNCSTAT10</th>\n",
+       "      <th>ALAND10</th>\n",
+       "      <th>AWATER10</th>\n",
+       "      <th>INTPTLAT10</th>\n",
+       "      <th>INTPTLON10</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>53415</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>602100</td>\n",
+       "      <td>24027602100</td>\n",
+       "      <td>6021</td>\n",
+       "      <td>Census Tract 6021</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13769934</td>\n",
+       "      <td>3674</td>\n",
+       "      <td>+39.3076905</td>\n",
+       "      <td>-076.8349752</td>\n",
+       "      <td>POLYGON ((-76.86305 39.31484, -76.86308 39.315...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53416</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>602303</td>\n",
+       "      <td>24027602303</td>\n",
+       "      <td>6023.03</td>\n",
+       "      <td>Census Tract 6023.03</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11740756</td>\n",
+       "      <td>26901</td>\n",
+       "      <td>+39.2600506</td>\n",
+       "      <td>-076.8754102</td>\n",
+       "      <td>POLYGON ((-76.86750 39.25170, -76.86754 39.251...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53424</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>605503</td>\n",
+       "      <td>24027605503</td>\n",
+       "      <td>6055.03</td>\n",
+       "      <td>Census Tract 6055.03</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3158645</td>\n",
+       "      <td>8444</td>\n",
+       "      <td>+39.2274469</td>\n",
+       "      <td>-076.8803625</td>\n",
+       "      <td>POLYGON ((-76.88405 39.23543, -76.88398 39.235...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53429</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>605502</td>\n",
+       "      <td>24027605502</td>\n",
+       "      <td>6055.02</td>\n",
+       "      <td>Census Tract 6055.02</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5260984</td>\n",
+       "      <td>16405</td>\n",
+       "      <td>+39.2257617</td>\n",
+       "      <td>-076.9054701</td>\n",
+       "      <td>POLYGON ((-76.90881 39.21739, -76.90882 39.217...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53452</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>603004</td>\n",
+       "      <td>24027603004</td>\n",
+       "      <td>6030.04</td>\n",
+       "      <td>Census Tract 6030.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>44425097</td>\n",
+       "      <td>99882</td>\n",
+       "      <td>+39.2817022</td>\n",
+       "      <td>-076.9188315</td>\n",
+       "      <td>POLYGON ((-76.98539 39.26610, -76.98557 39.266...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53453</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>605104</td>\n",
+       "      <td>24027605104</td>\n",
+       "      <td>6051.04</td>\n",
+       "      <td>Census Tract 6051.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>39969526</td>\n",
+       "      <td>1553493</td>\n",
+       "      <td>+39.2369323</td>\n",
+       "      <td>-076.9735549</td>\n",
+       "      <td>POLYGON ((-76.97979 39.23322, -76.98018 39.233...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53454</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>603003</td>\n",
+       "      <td>24027603003</td>\n",
+       "      <td>6030.03</td>\n",
+       "      <td>Census Tract 6030.03</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>45161338</td>\n",
+       "      <td>174904</td>\n",
+       "      <td>+39.3192333</td>\n",
+       "      <td>-076.9680454</td>\n",
+       "      <td>POLYGON ((-76.93350 39.35760, -76.93341 39.357...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53455</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>603001</td>\n",
+       "      <td>24027603001</td>\n",
+       "      <td>6030.01</td>\n",
+       "      <td>Census Tract 6030.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>24726303</td>\n",
+       "      <td>57708</td>\n",
+       "      <td>+39.3279761</td>\n",
+       "      <td>-076.8942412</td>\n",
+       "      <td>POLYGON ((-76.86308 39.31501, -76.86305 39.314...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53464</th>\n",
+       "      <td>24</td>\n",
+       "      <td>027</td>\n",
+       "      <td>602201</td>\n",
+       "      <td>24027602201</td>\n",
+       "      <td>6022.01</td>\n",
+       "      <td>Census Tract 6022.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>4611549</td>\n",
+       "      <td>6125</td>\n",
+       "      <td>+39.2900228</td>\n",
+       "      <td>-076.8721238</td>\n",
+       "      <td>POLYGON ((-76.87750 39.30290, -76.87747 39.302...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      STATEFP10 COUNTYFP10 TRACTCE10 GEOID10_TRACT   NAME10  \\\n",
+       "53415        24        027    602100   24027602100     6021   \n",
+       "53416        24        027    602303   24027602303  6023.03   \n",
+       "53424        24        027    605503   24027605503  6055.03   \n",
+       "53429        24        027    605502   24027605502  6055.02   \n",
+       "53452        24        027    603004   24027603004  6030.04   \n",
+       "53453        24        027    605104   24027605104  6051.04   \n",
+       "53454        24        027    603003   24027603003  6030.03   \n",
+       "53455        24        027    603001   24027603001  6030.01   \n",
+       "53464        24        027    602201   24027602201  6022.01   \n",
+       "\n",
+       "                 NAMELSAD10 MTFCC10 FUNCSTAT10   ALAND10  AWATER10  \\\n",
+       "53415     Census Tract 6021   G5020          S  13769934      3674   \n",
+       "53416  Census Tract 6023.03   G5020          S  11740756     26901   \n",
+       "53424  Census Tract 6055.03   G5020          S   3158645      8444   \n",
+       "53429  Census Tract 6055.02   G5020          S   5260984     16405   \n",
+       "53452  Census Tract 6030.04   G5020          S  44425097     99882   \n",
+       "53453  Census Tract 6051.04   G5020          S  39969526   1553493   \n",
+       "53454  Census Tract 6030.03   G5020          S  45161338    174904   \n",
+       "53455  Census Tract 6030.01   G5020          S  24726303     57708   \n",
+       "53464  Census Tract 6022.01   G5020          S   4611549      6125   \n",
+       "\n",
+       "        INTPTLAT10    INTPTLON10  \\\n",
+       "53415  +39.3076905  -076.8349752   \n",
+       "53416  +39.2600506  -076.8754102   \n",
+       "53424  +39.2274469  -076.8803625   \n",
+       "53429  +39.2257617  -076.9054701   \n",
+       "53452  +39.2817022  -076.9188315   \n",
+       "53453  +39.2369323  -076.9735549   \n",
+       "53454  +39.3192333  -076.9680454   \n",
+       "53455  +39.3279761  -076.8942412   \n",
+       "53464  +39.2900228  -076.8721238   \n",
+       "\n",
+       "                                                geometry  \n",
+       "53415  POLYGON ((-76.86305 39.31484, -76.86308 39.315...  \n",
+       "53416  POLYGON ((-76.86750 39.25170, -76.86754 39.251...  \n",
+       "53424  POLYGON ((-76.88405 39.23543, -76.88398 39.235...  \n",
+       "53429  POLYGON ((-76.90881 39.21739, -76.90882 39.217...  \n",
+       "53452  POLYGON ((-76.98539 39.26610, -76.98557 39.266...  \n",
+       "53453  POLYGON ((-76.97979 39.23322, -76.98018 39.233...  \n",
+       "53454  POLYGON ((-76.93350 39.35760, -76.93341 39.357...  \n",
+       "53455  POLYGON ((-76.86308 39.31501, -76.86305 39.314...  \n",
+       "53464  POLYGON ((-76.87750 39.30290, -76.87747 39.302...  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_subset = df.query(\n",
+    "    'STATEFP10==\"24\" and COUNTYFP10==\"027\" and NAME10 in [\"6030.03\", \"6051.04\", \"6055.02\", \"6055.03\", \"6023.03\", \"6022.01\", \"6030.01\", \"6022.01\", \"6021\", \"6030.04\"]'\n",
+    ")\n",
+    "df_subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64e8c34d-df3a-4011-afad-c8a04af71d39",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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0dZwoWrESRmMWsRGME6G43IrvvbiJ1XujtyDIQDp6ImvB91gpzkzksJf1k+/tDM5se0+fm3d21LN6z9GI7gHrLBZhzD2vbOGZNbGxzAiIqfCCBUXprKnyHkayvrqJutYuspOdXvd39bqoa+kmNd4+ah/iuv2NfO3pddQ0esJ2puUk84+vnonTHnn1THVPLEzZXd/Gk6v2hdqMoBJpqXdGS+G4BNbvbxpyf69L8dDbe1Dq5AmdNzYdZNaPlnP2fSs5/xdvc3gUyRff33mEa/+4+piAgadWwFBuC7db8e3nNnDdI6t4PgzX7moRC1N+t3I3Xp7hqMUixEx6Z4fVQu8I9/rYB3v59G/eP6GSUp/LzX3Ltx+LoTvS1sNPX/Nv8n7zgWZu/FM5nV4Kmzz5URU7vVQx33Oknb+treGDXUd5eUPtSfvBs2B/Y01TSFwCejgZhlQ3dPBSjK2ly06O41BLbOTXqjrajtNmoWuEdNybD7TwpScr+NaFU+npc/NsRTV7jrSfcMzLG2pp7OjhD1+YT4JRgdztVhxq6cJmlZOGpPe88vGQgcRuBQ+/u4f7rphzwvbfrtx17P2qPUepaew4lu+tvrWbZ8r384d399DW3cfEjATOm5bF508v5JScZN8+kDEi3rqskUZpaamqqKgItRmmcdffN/F0+f5QmxFUyooyKK+KjQkMgGk5SWw3MS9cVnIcF8zIoavXxce1LWw/3MqS6dk8duOCY8fsPNzKhQ+8O+x5bBbhvTvOIzc1HoDmjl5KfvKvE0YFaQl2Pr+wkCSnjV+s2EGPFzG2WoRrygq48NTxHGntpigzgbkF6VhGWStVRCqVUqXe9umeWJhR19oVMznDBtLUGTtOfQCz45frW7tP+uIrHHdidtwX1o6cu6zPrfjf17fxy6tLEBFS4m0kOmy0dR8fJjZ19PKbAb0zb7jciqdW7eepVcdtmpDq5KeXncZ507N9uSWf0T6xMGPltrqY8Q31k5nkiIlstQOpaw18NaRTBxRXVkrx9jbfQjde3lDLXX/fxOGWLpo7e7GaVGm+trmLrz29jj+8s9uU8/Wje2Jhxqo9sTOk6qc4MzHm1ksGozxdQcbxnthfy/ez3YvTfiieWVPNM2uqEcHUCaa27j5+89YublhUZFo4h+6JhRmxkJZ5MO3dsXfPk7KSAn6NVzbUUnWknb9V1vg9i9lPIFzmrd19vL7poGnn0z2xMGNnne/fltHC/oboLgoymPGpTpy2wPcf/rJ6P39ZHZ4TRGYurdIiFkY0d/Syu7595AOjiLQE+0lrB6OVU7KTSHBY2VDTzKHm2K0Qnp5g5zMlE0Y+0Ee0iIUR66rDP5upw2YhPcFOXWu3KUMNuyX6PRqz81Lp6nPF3OTFUHxydi4pTvNSLmkRCyPWDrMUJVwoGpfAjsNt2C1CTpqTjEQHTpsVN4rOHhdH2rr9ErgeV/T5w7KT4yjKTGTX4TY6e/vYGEOZOXyht89cR5sWsTBifXVTqE0YkaQ4zyPT61bUNHaesP6uH7tFGJ/uJCPBgdNuxa0UXb1ujrZ1c7i1+4QQkmDM0gWLmRNSsFmEjTXN1Onq3kOSlRxn6vm0iIURe+rDf7hhs448/Ot1K6obOr0WNrEITDB6cPF2KxYR2rr7aGzvob6tO+Ky2CbH2ZgxIYUDjR1sqdXVioZjTn4qnynJ49Nzckc+2A+0iIUJfS43B2PA2etWUNvURW2T93vNTo4jI9FBstPzaHb1umns6OFwS1dABS7ebmVCmpNkpx27VRARBE+IgUt5hsqHW7qOpY7OTo6jaFwimw40UR4j+d7GQuG4BP58y0JTfWH9aBELEw42d0VEpH5Pb2CHf3Wt3UMOxSakORmf4kQp2HKwxeuaPV+Js1mYNj4Zh83CwaYuDjR1+jQznJUUR0FGPPEOKxtrmk9IK60ZmqWzxgdEwECLWNhQ3RgZsVK97tD90w7swaUn2CnJT6ViXyP+aP/MCSk4bBa21rawscZ/h3t9Wzf1bR6RtVuFkoI0OntcfkXDxxKTsxK59/LZlBZlBOwaWsTChJoYKYxrFo0dvZRXNVI0LoFkp33Y3Pzxdiuz8lKobeo01W/V61Ksr25idn4qp+Ym09HjoupoZHwZBYNPnpbL/VfMId4R2GyxI4qYiDjx1JWMM45/QSn1IxFZAtwPOIBK4GalVN+gtoXAi3iWN9mBXyulHjL2zQceB+KB14GvK6WUiGQAzwJFQBVwpVIq/AOoxsiBpsgQMUcQIs39oV80cpLjKBiXgHC8xJ3DZqGnz832Qy1DpoIeK/lp8Xxc20KfW2ERKC1KZ09dOw0dsbUWdDCLT8nkl1eX+DQRNFZ86Yl1A0uUUm0iYgfeF5HleCpzn6+U2iEi9wA3AI8OansQOEMp1S0iScBmEXnZKKT7e+BLwGo8IrYUeAO4E3hTKfUzEbnT+P2Osd9qePPVJVNo6ujhqdX7w9o3Zg/CQzkaDrd2ey26EWg6elzHkgy6FVRUNRLvsLKwOIP11Y10mxwTFQmkxtv5xZXBETDwYQG48tA/9283Xi6gRynVX/V7BXC5l7Y9A6p5x/VfzyiQm6KUWqU8WRmfBD5rHHcpHoHE+PlZYgC71cKPL53FuVOzQm3KsHTF4AL14Wjs6CEvPf6EbZ09LlbvbSDZaae0MD1EloWO7y6dZnos2HD4JJUiYhWR9UAdHsEqB2xGoVyAZUDBEG0LRGQjUA3ca/TC8oCBGdpqjG0AOUqp/iXuh4CcIc57q4hUiEhFfX1wSlwFgyRnmLspzUktFRXE2YTT8lM54CXgFzw58Cv2NVKcmcDMCSlej4k2zpuWxbVlE4N6TZ9ETCnlUkqVAPlAGTATuBp4QETKgVY8vTNvbauVUrOBKcANIuJVlIZoqwCv/XGl1MNKqVKlVGlWVnj3XvwhXIdr/dhMSpAX6aQn2JmYkejTDOfeI55A2Nl5qUzMiB/x+EjlrCmZ/PraeYgE9xnx62tfKdUkIiuBpUqp+4HFACJyETB1hLa1IrLZaPMBHkHsJx/or4xxWERylVIHjWFnnT82RjpxYeY4H4wtBhZsj8SsCSnUt3Wzs86/FRYbDzQjAnML0mjv6YuaBeGZSXH853mTuf6MItOywPqDL7OTWUCvIWDxwIXAvSKSrZSqE5E4PI73n3ppmw8cVUp1ikg6cBbwgCFQLSJyOh7H/vXAr41mL+OZJPiZ8fMfY7/NyCH8ezqx56geyPyJaVSOYaG+UrDOWCM7MSOB8alODjR2cGCIFQzhSuG4BOZPTGfJjGwumJET0qK7vvTEcoEnRMSKZ/j5nFLqVRG5T0Q+ZWz7vVLqLQDDT3abUuoWYAbwcxFReLwp9yulNhnn/QrHQyzeMF7gEa/nRORmYB9wpQn3GTGkxAcmqtkseiJsbaOZJMdZ2X3EvHxv+xs6jiWEnJDmZEJqPA6bha5eF/uOdhxb4uQNq0VIdFhJirORaLw8762e3x02EuKsnp8OK3arBYtFsAhYRYz3gtut6HG56XO5cdisZCY5yElxkuCw0trdR21TJ+3dfVhEyEh0kJ+eQEFG/LHycOHAiJYopTYCc71svx243cv2CuAW4/0KYPYQ560AZnnZfhQ4fyS7opVw74e1d8dGlW5vTM1JoXJ/YOLN+lcjLD4lk79/5UwAevo860Y7e1z0ud1YREhy2khx2omzWYLie5o3MfxnV8NHTjUAfHwwvJevDKxIHUtMzkoMmIANpGhc4rH3DpuFnBTnMEdrQBcKCSuUUlTuC9+MCFaLxGyerIxER1CuMysvNkIxzESLWBix50g7jWGcbz4rKXgBjOFEaWE6lfuCs/ItGFWQog0tYmHEPzcfCrUJw5KdEnsiVpgRz/rqJr8yZYzpeoOqdmtGRotYmNDncvPMmvAsr9VP+Id/mMv08Um0dPUdWxsZaJKdtpjt7Y4FLWJhwmubDnpN5xwuTMxIiIgaAGZRWpTOrrrgDu+nZCcFPdo9GtAiFib84Z09oTZhSOIdVqwWCdqQKtSUFqbhcqmgx+xNzU4O6vWiBS1iYUJTmOafSnHaKEiPZ6+JQZ7hzGl5qVTua2JddRM9fS7KitIJVudo7sS04FwoytBxYmFCsPwu/jAh1YmIRM0av5EoGpfA7rrWYwur2rpdlFc1MjkrEasl8J/DqTGS6cJsdE8sTAg3EZuak0RHrytiMs6OlYxEOx09Ljq8FP7YXd/OjsNtLChKP1aFyWwsAlNz9HByNGgRCxP6XOFTNWduQRp769tpCuOYNTNx2CxkJMaNGMi7pqoRm0WYH4ClOJOzkkK6iDqS0cPJMCFcUlKXFWfEXB3FPpebtq4+Ts1NJjHOhgAHjDJug2ns6KVyfyMzcpNp6+qjeoiEiP5SUpBmynliES1iGgCm5SQjQswJGHhy4x9q6eJQy/F0OPMmpg07lN56sBWbBRYWZ7B2f+OYC/vOi8E01mahRSxMCEY/zGm3MCUriXiHFUFAPOs1a5s6dd3EUdDnhtV7GyjMSMBqEfaMYQZX+8NGjxaxMCGQjv0Up43puSl8fKCZzSbWXYxm/EkTvq+hA5sFyooyKK8aXU82N1Vnqxgt2rEfJgTCJ5Yab6esOIM+l5vyvQ206UpFPtPj50RLnxvKqxqYk59Kqp9BsplJcYzXKXdGjRaxMEApZaqIpcbbWVicQU+fi/K9DV7DBjTD0zzKmdkNNZ48+guLM8j2sWzZp+fkYomxdalmooeTYYCZAlZWlMGW2mZWx6CD3kzGEh/X1NF77PMvzIgnK9mJCLR0etI9tw7KjnvZ3Dxvp9H4iBaxMMDfoYs34mwWpo9PHrVPRnMi3X1uclPjONg8tiSQ+xo62TdoYX9agp3MJAeJDhv56Qmclpc6pmvEOlrEwoDuMQ73Eh1WCjLi2eBDDUSN72QmjV3EvNHU0XsskPimM4t15ooxokUsDBjLzGRqvOdbfduh2FjfGEzi7YH998hIdPCp2bkBvUYsMKJjX0ScIlIuIhtEZIuI/NjYvkRE1orIZhF5QkRO+ouLSImIfGS02ygiVw3Y956IrDdetSLykrH9XBFpHrDvhybeb1gy2vV4mUkOUpw2dtfHRoaJYBPoDtK8ienYwrzieyTgy39PN7BEKdUmInbgfRFZDjwBnK+U2iEi9+ApdPvooLYdwPVKqZ0iMgGoFJHlSqkmpdTi/oNE5G+cWCT3PaXUp8ZyY5FEn1vhsFno6fN9WJmR6MBuFdOWvWiCz7zCtFCbEBWM+DWgPPSPVezGywX0KKV2GNtXAJd7abtDKbXTeF8L1AFZA48RkRRgCfDSKO8h4ln64Lt+CRh4giMD4a/RBAeH1cLFs/RQ0gx86suKiFVE1uMRoRVAOWAzqn0DLAMKRjhHGeAAdg/a9VngTaXUwFDyM4zh6xsiMnOI890qIhUiUlFfX+/LbYQldS1d1PjZm5qak8QWHXkfcLp6AxccfO+y0yjOTBz5QM2I+CRiSimXUqoEyAfKgJnA1cADIlIOtOLpnXlFRHKBPwM3KaUGdzmuAZ4e8PtaoFApNQf4NUP00JRSDyulSpVSpVlZWd4OiQhGU4w2PSE4NRBjnX0NHQE57z2XzuSyufkBOXcs4pdXUSnVBKwEliqlPlJKLVZKlQHvAju8tTGGi68BdyulVg3al4lHFF8bcI2W/uGrUup1wG4cF5V8fND/HpVbhUfanmimcFxCQPKpXVNWwPVnFJl+3ljGl9nJLBFJM97HAxcC20Qk29gWB9wBPOSlrQN4EXhSKfWCl9MvA15VSnUNaDNejMAZYwhqAY76eV8RQ0q8HaufS04sOq4o4OQkB2Yt44Wn5gTkvLGMLz2xXGCliGwE1gArlFKvAreLyFZgI/CKUuotABEpFZFHjLZXAmcDNw4ImSgZcO6rOXEoCR5h2ywiG4BfAVcrFb1dj/OmZbP8G2fj8GOqvbWrb+SDNGPCjFUU3tB/O/ORaNCH0tJSVVFREWozxsSsHy2nrdu3BzzBYaVDZ6QIGBbxrIJo7Tb/Mz59UgbP3HqG6eeNdkSkUilV6m2fjrQLE/wZIXb0uPTMVgCZkp0UEAEDWLWngR06AaWpaBELE+Js/v0pMpP0DGWgCPTs79Pl+wN6/lhDi1iYMCEt3q/jw6WwSDTSGOBCxs+tqR51vjLNyWgRCxMKMhL8On5/gGKYYp14h5VddYFdTN/e4+LpNbo3ZhZaxMKEQj9F7EhbDzk+Zg7V+M6UrCSC0cl98sMq3Zs2CS1iYcK08f5Xuxk8BHX66VfTnEy8IzifYW1zF3vHUB1JcxydTyxMODU35YTfs5PjmJiRgIhnaVLV0Y5ji8QzkxxMSIsnxWlnQVE67d0u9jd00Nbdx/gUJxPHxbP1YKuOSRoFR1oD6w8biL9BzhrvaBELEyZlJTEnPxWHzUJtUycHmrqoaz2epcJuFfLT4mnu6uVIWw9H2rz/s/UXgY2zWZhfmE5De4/+xveR1Hj7mGpH+sPUnCSKxvnnQtB4R4tYiDna1s0LlTX8c8uhYdNL97oUNX4Ur+juc1O5rxGAsuKMmKzs7S+TMhNZV90UlGtdtWCiTkttElrEQoBSivK9Dfxl9X7+uflQwJa49FO+t4GFxRm6AtII2KzBERW7VfhsyYSgXCsW0CIWRBrbe1i+5RCPvL834NP4g1m9t4H5E9Oo3N8U1OtGEkMN0c3m3GnZjEvSM8tmoUUswFTua+DxD/exbn+j38kPzWZDTTOz8lLYfEAnVPRGU4CDXPvRxUHMRYtYAKlt6uSWJypoDJPo7D63YnddO1OyEtmli4ucRDD+Tk67hQtm6HQ8ZqIDiwJER08fn390ddgIWD+dvS7qWruZluN/XFo0k5ZgD8p1LpiRQ2Kc7juYif40A8RvV+5iT5j2dlq6+mjtbmXmhBQSHTYa2rvZ39BBj+vkCHKnzULXoCImCXYLNpuFvj43HWMs/BsuZCQ4ApLJdTCfmaMd+majRcxkXG7FAyt28NuVg+uhhBdKcUKxEYt4lj6NS3Jgs1jo6nWx90g7rd19xNksJMbZ6O510d7j8giXIV5F4xLoc6uQ+/vGSjB6RylOG+dMi9x6EOGKFjGTqG3q5M1tdfx19X62jiJvfqhxK09hDG/FMbr73HT3eXd6Vx3tIMFhZU5+6rBxbuGOv6mQRsMnZ+cSZ7MG/DqxhhYxE/jzqn388B+biYIkuaOio8fFhprmiA6qtQRhCdClJXkBv0Ysoh37Y+TBf+/gBy/FroANpHxvA2XFGdgjcU1ggP9+BRnxlBVlBPYiMYoWsTFQua+BB/+9M9RmhBXlexvITnEyIzeyZj/73IGdoDh/ek5QenuxiC8l25wiUm5U5N4iIj82ti8RkbUisllEnhCRk4amIlIiIh8Z7TaKyFUD9j0uInsHV0ESD78SkV1Gm3km3q+pvL7pUKhNCEsONHWy9WAr8wvTyYiQQr/dfYEtvDI7PzWg549lfOmJdQNLjIrcJcBSEVkEPIGnnNosYB9wg5e2HcD1SqmZwFLgwf4alga3K6VKjNd6Y9vFwCnG61bg937fVRBQSvHm1sOhNiOsqdzXSHefiwVF6UwfnxRqc4Yl3m5jQVE6MycEpgeZn64zVgSKER37Rs3H/oV+duPlAnqUUv1Vv1cAdwGPDmq7Y8D7WhGpA7KApmEueSmeYrsKWCUiaSKSq5Q66NstBYfd9e1UHdUpokeivcfFmipPNo30BDuTs5Lo6nXx8cGWoGRQ9ZUKI+NHstNGbqqTg81dI7TwD4dOWBkwfPpkRcQqIuuBOjyCVQ7YRKS/DtwyoGCEc5QBDmBgANVPjSHjA0YlcYA8oHrAMTXGtsHnu1VEKkSkor6+3pfbMJV/fayHkv7S2NFLxb5GNte2kGwkdAy3HlprV5/f9Q58wab9YQHDJxFTSrmUUiVAPlAGzMRTvfsBESkHWvH0zrwiIrnAn4GblFL9HtS7gOnAAiADuMMfw5VSDyulSpVSpVlZwQ0gVErx6oaw6hhGHM2dvaypamTboTYykxyUFWVwSnZ4CFog5Maic4cFDL/ixJRSTSKyEliqlLofWAwgIhcBU721EZEU4DXgbqXUqgHn6leBbhH5E/Ad4/cDnNiryze2hQ3Ltxzm4wgMaA1XPJlqPfFlE1Kd5GckcLC5k+qG0KwC6AvAOFcFOoYjhvFldjKr3xkvIvHAhcA2Eck2tsXh6UU95KWtA3gRj4/rhUH7co2fAnwW2Gzsehm43pilPB1oDid/2Me1LXz7ufWhNiNqqW3uonxvA9UNnWQkOigpSGP+xHSCORrr6TM/3EJXNgocvvTEcoEnRMSKR/SeU0q9KiL3icinjG2/V0q9BWD4yW5TSt0CXAmcDYwTkRuN891ozET+RUSy8PTe1wO3GftfBy4BduGZ3bxpzHdpEgebO/nSkxW09wR2Ol7joaG9h4Z2z3KnjEQHBRnxxNmsKKXYfriVls7AFELp6jX/79vrZXG9xhxERUGoeWlpqaqoqAjoNT7cfYRvPLP+hOIdmtCR6LAyMy+VbYdaTBezQMxOPvflMygr1hH7o0VEKpVSpd726bWTPrD1YAtffHwNXVGSdiYaaO9xUb63AYvA9PHJpDhtbD/cSrMJghaIUnd6OBk4dPCKD9z7z21awMIUt4Jth1opr2rE7Yb5E9PHfM627j5S4s39fg/0sqZYRovYCLR19/Hh7qOhNkPjA63dfVTubzQl6r44M9EEi44TBV6bsEWL2Ag89PbugMxWaQJHS1cfY62+5rCa969htwqn5em1k4FCi9gwVFQ18Lu3d4XaDI2fVDd0Mr9wbE50MydwPjc3n/TEyFgIH4loERuCXXWtfOUva8NqfZ/Gd2qaOsYUW7bvaAfpJhUPWTprvCnn0XhHi9ggWrp6+e+Xt7D0wffCNpwiO1kXXh2J2qYuZuenjSlBY9E4c/xiZk8SaE5Ei5hBa1cvj7y3hyX3v83jH1YFZOmJGcydmMY/v3E2iyaPC7UpYc/66iYmZyeROkoRsY3VsWbQ2aN9qoEkpr8i3G7F79/ZzfZDrazcVkdrd2AiwM1i5oQU/nrL6cQ7rNxz6SyueOjDsKtrGW5sO9RKZpKDBUXpbKhu8lqWbigOt5jTEw90wsVYJ6Z7Yv/7xlbuW76dlzfUhr2AAVw2N494h6dazpTsJP72H4uYGIC0MdHGkbYe1lQ1kpPiZJwfDvb9DR1kJY196K4TWASWmBWxP767hz++tzfUZvhMbqqTz83LP2HbpKwk/v6VRWGTwibcqW7s9LvSd1Gmb18SDquQnmAnL83J5KxEZuQmc2puCrmpcbjD1DURLcTkcPLFdTX89PWtoTbDZwrHJfDUzQvJ8NKLyEyK4/EvlnH57z7kUIu56/2ikd317UzJTmJXXduIxyY4rCQ4rJQVp+NyQ3t3H70uN70uN129bqMep4uuXjc9LkVPR6/X4X13nxaxQBJzIrZ6z1Fuf35jqM3wmZyUOJ7/8hlkpziHPCYvLZ4nby7jyj98RJP2kY1IWvyJvTGLQFZyHFnJcSQ6bPS43Bxu6aK2qYt3dhwZ8/Xau/vo6OnDabPqikcBIOaGk9NzU8gZRhDCjZvPKh5WwPqZmpPMEzeVkeDQFaaHwm4VJmclkhRnpawog9l5qeSleT7bwy3dbD7Qwuq9DbR09lLbZF6v9u6XNnHqD5dzzv0r2VM/cg9Q4x8xJ2Kp8XZ+ceWciHC2WgQ+60fV6DkFafz1S6czOcvcdX+RRpzNQnFmInMnplFWnMHciWkUZybidit217fz9o4jlFc1sPFAMweauk4KaPbH+e8L/bnEqhs6+fErH5t6bk0MihjAwknj+Mq5k0NtxoicdUqWT72wgZQUpPHG18/ma0umBMiq8CQ7OY6yogxykuPo7nOz90g76/Y3Ub63gXX7m9h7pB1foysCGSP4zo561lQ1BOz8sUjM+cT6+faF0+hzK/7wzp5QmzIkVy8YtoDUkDhsFr590TS2Hmzh31vrTLYqPEhwWJmUmYjTbsUiwvrqRspNWmFR3xa4lRplxRkU6rAYU4lZEbNYhLsunkFuipMfv/px2KVKGZ/i5IIZOWM6x3eXTufUCal8XNvCv6Oo0O/UnCQOtXSxuTYwxVpqGjuJt1voNDmH3OJTMnnkhlLibNpvaSYxOZwcyI1nFrNkWnaozTiJWxYXj7ng6tScZL514VT+8IX5JMdFx/dVosPK7Py0gOXXB0/ur4kZ5vsVp49P1gIWAGJexACWzAgvEctMcnDdwkLTzme1CKVFY894Gg7cefF0vnB6IZkmRNIPR7LTfNHXBWYCgy8l25wiUi4iG0Rki4j82Ni+RETWishmEXlCRE76q4tIiYh8ZLTbKCJXDdj3FxHZbrR/TETsxvZzRaRZRNYbrx+aecPeOH/62IZtZmK1CD+5dNax5UVmcc7U4BYYDgSnT8rguoWFzClI4/GbFgS0d2l2sdv/OHcyV5WOzsepGR5femLdwBKl1BygBFgqIouAJ4CrlVKzgH3ADV7adgDXK6VmAkuBB/trWAJ/wVMB/DQgHrhlQLv3lFIlxuse/2/LP8anOpk5ISXQlxmR7OQ4nrn1dC4+Ldf0c19yWm5QazeazdScJH559dxjwaKz8lL50WdmBuRak7ISqTra7lcbm0W46cwivnPR1JNmvj83N4/vXDSNOQVpJlqp6WfErzLlqenWH6FnN14uoEcptcPYvgK4C3h0UNsdA97XikgdkAU0KaVe798nIuV4Kn2HjDsvns6tT1bSGYCag76wsDiD31w7j6wA5QrLTnFy9tQs3t5eH5DzB5IvnF7I3Z+cgdN+Yu902fx8Zk5I4eJfvjfmaywszkApaO/pY4ufEwan5aXy6A2lJ4TDXFFawPMV1cybmM4Fp4ZPTz8a8cknJiJWEVkP1OERrHLAZhTKBVgGDNtXFpEywAHsHrTdDnwB+OeAzWcYw9c3RCQwX7eDWHxKFk/dspDUeHOyefqKRTxDjaduWRgwAesn0oYz2clxPHZjKT/57KyTBKyfqTnJxA+xz1/Kqxr8FrB4u5XfXjvvpHi+4sxEvrt0uhawIOCTiCmlXEqpEjy9pTJgJnA18IDRi2rF0zvziojkAn8GblJKDZ63/h3wrlKq/+t0LVBoDF9/Dbw0xDlvFZEKEamorzendzG/MJ1vXTjVlHP5wmfmTODt75zHHUunYzexMMVQXHhqDvnp8QG/jhlcNjeP5d84myUj+CutFuFiE9I/j7bW5OXz85g4Tsd9hRK//nOUUk3ASmCpUuojpdRipVQZ8C6ww1sbEUkBXgPuVkqtGrTvR3iGl98acI0WpVSb8f51wC4imV5seVgpVaqUKs3KMs9pHazKRvMmpvHAVSVB/QewWS3c/olpQbveaLlv2WweuKrE5+IaM3LH7s/cXd/md4WkqTlJ/PBTQRkoaIbBl9nJrH5nvIjEAxcC20Qk29gWB9wBPOSlrQN4EXhSKfXCoH23AJ8ArhnYOxOR8SKeqSFjCGoBglb4sb0n8MkRExxWHrxqLtYQeNo/M2cCi0856TshbPjimcVc4eewd74J4SPdfW5mTvCvrFp2snPMsXyasePLXyAXWCkiG4E1wAql1KvA7SKyFdgIvKKUegtAREpF5BGj7ZXA2cCNA0ImSox9DwE5wEeDQimWAZtFZAPwKzwzoEGLp+8IQizPxbNyQzYEERF+cWUJeWnhN6wsLUznzoun+92ubZRDwcHsqGv1LBovymBhcQYZIyRQXFg8trJwGnOQIOpDwCgtLVUVFRWmnOvuFzfxl9X7TTnXUPzuunlcEoAwCn/YebiVa/64iiNtPSG1o5+8tHhe/Moivxe8A/S63Fzz8Coq9jWaatPMCclsqW09abvTbuH7nzyVz59uXkCyZnhEpFIpVeptn+4LD+KmM4uZZHIJ+4HMnJDCRWEwY3VKTjLPffkMJoVB2h6n3cLD188flYCBZ5lQIMrrDbVE6O5LZnBt2UTTr6cZHVrEBjElO4kX//PMgEW4f/70QmxBmIn0hUlZSbz81bNCHnrxf8vm+O2PGojDZuGr500x3cc41N/pB//Yws/+uc3Ua2lGT3j8N4UZqfF2HrtxAV8+e5Kp542zWbhsru9JDoNBUpyNe5fN5tlbT2dKCAqO3LioiM/MmTDm81y5oID//dxp2E2qFQkwnKtl+ZZDpl1HMza0iA2B1SLcdckMHryqhDiTZqCSnbYhgzZDzcJJ43j9vxZz+yem4bQH57GYk5/K9y6ZYdr5riwt4JdXz6XIpEmT7mHCbRIc0ZEVJBrQIjYCn52bx/O3ncF4E/LyBzrzwlhx2Cz853lTWPHNczh3WmAXjCfF2fjVNXNND1G45LRcbv/EdFO+eI4OmPRIdFgpzkykrDiDxadkcsYkXYE9XNBfJz4wOz+Nl792Jrf9uZK1+5tGfZ4zp4RvfNZACjIS+NONC3hj8yHueeXjgJSC+/mVcygcF5hJhU/OzuVwSxf3vDq2fPaHWrqwW4SLZo7nfy8/jRRncJekaXxD98R8JDvZydO3ns6VpaNfpx5u/rDhEBEuOS2Xf3/7HG45q9hUp/n3PzmDT8wc+1Kh4bhxURE/vWzWmHpkl83N41/fOpvfXjdPC1gYo0XMD+JsVu69fDb/d/lsv4NFZ+SmhEW6H39JirPx/U+dyitfPYt5E9PGfL4r5udz81nFYzdsBCwW4bqFhZSNIiA13m7l0RtKuf+KORRn6urq4Y4WMT8REa5cUMA7t5/LL68uISfFNz/XFfPzkUioEzcEp05I4YXbFvG/nztt1Jk+5hSk8ZPPzgra53CgqZP3dh7BIvDEF8t8is9LS7Dzly8t5Pwx1jfQBA/tExslNquFS0vyaGzv4b9HqCWYGm9n2RiGoeGCxSJcUzaRi07N4WdvbOP5yhqf22YmxfGHz88P2uxsY3sPn/n1+4Cnuvc5U7M4Y9I4Wrp6cdgsbDvYyrZDLWw/1MqW2hY21DRRkJ7A4zctYFKW7n1FElrExsjl8/O5b/n2YfOnf/mcSVHlUxmXFMd9V8zhygUF3P3iJnYcHr6qtc0i/PbauYxPDV7l9Q01TRxt98wupsbbcbsVDpvl2AxxWXHGCUPNyn2NFI5LCPsZZM3J6OHkGEl22vncvKF7WZlJDm5cVBQ8g4LIgqIMXvuvxdx18fRhExPec+ksFgY5JGFgcsMdh9v4pNErG4r5helawCIULWImcOvZk1g02fs/6Q1nFEV1YKTdauHL50zm398+x2tywhsXFXHtwuCvM7y0ZMIJ/sqtB1vYVNMcdDs0gUeLmAkUZCTw1y+dzj/+80wunjWefr91nM3CdTGS6SAvLZ7ff34+T36x7NgC+nOnZfH9T5oXke8P+ekJJ61/feyDvSGxRRNYoreLEALmFKTx+8/PZ099Gw+/uwen3UqGj9lJo4Wzp2bxxjcW8/Tq/SwrLQjpYvejg9IMvbqxljsvnk6OCasvNOGDzicWQJRSER1WEal09rj45rPr+aeXRdpXlRZw77LZIbBKMxaGyyeme2IBRAtYaFi+5ZBXAQN4tqKaeIeVqxYU0NnrwuVWLCjSGVojGS1imqjj7+sODLv/8Q+rePzDKgAunjVei1iEox37mqjDabP4vNYzHGsNaPxDi5gm6nj4+lLe/e55zPVhrad28kc+WsQ0UUleWjxP3byQtBEqFumSa5GPL3UnnSJSLiIbRGSLiPzY2L5ERNaKyGYReUJETvKviUiJiHxktNsoIlcN2FcsIqtFZJeIPGvUqERE4ozfdxn7i0y8X00MkRhnGzIIGTyZdr8QI3F80YwvX0PdwBKl1BygBFgqIouAJ/DUhJwF7ANu8NK2A7heKTUTWAo82F+IF7gXeEApNQVoBG42tt8MNBrbHzCO02hGxe+umz9kIZSkOBstXb1BtkhjNiOKmPLQv8LXbrxcQI9SaoexfQVwuZe2O5RSO433tUAdkGVU+F4C9FcFfwL4rPH+UuN3jP3n91cE12hGw3eXTiPFefJE/MHmLlbtaQiBRRoz8ckhICJWEVmPR4RWAOWATUT6g8+WAcPW/RKRMsAB7AbGAU1Kqf7SzTVAf9rTPKAawNjfbBw/+Hy3ikiFiFTU19f7chuaGGVcUhw//PRMr/VE5xWmBd8gjan4JGJKKZdSqgTIB8qAmcDVwAMiUg604umdeUVEcoE/AzcppYYuIeMHSqmHlVKlSqnSrKzAFrXQRD7L5uez4lvncNagOgcd3UOnUNJEBn5NzSilmoCVwFKl1EdKqcVKqTLgXWCHtzYikgK8BtytlFplbD4KpA2YDMgH+iMUD2D06oz9qcbxGs2Y6Op1kRRnY6BzYsXHh0NnkMYUfJmdzOp3xotIPHAhsE1Eso1tccAdwENe2jqAF4EnlVL9/i+UZ8HmSjzDUPBMCvzDeP8yxycJlgFvqWhY4KkJOYlxNh76wny+d7Ens0ay0xbwgiWawONLTywXWCkiG4E1wAql1KvA7SKyFdgIvKKUegtAREpF5BGj7ZXA2cCNIrLeeJUY++4AviUiu/D4vB41tj8KjDO2fwu4c8x3qdEM4AtnFHL21Cy+tmQKE00qtKsJHTqLhUajCXuGy2Khw5U1Gk1Eo0VMo9FENFrENBpNRKNFTKPRRDRaxDQaTUSjRUyj0UQ0WsQ0Gk1Eo0VMo9FENFrENBpNRBMVEfsiUo8nMSNAJnAkhOaMlki0OxJthsi0OxJtBvPsLlRKeU1XExUiNhARqRhqeUI4E4l2R6LNEJl2R6LNEBy79XBSo9FENFrENBpNRBONIvZwqA0YJZFodyTaDJFpdyTaDEGwO+p8YhqNJraIxp6YRqOJIbSIaTSaiCZiRMyoCt6f4rrKKCGHiFw3YPt6EXEPSIE9sH2GiKwQkZ3Gz3Rj+7ki0jyg/Q8jwGYRkV8ZVdI3isg8s2wezm5j3+wBVd03iYjTS/s5xjGbROQVo1gMIlIkIp0Dzn1SXYZws9nYd5fxWW8XkU+YZbNJdpeIyCqjfYV4SiOG5Lk2webRPddKqYh7AT8Hfuhl+2nA7iHa/B9wp/H+TuBe4/25wKsRZvMlwBuAAKcDq4NhN2DDU1NhjvH7OMDqpc0a4Bzj/ReBnxjvi4DNwfysTbD5VGADEAcU46mbelL7ENr9L+DiAc/F2ypEz7UJNo/quY6Ynlg/IiJ4CpA87WX3NcAzQzQdWFl8YMXxgBMAmy/FU0FKKU8ZvDTx1PY0FS92XwRsVEptAFBKHVVKeSvcOBVPGT8Yojp8oAiAzZcCzyilupVSe4FdeGqvhovdCujvNaYCtWbbNhQBsHlUz3XEiRiwGDislNrpZd9VeBcKgByl1EHj/SEgZ8C+M0Rkg4i8ISIzTbS1H7NtPlYl3WBgBXUzGWz3VECJyHIRWSsi3x2i3RY8DyTAFZxYHb5YRNaJyDsisjgCbA73z/obwH0iUg3cD9w1YF+wn+ux2jyqz9o20gHBRET+DXgrBHi3Uqq/LuU1ePmnF5GFQIdSavNI11FKKRHpjy1Zi2ddVpuIXAK8BJwS5jaPmVHabQPOAhYAHcCb4qlC8+agc3wR+JWI/ABPHdEeY/tBYKJS6qiIzAdeEpGZSqmWMLZ5zATY7v8AvqmU+puIXImn5OEFhOa5HqvNoyPQY2aTx9824DCQ72XfA8D3hmm7Hcg13ucC24c4rgrIDGebgT8A13g7LpB2A1cDTwz4/QfA7SOcZypQPsS+t4HScLYZTy/hrgH7lgNnhMtnDTRzPN5TgJYhrhHw53qsNo/2uY604eQFwDalVM3AjSJiwTM2H8q3BCdWFj9WcVxExhtje4xZEgtwNJxtNrZfb8zmnA40q+PDTrPwZvdy4DQRSRARG3AO8PHghnK8OrwF+D5GdXjxVJO3Gu8n4ekZ7Alnm/F81leLSJyIFBs2l5to85jsxuNPOsd4vwTYadxHKJ7rMdnMaJ9rM79RAv0CHgdu87L9XGCVl+2PYHzT45kpedP4wP4NZBjbv4rHH7IBWAUsigCbBfgtnpmyTZjYm/HB7s8bn9dm4P+GsPvrwA7j9TOOf+tebrRdj2e48+lwt9nYd7fxWW/HmFULI7vPAiqN53c1MD/Ez/VYbB7Vc62XHWk0mogm0oaTGo1GcwJaxDQaTUSjRUyj0UQ0WsQ0Gk1Eo0VMo9FENFrENBpNRKNFTKPRRDT/HyCyc5M7Ot+SAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_subset.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44c0c3d9-fa1e-405c-9887-29a0a69801ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tract_subset = df_subset.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1d7645f9-22b2-4fec-9514-d777dae32b29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_subset = df_subset[[\"GEOID10_TRACT\", \"geometry\"]].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cfff1e45-3b3f-453b-a33b-8cf20f0152b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False,  True,  True, False, False, False,  True,  True])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.choice([True, False], len(df_subset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4f01f4fa-69cf-4c9a-8acc-fddf3d8ab39d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_subset_scores = df_subset.copy()\n",
+    "# df_subset_scores[\"included\"] = np.random.choice([True, False], len(df_subset))\n",
+    "df_subset_scores[\"included\"] = True\n",
+    "df_subset_scores.loc[df.GEOID10_TRACT == \"24027603004\", \"included\"] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8cdda9e3-9c73-405e-8c35-e40e8ff2d29b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tract_data = df_subset.rename(columns={\"GEOID10_TRACT\": \"ORIGINAL_TRACT\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f43ffa4a-3503-4497-a247-8cc02b264f7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df_subset_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8e1797ed-4838-4139-943c-809e4df0ab70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>included</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>53415</th>\n",
+       "      <td>24027602100</td>\n",
+       "      <td>POLYGON ((-76.86305 39.31484, -76.86308 39.315...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53416</th>\n",
+       "      <td>24027602303</td>\n",
+       "      <td>POLYGON ((-76.86750 39.25170, -76.86754 39.251...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53424</th>\n",
+       "      <td>24027605503</td>\n",
+       "      <td>POLYGON ((-76.88405 39.23543, -76.88398 39.235...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53429</th>\n",
+       "      <td>24027605502</td>\n",
+       "      <td>POLYGON ((-76.90881 39.21739, -76.90882 39.217...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53452</th>\n",
+       "      <td>24027603004</td>\n",
+       "      <td>POLYGON ((-76.98539 39.26610, -76.98557 39.266...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53453</th>\n",
+       "      <td>24027605104</td>\n",
+       "      <td>POLYGON ((-76.97979 39.23322, -76.98018 39.233...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53454</th>\n",
+       "      <td>24027603003</td>\n",
+       "      <td>POLYGON ((-76.93350 39.35760, -76.93341 39.357...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53455</th>\n",
+       "      <td>24027603001</td>\n",
+       "      <td>POLYGON ((-76.86308 39.31501, -76.86305 39.314...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53464</th>\n",
+       "      <td>24027602201</td>\n",
+       "      <td>POLYGON ((-76.87750 39.30290, -76.87747 39.302...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      GEOID10_TRACT                                           geometry  \\\n",
+       "53415   24027602100  POLYGON ((-76.86305 39.31484, -76.86308 39.315...   \n",
+       "53416   24027602303  POLYGON ((-76.86750 39.25170, -76.86754 39.251...   \n",
+       "53424   24027605503  POLYGON ((-76.88405 39.23543, -76.88398 39.235...   \n",
+       "53429   24027605502  POLYGON ((-76.90881 39.21739, -76.90882 39.217...   \n",
+       "53452   24027603004  POLYGON ((-76.98539 39.26610, -76.98557 39.266...   \n",
+       "53453   24027605104  POLYGON ((-76.97979 39.23322, -76.98018 39.233...   \n",
+       "53454   24027603003  POLYGON ((-76.93350 39.35760, -76.93341 39.357...   \n",
+       "53455   24027603001  POLYGON ((-76.86308 39.31501, -76.86305 39.314...   \n",
+       "53464   24027602201  POLYGON ((-76.87750 39.30290, -76.87747 39.302...   \n",
+       "\n",
+       "       included  \n",
+       "53415      True  \n",
+       "53416      True  \n",
+       "53424      True  \n",
+       "53429      True  \n",
+       "53452     False  \n",
+       "53453      True  \n",
+       "53454      True  \n",
+       "53455      True  \n",
+       "53464      True  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "799b883e-1f45-4f79-9b3f-65361aec881c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adjacent_tracts: gpd.GeoDataFrame = df.sjoin(\n",
+    "    tract_data, how=\"left\", predicate=\"touches\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "21672763-02e0-4608-82b2-3e256591c567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>included</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>24027602100</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24027602201</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24027602303</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>24027603001</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24027603003</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>24027603004</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24027605104</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>24027605502</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>24027605503</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  included\n",
+       "0   24027602100  0.666667\n",
+       "1   24027602201  0.666667\n",
+       "2   24027602303  0.666667\n",
+       "3   24027603001  0.666667\n",
+       "4   24027603003  0.666667\n",
+       "5   24027603004  1.000000\n",
+       "6   24027605104  0.666667\n",
+       "7   24027605502  0.666667\n",
+       "8   24027605503  0.666667"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adjacent_tracts.groupby(\"ORIGINAL_TRACT\")[[\"included\"]].mean().reset_index().rename(\n",
+    "    columns={\"ORIGINAL_TRACT\": \"GEOID10_TRACT\"}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6f4289b-944c-489e-955a-a7a177ec6dd5",
+   "metadata": {},
+   "source": [
+    "# Make test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "78772a00-3185-4d28-9755-7ba72c170282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tract_subset.to_file(\"../tests/score/test_utils/data/us.geojson\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "425a5cd3-5929-4560-89e7-2dd9233bf303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[[\"GEOID10_TRACT\", \"included\"]].to_csv(\n",
+    "    \"../tests/score/test_utils/data/scores.csv\", index=False\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py
index 3a00d7f8d..96da097f1 100644
--- a/data/data-pipeline/data_pipeline/score/field_names.py
+++ b/data/data-pipeline/data_pipeline/score/field_names.py
@@ -1,6 +1,8 @@
 # Suffixes
 PERCENTILE_FIELD_SUFFIX = " (percentile)"
 ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD = " for island areas"
+ADJACENT_MEAN_SUFFIX = " (including adjacency index)"
+ADJACENCY_INDEX_SUFFIX = " (average of neighbors)"
 
 # Geographic field names
 GEOID_TRACT_FIELD = "GEOID10_TRACT"
@@ -691,6 +693,9 @@
 
 FPL_200_SERIES = "Is low income?"
 FPL_200_SERIES_IMPUTED_AND_ADJUSTED = "Is low income (imputed and adjusted)?"
+FPL_200_SERIES_IMPUTED_AND_ADJUSTED_DONUTS = (
+    "Meets the less stringent low income criterion for the adjacency index?"
+)
 FPL_200_AND_COLLEGE_ATTENDANCE_SERIES = (
     "Is low income and has a low percent of higher ed students?"
 )
@@ -715,5 +720,10 @@
     "Tract-level redlining score meets or exceeds 3.25 and is low income"
 )
 
+
+ADJACENT_TRACT_SCORE_ABOVE_DONUT_THRESHOLD = (
+    "Is the tract surrounded by disadvantaged communities?"
+)
+
 # End of names for individual factors being exceeded
 ####
diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py
index 1effde59e..36a1e07b3 100644
--- a/data/data-pipeline/data_pipeline/score/score_narwhal.py
+++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py
@@ -6,6 +6,7 @@
 import data_pipeline.score.field_names as field_names
 from data_pipeline.utils import get_module_logger
 import data_pipeline.etl.score.constants as constants
+from data_pipeline.score.utils import calculate_tract_adjacency_scores
 
 logger = get_module_logger(__name__)
 
@@ -20,6 +21,12 @@ def __init__(self, df: pd.DataFrame) -> None:
         self.MEDIAN_HOUSE_VALUE_THRESHOLD: float = 0.90
         self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD: float = 0.10
 
+        # We define a donut hole DAC as a tract that is entirely surrounded by
+        # DACs (score threshold = 1) and above median for low income, as a starting
+        # point. As we ground-truth, these thresholds might change.
+        self.LOW_INCOME_THRESHOLD_DONUT: float = 0.50
+        self.SCORE_THRESHOLD_DONUT: float = 1.00
+
         super().__init__(df)
 
     def _combine_island_areas_with_states_and_set_thresholds(
@@ -907,6 +914,54 @@ def _workforce_factor(self) -> bool:
             | workforce_combined_criteria_for_island_areas
         )
 
+    def _mark_donut_hole_tracts(self) -> pd.DataFrame:
+        """Mark tracts that do not qualify on their own, but are surrounded by those that do
+
+        A donut hole is a tract surrounded by tracts that are marked for inclusion
+        by the scoring system AND meet a less stringent low-income threshhold.
+
+        We calculate "donut holes" after the initial score generation
+        """
+        logger.info("Marking donut hole tracts")
+
+        # This is the boolean we pass to the front end for the donut-hole-specific
+        # low income criterion
+        self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED_DONUTS] = (
+            self.df[
+                field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.LOW_INCOME_THRESHOLD_DONUT
+        )
+
+        self.df = self.df.merge(
+            calculate_tract_adjacency_scores(
+                self.df, field_names.SCORE_N_COMMUNITIES
+            ),
+            how="left",
+            on=field_names.GEOID_TRACT_FIELD,
+        )
+
+        # This is the boolean we pass to the front end for color
+        self.df[field_names.ADJACENT_TRACT_SCORE_ABOVE_DONUT_THRESHOLD] = (
+            self.df[
+                (
+                    field_names.SCORE_N_COMMUNITIES
+                    + field_names.ADJACENCY_INDEX_SUFFIX
+                )
+            ]
+            >= self.SCORE_THRESHOLD_DONUT
+        )
+
+        # This should be the "final list" of Score Narwhal communities, meaning that we would
+        # expect this to be True if either the tract is a donut hole community OR the tract is a DAC
+        self.df[
+            field_names.SCORE_N_COMMUNITIES + field_names.ADJACENT_MEAN_SUFFIX
+        ] = (
+            self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED_DONUTS]
+            & self.df[field_names.ADJACENT_TRACT_SCORE_ABOVE_DONUT_THRESHOLD]
+        )
+
     def add_columns(self) -> pd.DataFrame:
         logger.info("Adding Score Narhwal")
 
@@ -946,5 +1001,6 @@ def add_columns(self) -> pd.DataFrame:
             field_names.SCORE_N_COMMUNITIES
             + field_names.PERCENTILE_FIELD_SUFFIX
         ] = self.df[field_names.SCORE_N_COMMUNITIES].astype(int)
+        self._mark_donut_hole_tracts()
 
         return self.df
diff --git a/data/data-pipeline/data_pipeline/score/utils.py b/data/data-pipeline/data_pipeline/score/utils.py
new file mode 100644
index 000000000..bd0df93d5
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/score/utils.py
@@ -0,0 +1,56 @@
+"""Utilities to help generate the score."""
+import pandas as pd
+import geopandas as gpd
+import data_pipeline.score.field_names as field_names
+
+# XXX: @jorge I am torn about the coupling that importing from
+# etl.sources vs keeping the code DRY. Thoughts?
+from data_pipeline.etl.sources.geo_utils import get_tract_geojson
+from data_pipeline.utils import get_module_logger
+
+logger = get_module_logger(__name__)
+
+
+def calculate_tract_adjacency_scores(
+    df: pd.DataFrame, score_column: str
+) -> pd.DataFrame:
+    """Calculate the mean score of each tract in df based on its neighbors
+
+    Args:
+        df (pandas.DataFrame): A dataframe with at least the following columns:
+          * field_names.GEOID_TRACT_FIELD
+          * score_column
+
+        score_column (str): The name of the column that contains the scores
+                            to average
+    Returns:
+        df (pandas.DataFrame): A dataframe with two columns:
+          * field_names.GEOID_TRACT_FIELD
+          * {score_column}_ADJACENT_MEAN, which is the average of score_column for
+            each tract that touches the tract identified
+            in field_names.GEOID_TRACT_FIELD
+    """
+    ORIGINAL_TRACT = "ORIGINAL_TRACT"
+    logger.debug("Calculating tract adjacency scores")
+    tract_data = get_tract_geojson()
+    df: gpd.GeoDataFrame = tract_data.merge(
+        df, on=field_names.GEOID_TRACT_FIELD
+    )
+    df = df.rename(columns={field_names.GEOID_TRACT_FIELD: ORIGINAL_TRACT})
+
+    logger.debug("Perfoming spatial join to find all adjacent tracts")
+    adjacent_tracts: gpd.GeoDataFrame = df.sjoin(
+        tract_data, predicate="touches"
+    )
+
+    logger.debug("Calculating means based on adjacency")
+    return (
+        adjacent_tracts.groupby(field_names.GEOID_TRACT_FIELD)[[score_column]]
+        .mean()
+        .reset_index()
+        .rename(
+            columns={
+                score_column: f"{score_column}{field_names.ADJACENCY_INDEX_SUFFIX}",
+            }
+        )
+    )


Test patch:
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv
index abcdc13a7..9460fb269 100644
--- a/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv
+++ b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/score_data_initial.csv
@@ -1,3 +1,3 @@
-GEOID10_TRACT,Persistent Poverty Census Tract,Tract-level redlining score meets or exceeds 3.25,Does the tract have at least 35 acres in it?,Contains agricultural value,Housing burden (percent),Share of homes with no kitchen or indoor plumbing (percent),Total population,Median household income (% of state median household income),Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Percent of individuals < 100% Federal Poverty Line,Percent of individuals < 150% Federal Poverty Line,Percent of individuals below 200% Federal Poverty Line,Area Median Income (State or metropolitan),Median household income in the past 12 months,Energy burden,FEMA Risk Index Expected Annual Loss Score,Urban Heuristic Flag,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent pre-1960s housing (lead paint indicator),Individuals under 5 years old,Individuals over 64 years old,Linguistic isolation (percent),Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Percent individuals age 25 or over with less than high school degree,Unemployment (percent),Median value ($) of owner-occupied housing units,Percent enrollment in college or graduate school,Percent of population not currently enrolled in college or graduate school,Expected building loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected population loss rate (Natural Hazards Risk Index),Percent individuals age 25 or over with less than high school degree in 2009,Percentage households below 100% of federal poverty line in 2009,Unemployment (percent) in 2009,Unemployment (percent) in 2010,Percent of individuals less than 100% Federal Poverty Line in 2010,Total population in 2009,Summer days above 90F,Percent low access to healthy food,Percent impenetrable surface areas,Leaky underground storage tanks,DOT Travel Barriers Score,Share of properties at risk of flood in 30 years,Share of properties at risk of fire in 30 years,Share of the tract's land area that is covered by impervious surface or cropland as a percent,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",Third grade reading proficiency,Median household income as a percent of area median income,Life expectancy (years),Median household income as a percent of territory median income in 2009,Housing burden (percent) (percentile),Share of homes with no kitchen or indoor plumbing (percent) (percentile),Total population (percentile),Median household income (% of state median household income) (percentile),Current asthma among adults aged greater than or equal to 18 years (percentile),Coronary heart disease among adults aged greater than or equal to 18 years (percentile),Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years (percentile),Current lack of health insurance among adults aged 18-64 years (percentile),Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile),Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years (percentile),Percent of individuals < 100% Federal Poverty Line (percentile),Percent of individuals < 150% Federal Poverty Line (percentile),Percent of individuals below 200% Federal Poverty Line (percentile),Area Median Income (State or metropolitan) (percentile),Median household income in the past 12 months (percentile),Energy burden (percentile),FEMA Risk Index Expected Annual Loss Score (percentile),Urban Heuristic Flag (percentile),Air toxics cancer risk (percentile),Respiratory hazard index (percentile),Diesel particulate matter exposure (percentile),PM2.5 in the air (percentile),Ozone (percentile),Traffic proximity and volume (percentile),Proximity to Risk Management Plan (RMP) facilities (percentile),Proximity to hazardous waste sites (percentile),Proximity to NPL sites (percentile),Wastewater discharge (percentile),Percent pre-1960s housing (lead paint indicator) (percentile),Individuals under 5 years old (percentile),Individuals over 64 years old (percentile),Linguistic isolation (percent) (percentile),Percent of households in linguistic isolation (percentile),Poverty (Less than 200% of federal poverty line) (percentile),Percent individuals age 25 or over with less than high school degree (percentile),Unemployment (percent) (percentile),Median value ($) of owner-occupied housing units (percentile),Percent enrollment in college or graduate school (percentile),Percent of population not currently enrolled in college or graduate school (percentile),Expected building loss rate (Natural Hazards Risk Index) (percentile),Expected agricultural loss rate (Natural Hazards Risk Index) (percentile),Expected population loss rate (Natural Hazards Risk Index) (percentile),Percent individuals age 25 or over with less than high school degree in 2009 (percentile),Percentage households below 100% of federal poverty line in 2009 (percentile),Unemployment (percent) in 2009 (percentile),Unemployment (percent) in 2010 (percentile),Percent of individuals less than 100% Federal Poverty Line in 2010 (percentile),Total population in 2009 (percentile),Summer days above 90F (percentile),Percent low access to healthy food (percentile),Percent impenetrable surface areas (percentile),Leaky underground storage tanks (percentile),DOT Travel Barriers Score (percentile),Share of properties at risk of flood in 30 years (percentile),Share of properties at risk of fire in 30 years (percentile),Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Low third grade reading proficiency (percentile),Low median household income as a percent of area median income (percentile),Low life expectancy (percentile),Low median household income as a percent of territory median income in 2009 (percentile),Total population in 2009 (island areas) and 2019 (states and PR),Total threshold criteria exceeded,Exceeds FPL200 threshold,Percent higher ed enrollment rate is less than 20%,Is low income and has a low percent of higher ed students?,Greater than or equal to the 90th percentile for expected population loss,Greater than or equal to the 90th percentile for expected agricultural loss,Greater than or equal to the 90th percentile for expected building loss,At least one climate threshold exceeded,"Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students?",Climate Factor (Definition M),Greater than or equal to the 90th percentile for energy burden,Greater than or equal to the 90th percentile for pm2.5 exposure,At least one energy threshold exceeded,"Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students?",Energy Factor (Definition M),Greater than or equal to the 90th percentile for diesel particulate matter,Greater than or equal to the 90th percentile for traffic proximity,At least one traffic threshold exceeded,"Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students?",Transportation Factor (Definition M),Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile,Greater than or equal to the 90th percentile for housing burden,At least one housing threshold exceeded,"Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students?",Housing Factor (Definition M),Greater than or equal to the 90th percentile for RMP proximity,Greater than or equal to the 90th percentile for NPL (superfund sites) proximity,Greater than or equal to the 90th percentile for proximity to hazardous waste sites,At least one pollution threshold exceeded,"Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students?",Pollution Factor (Definition M),Greater than or equal to the 90th percentile for wastewater discharge,At least one water threshold exceeded,"Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students?",Water Factor (Definition M),Greater than or equal to the 90th percentile for diabetes,Greater than or equal to the 90th percentile for asthma,Greater than or equal to the 90th percentile for heart disease,Greater than or equal to the 90th percentile for low life expectancy,At least one health threshold exceeded,"Greater than or equal to the 90th percentile for diabetes, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students?",Health Factor (Definition M),Low high school education and low percent of higher ed students,Greater than or equal to the 90th percentile for unemployment,Greater than or equal to the 90th percentile for low median household income as a percent of area median income,Greater than or equal to the 90th percentile for households in linguistic isolation,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level,"Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students?",Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),Unemployment (percent) in 2009 for island areas (percentile),Unemployment (percent) in 2009 exceeds 90th percentile,Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 exceeds 90th percentile,Low median household income as a percent of territory median income in 2009 exceeds 90th percentile,Low high school education in 2009 (island areas),Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?,At least one workforce threshold exceeded,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition M),Total categories exceeded,Definition M (communities),Any Non-Workforce Factor (Definition M),Definition M (percentile),Is low income (imputed and adjusted)?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years,Greater than or equal to the 90th percentile for expected population loss rate and is low income?,Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?,Greater than or equal to the 90th percentile for expected building loss rate and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?,Climate Factor (Definition N),Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?,Greater than or equal to the 90th percentile for energy burden and is low income?,Energy Factor (Definition N),Greater than or equal to the 90th percentile for DOT travel barriers,Greater than or equal to the 90th percentile for diesel particulate matter and is low income?,Greater than or equal to the 90th percentile for traffic proximity and is low income?,Greater than or equal to the 90th percentile for DOT transit barriers and is low income?,Transportation Factor (Definition N),Tract-level redlining score meets or exceeds 3.25 and is low income,Greater than or equal to the 90th percentile for share of homes without indoor plumbing or a kitchen,Greater than or equal to the 90th percentile for share of homes with no kitchen or indoor plumbing and is low income?,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?,Greater than or equal to the 90th percentile for housing burden and is low income?,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?,Housing Factor (Definition N),Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?,Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?,Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?,Pollution Factor (Definition N),Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,Water Factor (Definition N),Greater than or equal to the 90th percentile for diabetes and is low income?,Greater than or equal to the 90th percentile for asthma and is low income?,Greater than or equal to the 90th percentile for heart disease and is low income?,Greater than or equal to the 90th percentile for low life expectancy and is low income?,Health Factor (Definition N),Low high school education,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS education?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education?,Greater than or equal to the 90th percentile for unemployment and has low HS education?,Workforce Factor (Definition N),Definition N (communities),Definition N (communities) (percentile)
-01073001100,True,True,True,True,0.2752043596730245,0.0,4781.0,0.7327449738800064,11.2,7.2,6.7,16.6,19.3,15.1,0.150375939849624,0.318796992481203,0.3744360902255639,57447.0,37030.0,0.049,18.7674524286,1.0,40.0,0.5,0.467489734286576,9.8735797260274,43.056760130719,181.621925132718,2.0427358988323,0.702342755246247,0.134193041307899,4.45238981883771,0.168806466951973,0.035557414766785,0.203932231750679,0.0,0.0,0.374436090225563,0.0821917808219178,0.0092071611253196,85500.0,0.0890751899397432,0.9109248100602568,0.0004047858,5.6328e-05,2.8039e-06,,,,0.1536983669548511,0.3189099613330878,,62.666668,0.068036923,0.171,1.96440511031451,47.695227725,0.0754274220583305,0.6620851491786792,-77.7525,0.2853609002858206,58.143433,0.6445941476491375,70.3,,0.6466760729305078,0.2159833426939357,0.6290185267766651,0.2601978513507951,0.8509696039125366,0.7264920810941454,0.4789587420739856,0.6191105803406409,0.965388552418323,0.697012994398476,0.6204255784694491,0.7319894972922707,0.6305043487774192,0.3145069836211475,0.1524256393370651,0.864954517474865,0.6038301323911519,0.5972204988211937,0.9070825388177608,0.8818509942794879,0.8407790792699537,0.8257128232087766,0.5755156814188676,0.3920895082932574,0.9007580978635424,0.4820205132363076,0.7531654977635437,0.9619599422457518,0.3979135417088958,0.1737408953933055,0.7659355954649262,0.1287706711725437,0.13169416629505,0.6347481790786611,0.4189065592792301,0.029797296373751,0.1130218397675614,0.7459773722926589,0.2540362752992234,0.7846412062513758,0.2153147384849333,0.6143028498159407,,,,0.9349594607528132,0.8950599559730369,,0.7537922665342821,0.8019598155467721,0.4126953421856217,0.521114579532709,0.4517484245644384,0.4977059209088922,0.8410893082809093,,0.607629501459933,0.990724418702258,0.8218135517196475,0.97046998263836,,4781.0,0,False,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,True,False,False,True,False,False,False,False,True,False,False,False,True,False,False,True,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0.1536983669548511,,False,0.3189099613330878,,False,False,False,False,False,False,False,False,False,0.0,False,False,0,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,0
-01073001400,True,True,True,True,0.1823529411764705,0.0047058823529411,1946.0,0.7136694633528574,11.1,9.1,7.3,21.4,22.4,17.4,0.2816032887975334,0.3679342240493319,0.4835560123329907,57447.0,36066.0,0.07,17.3011023381,1.0,40.0,0.6,0.655319095139786,9.945103013698628,43.1266823529412,3260.33374354854,1.81915896353987,3.34035680534013,0.214095348702766,0.103297800913177,0.647212543554006,0.054984583761562,0.189105858170606,0.0245098039215686,0.024509803921569,0.48355601233299,0.1742543171114599,0.1150121065375302,67800.0,0.0771549125979505,0.9228450874020494,0.0008951111,5.1282e-06,2.3791e-06,,,,0.0804953560371517,0.2950894905920146,,61.666668,0.087159691,0.34900002,3.16184976454882,44.7571359825,0.2384615384615384,0.0,-56.8746,0.4064010997350401,93.77919,0.6278134628440127,71.0,,0.3421186011150532,0.5051574635963891,0.0916001135119795,0.240302951305517,0.8385794307486707,0.9217563763541756,0.6048579715089994,0.7894025988796952,0.9878088657624612,0.8447283118655634,0.8689486351950112,0.8013648049887862,0.7892483999781194,0.3145069836211475,0.1404620788058391,0.970802270706518,0.5282998116553705,0.5972204988211937,0.9070825388177608,0.9704848815036776,0.9380686461454644,0.8391046304110233,0.5827649654828936,0.9563394697362702,0.8799745949379062,0.800259455953298,0.8653801975648978,0.8431750027766466,0.8462723476709774,0.471128768530155,0.6930041485925866,0.5867081244286861,0.5847015580870529,0.7916514641694031,0.7516347007030237,0.9067399297439892,0.0522639122516786,0.6434566620719774,0.356556985519905,0.9166162227602904,0.0865380767537716,0.558933421571466,,,,0.6917513228236646,0.8737301229199994,,0.7501654807214959,0.8647617479139218,0.6268497920495212,0.6418426778016514,0.3716517703914219,0.8849410093948001,0.3366245885930925,,0.7883908294582027,0.9537899773356836,0.8364273002184828,0.959938777375042,,1946.0,9,True,True,True,False,False,True,True,False,False,True,True,True,False,True,False,True,True,True,True,True,True,True,True,False,False,True,False,False,False,False,False,False,False,False,False,False,False,False,False,False,False,True,False,True,True,True,True,False,True,True,True,True,True,False,False,False,False,False,False,True,0.0804953560371517,,False,0.2950894905920146,,False,False,False,False,False,False,True,True,True,6.0,True,True,1,True,False,False,False,False,True,False,False,True,False,True,True,False,True,True,False,True,True,False,False,False,False,False,False,True,False,False,False,False,False,False,False,False,True,False,True,True,True,True,False,False,False,True,True,True,1
+GEOID10_TRACT,Persistent Poverty Census Tract,Tract-level redlining score meets or exceeds 3.25,Does the tract have at least 35 acres in it?,Contains agricultural value,Housing burden (percent),Share of homes with no kitchen or indoor plumbing (percent),Total population,Median household income (% of state median household income),Current asthma among adults aged greater than or equal to 18 years,Coronary heart disease among adults aged greater than or equal to 18 years,Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years,Current lack of health insurance among adults aged 18-64 years,Diagnosed diabetes among adults aged greater than or equal to 18 years,Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years,Percent of individuals < 100% Federal Poverty Line,Percent of individuals < 150% Federal Poverty Line,Percent of individuals below 200% Federal Poverty Line,Area Median Income (State or metropolitan),Median household income in the past 12 months,Energy burden,FEMA Risk Index Expected Annual Loss Score,Urban Heuristic Flag,Air toxics cancer risk,Respiratory hazard index,Diesel particulate matter exposure,PM2.5 in the air,Ozone,Traffic proximity and volume,Proximity to Risk Management Plan (RMP) facilities,Proximity to hazardous waste sites,Proximity to NPL sites,Wastewater discharge,Percent pre-1960s housing (lead paint indicator),Individuals under 5 years old,Individuals over 64 years old,Linguistic isolation (percent),Percent of households in linguistic isolation,Poverty (Less than 200% of federal poverty line),Percent individuals age 25 or over with less than high school degree,Unemployment (percent),Median value ($) of owner-occupied housing units,Percent enrollment in college or graduate school,Percent of population not currently enrolled in college or graduate school,Expected building loss rate (Natural Hazards Risk Index),Expected agricultural loss rate (Natural Hazards Risk Index),Expected population loss rate (Natural Hazards Risk Index),Percent individuals age 25 or over with less than high school degree in 2009,Percentage households below 100% of federal poverty line in 2009,Unemployment (percent) in 2009,Unemployment (percent) in 2010,Percent of individuals less than 100% Federal Poverty Line in 2010,Total population in 2009,Summer days above 90F,Percent low access to healthy food,Percent impenetrable surface areas,Leaky underground storage tanks,DOT Travel Barriers Score,Share of properties at risk of flood in 30 years,Share of properties at risk of fire in 30 years,Share of the tract's land area that is covered by impervious surface or cropland as a percent,"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted",Third grade reading proficiency,Median household income as a percent of area median income,Life expectancy (years),Median household income as a percent of territory median income in 2009,Housing burden (percent) (percentile),Share of homes with no kitchen or indoor plumbing (percent) (percentile),Total population (percentile),Median household income (% of state median household income) (percentile),Current asthma among adults aged greater than or equal to 18 years (percentile),Coronary heart disease among adults aged greater than or equal to 18 years (percentile),Cancer (excluding skin cancer) among adults aged greater than or equal to 18 years (percentile),Current lack of health insurance among adults aged 18-64 years (percentile),Diagnosed diabetes among adults aged greater than or equal to 18 years (percentile),Physical health not good for greater than or equal to 14 days among adults aged greater than or equal to 18 years (percentile),Percent of individuals < 100% Federal Poverty Line (percentile),Percent of individuals < 150% Federal Poverty Line (percentile),Percent of individuals below 200% Federal Poverty Line (percentile),Area Median Income (State or metropolitan) (percentile),Median household income in the past 12 months (percentile),Energy burden (percentile),FEMA Risk Index Expected Annual Loss Score (percentile),Urban Heuristic Flag (percentile),Air toxics cancer risk (percentile),Respiratory hazard index (percentile),Diesel particulate matter exposure (percentile),PM2.5 in the air (percentile),Ozone (percentile),Traffic proximity and volume (percentile),Proximity to Risk Management Plan (RMP) facilities (percentile),Proximity to hazardous waste sites (percentile),Proximity to NPL sites (percentile),Wastewater discharge (percentile),Percent pre-1960s housing (lead paint indicator) (percentile),Individuals under 5 years old (percentile),Individuals over 64 years old (percentile),Linguistic isolation (percent) (percentile),Percent of households in linguistic isolation (percentile),Poverty (Less than 200% of federal poverty line) (percentile),Percent individuals age 25 or over with less than high school degree (percentile),Unemployment (percent) (percentile),Median value ($) of owner-occupied housing units (percentile),Percent enrollment in college or graduate school (percentile),Percent of population not currently enrolled in college or graduate school (percentile),Expected building loss rate (Natural Hazards Risk Index) (percentile),Expected agricultural loss rate (Natural Hazards Risk Index) (percentile),Expected population loss rate (Natural Hazards Risk Index) (percentile),Percent individuals age 25 or over with less than high school degree in 2009 (percentile),Percentage households below 100% of federal poverty line in 2009 (percentile),Unemployment (percent) in 2009 (percentile),Unemployment (percent) in 2010 (percentile),Percent of individuals less than 100% Federal Poverty Line in 2010 (percentile),Total population in 2009 (percentile),Summer days above 90F (percentile),Percent low access to healthy food (percentile),Percent impenetrable surface areas (percentile),Leaky underground storage tanks (percentile),DOT Travel Barriers Score (percentile),Share of properties at risk of flood in 30 years (percentile),Share of properties at risk of fire in 30 years (percentile),Share of the tract's land area that is covered by impervious surface or cropland as a percent (percentile),"Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Low third grade reading proficiency (percentile),Low median household income as a percent of area median income (percentile),Low life expectancy (percentile),Low median household income as a percent of territory median income in 2009 (percentile),Total population in 2009 (island areas) and 2019 (states and PR),Total threshold criteria exceeded,Exceeds FPL200 threshold,Percent higher ed enrollment rate is less than 20%,Is low income and has a low percent of higher ed students?,Greater than or equal to the 90th percentile for expected population loss,Greater than or equal to the 90th percentile for expected agricultural loss,Greater than or equal to the 90th percentile for expected building loss,At least one climate threshold exceeded,"Greater than or equal to the 90th percentile for expected population loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected agriculture loss rate, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for expected building loss rate, is low income, and has a low percent of higher ed students?",Climate Factor (Definition M),Greater than or equal to the 90th percentile for energy burden,Greater than or equal to the 90th percentile for pm2.5 exposure,At least one energy threshold exceeded,"Greater than or equal to the 90th percentile for PM2.5 exposure, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for energy burden, is low income, and has a low percent of higher ed students?",Energy Factor (Definition M),Greater than or equal to the 90th percentile for diesel particulate matter,Greater than or equal to the 90th percentile for traffic proximity,At least one traffic threshold exceeded,"Greater than or equal to the 90th percentile for diesel particulate matter, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for traffic proximity, is low income, and has a low percent of higher ed students?",Transportation Factor (Definition M),Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile,Greater than or equal to the 90th percentile for housing burden,At least one housing threshold exceeded,"Greater than or equal to the 90th percentile for lead paint, the median house value is less than 90th percentile, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for housing burden, is low income, and has a low percent of higher ed students?",Housing Factor (Definition M),Greater than or equal to the 90th percentile for RMP proximity,Greater than or equal to the 90th percentile for NPL (superfund sites) proximity,Greater than or equal to the 90th percentile for proximity to hazardous waste sites,At least one pollution threshold exceeded,"Greater than or equal to the 90th percentile for proximity to RMP sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to superfund sites, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for proximity to hazardous waste facilities, is low income, and has a low percent of higher ed students?",Pollution Factor (Definition M),Greater than or equal to the 90th percentile for wastewater discharge,At least one water threshold exceeded,"Greater than or equal to the 90th percentile for wastewater discharge, is low income, and has a low percent of higher ed students?",Water Factor (Definition M),Greater than or equal to the 90th percentile for diabetes,Greater than or equal to the 90th percentile for asthma,Greater than or equal to the 90th percentile for heart disease,Greater than or equal to the 90th percentile for low life expectancy,At least one health threshold exceeded,"Greater than or equal to the 90th percentile for diabetes, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for asthma, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for heart disease, is low income, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low life expectancy, is low income, and has a low percent of higher ed students?",Health Factor (Definition M),Low high school education and low percent of higher ed students,Greater than or equal to the 90th percentile for unemployment,Greater than or equal to the 90th percentile for low median household income as a percent of area median income,Greater than or equal to the 90th percentile for households in linguistic isolation,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level,"Greater than or equal to the 90th percentile for households in linguistic isolation, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for households at or below 100% federal poverty level, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for low median household income as a percent of area median income, has low HS attainment, and has a low percent of higher ed students?","Greater than or equal to the 90th percentile for unemployment, has low HS attainment, and has a low percent of higher ed students?",Unemployment (percent) in 2009 (island areas) and 2010 (states and PR),Unemployment (percent) in 2009 for island areas (percentile),Unemployment (percent) in 2009 exceeds 90th percentile,Percentage households below 100% of federal poverty line in 2009 (island areas) and 2010 (states and PR),Percentage households below 100% of federal poverty line in 2009 for island areas (percentile),Percentage households below 100% of federal poverty line in 2009 exceeds 90th percentile,Low median household income as a percent of territory median income in 2009 exceeds 90th percentile,Low high school education in 2009 (island areas),Greater than or equal to the 90th percentile for unemployment and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education in 2009 (island areas)?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education in 2009 (island areas)?,At least one workforce threshold exceeded,Both workforce socioeconomic indicators exceeded,Workforce Factor (Definition M),Total categories exceeded,Definition M (communities),Any Non-Workforce Factor (Definition M),Definition M (percentile),Is low income (imputed and adjusted)?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years,Greater than or equal to the 90th percentile for expected population loss rate and is low income?,Greater than or equal to the 90th percentile for expected agriculture loss rate and is low income?,Greater than or equal to the 90th percentile for expected building loss rate and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of flood in 30 years and is low income?,Greater than or equal to the 90th percentile for share of properties at risk of fire in 30 years and is low income?,Climate Factor (Definition N),Greater than or equal to the 90th percentile for PM2.5 exposure and is low income?,Greater than or equal to the 90th percentile for energy burden and is low income?,Energy Factor (Definition N),Greater than or equal to the 90th percentile for DOT travel barriers,Greater than or equal to the 90th percentile for diesel particulate matter and is low income?,Greater than or equal to the 90th percentile for traffic proximity and is low income?,Greater than or equal to the 90th percentile for DOT transit barriers and is low income?,Transportation Factor (Definition N),Tract-level redlining score meets or exceeds 3.25 and is low income,Greater than or equal to the 90th percentile for share of homes without indoor plumbing or a kitchen,Greater than or equal to the 90th percentile for share of homes with no kitchen or indoor plumbing and is low income?,Greater than or equal to the 90th percentile for lead paint and the median house value is less than 90th percentile and is low income?,Greater than or equal to the 90th percentile for housing burden and is low income?,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent,Greater than or equal to the 90th percentile for share of the tract's land area that is covered by impervious surface or cropland as a percent and is low income?,Housing Factor (Definition N),Greater than or equal to the 90th percentile for proximity to RMP sites and is low income?,Greater than or equal to the 90th percentile for proximity to superfund sites and is low income?,Greater than or equal to the 90th percentile for proximity to hazardous waste facilities and is low income?,Pollution Factor (Definition N),Greater than or equal to the 90th percentile for leaky underwater storage tanks,Greater than or equal to the 90th percentile for wastewater discharge and is low income?,Greater than or equal to the 90th percentile for leaky underground storage tanks and is low income?,Water Factor (Definition N),Greater than or equal to the 90th percentile for diabetes and is low income?,Greater than or equal to the 90th percentile for asthma and is low income?,Greater than or equal to the 90th percentile for heart disease and is low income?,Greater than or equal to the 90th percentile for low life expectancy and is low income?,Health Factor (Definition N),Low high school education,Greater than or equal to the 90th percentile for households in linguistic isolation and has low HS education?,Greater than or equal to the 90th percentile for households at or below 100% federal poverty level and has low HS education?,Greater than or equal to the 90th percentile for low median household income as a percent of area median income and has low HS education?,Greater than or equal to the 90th percentile for unemployment and has low HS education?,Workforce Factor (Definition N),Definition N (communities),Definition N (communities) (percentile),Meets the less stringent low income criterion for the adjacency index?,Definition N (communities) (average of neighbors),Is the tract surrounded by disadvantaged communities?,Definition N (communities) (including adjacency index)
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diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_data_expected.pkl
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diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_transformed_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/score_transformed_expected.pkl
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diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl
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diff --git a/data/data-pipeline/data_pipeline/tests/score/__init__.py b/data/data-pipeline/data_pipeline/tests/score/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/__init__.py b/data/data-pipeline/data_pipeline/tests/score/test_utils/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/scores.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/scores.csv
new file mode 100644
index 000000000..d11e7ff4f
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/scores.csv
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diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/us.geojson
new file mode 100644
index 000000000..13a4d5b9c
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+]
+}
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py b/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py
new file mode 100644
index 000000000..fa18611a9
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/test_adjacency.py
@@ -0,0 +1,71 @@
+# pylint: disable=protected-access
+# flake8: noqa=F841
+from pathlib import Path
+from unittest import mock
+from functools import partial
+from contextlib import contextmanager
+
+import pytest
+import pandas as pd
+from data_pipeline.score.utils import (
+    calculate_tract_adjacency_scores as original_calculate_tract_adjacency_score,
+)
+from data_pipeline.etl.sources.geo_utils import get_tract_geojson
+from data_pipeline.score import field_names
+
+
+@contextmanager
+def patch_calculate_tract_adjacency_scores():
+    tract_data = Path(__file__).parent / "data" / "us.geojson"
+    get_tract_geojson_mock = partial(
+        get_tract_geojson, _tract_data_path=tract_data
+    )
+    with mock.patch(
+        "data_pipeline.score.utils.get_tract_geojson",
+        new=get_tract_geojson_mock,
+    ):
+        yield original_calculate_tract_adjacency_score
+
+
+@pytest.fixture
+def score_data():
+    score_csv = Path(__file__).parent / "data" / "scores.csv"
+    return pd.read_csv(
+        score_csv, dtype={field_names.GEOID_TRACT_FIELD: str, "included": bool}
+    )
+
+
+def test_all_adjacent_are_true(score_data):
+    score_data["included"] = True
+    score_data.loc[
+        score_data.GEOID10_TRACT == "24027603004", "included"
+    ] = False
+    with patch_calculate_tract_adjacency_scores() as calculate_tract_adjacency_scores:
+        adjancency_scores = calculate_tract_adjacency_scores(
+            score_data, "included"
+        )
+        assert (
+            adjancency_scores.loc[
+                adjancency_scores.GEOID10_TRACT == "24027603004",
+                "included" + field_names.ADJACENCY_INDEX_SUFFIX,
+            ].iloc[0]
+            == 1.0
+        )
+
+
+def test_all_adjacent_are_false(score_data):
+    score_data["included"] = False
+    score_data.loc[
+        score_data.GEOID10_TRACT == "24027603004", "included"
+    ] = False
+    with patch_calculate_tract_adjacency_scores() as calculate_tract_adjacency_scores:
+        adjancency_scores = calculate_tract_adjacency_scores(
+            score_data, "included"
+        )
+        assert (
+            adjancency_scores.loc[
+                adjancency_scores.GEOID10_TRACT == "24027603004",
+                "included" + field_names.ADJACENCY_INDEX_SUFFIX,
+            ].iloc[0]
+            == 0.0
+        )