project analysis start
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267
analysis/collab_patterns_outcomes.ipynb
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267
analysis/collab_patterns_outcomes.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Analysis of Collaboration Patterns and Outcomes:\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_824605/3927317048.py:16: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
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" collab_analysis = pd.read_sql(\"\"\"\n",
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"/tmp/ipykernel_824605/3927317048.py:32: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
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" temporal_analysis = pd.read_sql(\"\"\"\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" program_name project_count \\\n",
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"0 Sustainable Agricultural Lands Conservation Pr... 96 \n",
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"1 Water-Energy Efficiency 35 \n",
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"2 Forest Health Program 147 \n",
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"3 Low Carbon Transit Operations Program 770 \n",
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"4 Low-Income Weatherization Program 51 \n",
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"5 Climate Resilience Planning 74 \n",
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"6 Climate Smart Agriculture 1318 \n",
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"7 Coastal Resilience Planning 17 \n",
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"8 Community Air Protection 222 \n",
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"9 Community Assistance for Climate Equity Program 33 \n",
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"10 Fire Prevention Program 600 \n",
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"11 Fluorinated Gases Emission Reduction Incentives 15 \n",
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"12 Food Production Investment Program 56 \n",
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"13 Forest Carbon Plan Implementation 388 \n",
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"14 Funding Agricultural Replacement Measures for ... 30 \n",
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"15 Low-Carbon Economy Workforce 30 \n",
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"16 Low-Carbon Fuels Production Program 4 \n",
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"17 Low Carbon Transportation 129 \n",
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"18 Prescribed Fire and Smoke Monitoring Program 51 \n",
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"19 Regional Forest and Fire Capacity 7 \n",
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"20 Renewable Energy for Agriculture Program 45 \n",
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"21 Safe and Affordable Drinking Water Fund 67 \n",
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"22 SB 1383 Local Assistance Grant Program 467 \n",
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"23 Training and Workforce Development Program 766 \n",
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"24 Transformative Climate Communities 99 \n",
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"25 Transit and Intercity Rail Capital Program 52 \n",
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"26 Transition to a Carbon-Neutral Economy 2 \n",
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"27 Urban and Community Forestry Program 115 \n",
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"28 Urban Greening Program 88 \n",
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"29 Waste Diversion 121 \n",
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"30 Wetlands and Watershed Restoration 8 \n",
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"31 Wildfire Response and Readiness 63 \n",
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"32 Woodsmoke Reduction Program 85 \n",
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"33 Active Transportation Program 3 \n",
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"34 Workforce Training and Development 14 \n",
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"35 Affordable Housing and Sustainable Communities... 94 \n",
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"36 Climate Adaptation and Resiliency Program 17 \n",
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"37 Climate Change Research Program 22 \n",
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"38 Climate Ready Program 11 \n",
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"\n",
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" avg_ghg_efficiency dac_rate avg_partners total_funding \n",
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"0 0.072917 0.010417 0.250000 1.224242e+08 \n",
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"1 0.066094 0.440761 0.000000 5.247043e+07 \n",
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"2 0.027211 0.000000 0.000000 2.094100e+08 \n",
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"3 0.002597 0.092208 0.127273 7.787979e+08 \n",
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"4 0.001898 0.917273 0.000000 1.583588e+08 \n",
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"5 0.000000 0.000000 0.000000 8.680291e+06 \n",
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"6 0.000000 0.053111 0.028832 3.474239e+08 \n",
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"7 0.000000 0.000000 0.000000 2.667413e+06 \n",
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"8 0.000000 0.000000 0.000193 5.295232e+08 \n",
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"9 0.000000 0.081081 0.135135 9.250430e+06 \n",
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"10 0.000000 0.000000 0.003333 5.962741e+08 \n",
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"11 0.000000 0.000000 0.000000 1.000001e+06 \n",
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"12 0.000000 0.000000 0.142857 1.177915e+08 \n",
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"13 0.000000 0.000000 0.002577 1.051691e+08 \n",
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"14 0.000000 0.000000 0.000000 2.540456e+08 \n",
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"15 0.000000 0.000000 0.000000 2.442836e+07 \n",
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"16 0.000000 0.000000 0.000000 1.250000e+07 \n",
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"17 0.000000 0.370603 0.004289 1.840180e+09 \n",
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"18 0.000000 0.000000 0.000000 3.903695e+06 \n",
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"19 0.000000 0.000000 0.000000 6.244191e+06 \n",
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"20 0.000000 0.000000 0.000000 9.500000e+06 \n",
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"21 0.000000 0.000000 0.356436 1.206498e+08 \n",
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"22 0.000000 0.000000 0.025696 5.655802e+07 \n",
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"23 0.000000 0.000000 0.084856 4.716136e+07 \n",
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"24 0.000000 0.000000 0.000000 2.027377e+08 \n",
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"25 0.000000 0.184397 0.170213 8.145985e+08 \n",
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"26 0.000000 0.000000 0.000000 2.560940e+06 \n",
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"27 0.000000 0.183369 0.000000 5.919797e+07 \n",
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"28 0.000000 0.183908 0.000000 1.387790e+08 \n",
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"29 0.000000 0.089844 0.074219 1.185714e+08 \n",
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"30 0.000000 0.000000 0.000000 1.617130e+07 \n",
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"31 0.000000 0.000000 0.000000 3.103736e+07 \n",
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"32 0.000000 0.000000 0.000000 7.895909e+06 \n",
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"33 0.000000 0.666667 0.000000 1.000000e+07 \n",
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"34 0.000000 0.000000 0.000000 1.000000e+06 \n",
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"35 0.000000 0.159574 0.021277 1.211578e+09 \n",
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"36 0.000000 0.000000 0.058824 1.257480e+07 \n",
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"37 0.000000 0.000000 0.136364 3.063090e+07 \n",
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"38 0.000000 0.000000 0.454545 1.271824e+07 \n",
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"\n",
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"Temporal Trends Analysis:\n"
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]
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}
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],
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"source": [
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"import psycopg2\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Create connection\n",
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"conn = psycopg2.connect(\n",
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" dbname='calif_equity',\n",
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" user='postgres',\n",
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" password='MandyLinkToby3',\n",
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" host='192.168.0.74',\n",
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" port=5432 \n",
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")\n",
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"\n",
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"# Collaboration patterns - modified to handle duplicates\n",
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"print(\"Analysis of Collaboration Patterns and Outcomes:\")\n",
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"collab_analysis = pd.read_sql(\"\"\"\n",
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" SELECT \n",
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" p.program_name,\n",
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" COUNT(DISTINCT p.project_id) as project_count,\n",
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" AVG(p.ghg_efficiency) as avg_ghg_efficiency,\n",
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" AVG(CASE WHEN p.dac_benefit THEN 1 ELSE 0 END) as dac_rate,\n",
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" AVG(p.partner_count) as avg_partners,\n",
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" SUM(p.total_funding) as total_funding\n",
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" FROM project_efficiency p\n",
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" GROUP BY p.program_name\n",
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" ORDER BY avg_ghg_efficiency DESC\n",
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"\"\"\", conn)\n",
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"print(collab_analysis)\n",
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"\n",
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"# Temporal analysis - modified to handle duplicates\n",
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"print(\"\\nTemporal Trends Analysis:\")\n",
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"temporal_analysis = pd.read_sql(\"\"\"\n",
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" SELECT \n",
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" EXTRACT(YEAR FROM cp.date_operational) as year,\n",
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" COUNT(DISTINCT cp.project_id) as project_count,\n",
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" AVG(pe.ghg_efficiency) as avg_ghg_efficiency,\n",
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" AVG(CASE WHEN pe.dac_benefit THEN 1 ELSE 0 END) as dac_rate,\n",
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" AVG(pe.partner_count) as avg_partners,\n",
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" SUM(DISTINCT pe.total_funding) as total_funding\n",
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" FROM project_efficiency pe\n",
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" JOIN cci_projects cp ON pe.project_id = cp.project_id\n",
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" WHERE cp.date_operational BETWEEN '2015-01-01' AND '2024-12-31'\n",
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" GROUP BY EXTRACT(YEAR FROM cp.date_operational)\n",
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" ORDER BY year\n",
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"\"\"\", conn)\n",
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"print(temporal_analysis)\n",
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"\n",
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"# Regional analysis - modified to handle duplicates\n",
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"print(\"\\nRegional Efficiency Analysis:\")\n",
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"regional_analysis = pd.read_sql(\"\"\"\n",
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" WITH distinct_projects AS (\n",
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" SELECT DISTINCT ON (pe.project_id)\n",
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" pe.project_id,\n",
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" pe.ghg_efficiency,\n",
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" pe.dac_benefit,\n",
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" pe.partner_count,\n",
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" pe.total_funding,\n",
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" cp.county\n",
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" FROM project_efficiency pe\n",
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" JOIN cci_projects cp ON pe.project_id = cp.project_id\n",
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" WHERE cp.county NOT LIKE '%,%'\n",
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" )\n",
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" SELECT \n",
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" CASE \n",
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" WHEN county IN ('Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange')\n",
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" THEN 'Urban'\n",
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" WHEN county IN ('Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare')\n",
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" THEN 'Central Valley'\n",
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" ELSE 'Other'\n",
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" END as region_type,\n",
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" COUNT(*) as project_count,\n",
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" AVG(ghg_efficiency) as avg_ghg_efficiency,\n",
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" AVG(CASE WHEN dac_benefit THEN 1 ELSE 0 END) as dac_rate,\n",
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" AVG(partner_count) as avg_partners,\n",
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" SUM(total_funding) as total_funding\n",
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" FROM distinct_projects\n",
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" GROUP BY \n",
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" CASE \n",
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" WHEN county IN ('Los Angeles', 'San Francisco', 'Alameda', 'San Diego', 'Orange')\n",
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" THEN 'Urban'\n",
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" WHEN county IN ('Fresno', 'Kern', 'Kings', 'Madera', 'Merced', 'San Joaquin', 'Stanislaus', 'Tulare')\n",
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" THEN 'Central Valley'\n",
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" ELSE 'Other'\n",
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" END\n",
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"\"\"\", conn)\n",
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"print(regional_analysis)\n",
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"\n",
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"# Create visualizations\n",
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"fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))\n",
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"\n",
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"# 1. Temporal trends\n",
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"if not temporal_analysis.empty:\n",
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" temporal_analysis.plot(x='year', y=['avg_ghg_efficiency', 'dac_rate'], \n",
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" kind='line', ax=ax1)\n",
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" ax1.set_title('Efficiency and Equity Over Time')\n",
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" ax1.set_ylabel('Rate')\n",
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"\n",
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"# 2. Project funding by region\n",
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"if not regional_analysis.empty:\n",
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" regional_analysis.plot(kind='bar', x='region_type', y='total_funding', \n",
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" ax=ax2)\n",
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" ax2.set_title('Total Funding by Region')\n",
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" ax2.set_ylabel('Total Funding ($)')\n",
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"\n",
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"# 3. Program performance\n",
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"if not collab_analysis.empty:\n",
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" # Sort by efficiency and take top 10 for better visualization\n",
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" top_programs = collab_analysis.nlargest(10, 'avg_ghg_efficiency')\n",
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" top_programs.plot(kind='bar', x='program_name', \n",
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" y=['avg_ghg_efficiency', 'dac_rate'],\n",
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" ax=ax3)\n",
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" ax3.set_title('Top 10 Programs by GHG Efficiency')\n",
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" ax3.set_ylabel('Rate')\n",
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" plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45, ha='right')\n",
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"\n",
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"# 4. Partners vs Efficiency scatter\n",
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"if not temporal_analysis.empty:\n",
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" temporal_analysis.plot(kind='scatter', x='avg_partners', \n",
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" y='avg_ghg_efficiency', ax=ax4)\n",
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" ax4.set_title('Partnership Size vs Efficiency')\n",
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" ax4.set_ylabel('GHG Efficiency')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()\n",
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"\n",
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"# Close connection\n",
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"conn.close()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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