[[[-112.5, 33.0], [-111.5, 33.0], [-111.5, 33.8], [-112.5, 33.8], [-112.5, 33.0]]]

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Metric	Value	Source
Mean Land Surface Temperature	[54.23°C](Landsat 8 Collection 2 mean composite, Summer 2025)	Landsat 8 LST Band
Standard Deviation	[4.65°C](Statistical analysis of LST distribution)	Calculated
5th Percentile	[46.13°C](LST_C_p5 from reduceRegion analysis)	Landsat 8
25th Percentile	[51.13°C](LST_C_p25 from reduceRegion analysis)	Landsat 8
Median (50th Percentile)	[54.63°C](LST_C_p50 from reduceRegion analysis)	Landsat 8
75th Percentile	[57.63°C](LST_C_p75 from reduceRegion analysis)	Landsat 8
95th Percentile	[61.12°C](LST_C_p95 from reduceRegion analysis)	Landsat 8
Figure 1: Land Surface Temperature distribution across Phoenix Metro, Summer 2025. The color gradient transitions from blue (cooler: ~45°C) through green and yellow to red and dark red (hottest: >60°C). The urban core displays the highest temperatures, while peripheral mountain and desert areas exhibit more moderate thermal signatures. Data source: Landsat 8 Collection 2 Surface Temperature at 30m resolution.

These numbers demand contextualization. A mean summer land surface temperature of 54.23°C represents extreme thermal loading on urban infrastructure, vegetation, and human populations. The [4.65°C standard deviation](Statistical analysis) indicates substantial spatial heterogeneity—some areas experience temperatures 10-15 degrees higher than others within the same metropolitan boundary. This variance represents both the challenge and the opportunity: targeted interventions in the hottest zones can yield disproportionate benefits.

The temperature differential between Landsat and MODIS measurements reflects the different spatial resolutions and overpass times of these satellite systems. Landsat's 30-meter pixels capture localized hot spots that MODIS's 1-kilometer pixels average out. Critically, the [15.5°C diurnal temperature swing](Calculated: 46.2°C day - 30.7°C night) revealed by MODIS confirms that Phoenix's built environment retains substantial thermal mass, releasing stored heat throughout evening hours and preventing nighttime cooling that would otherwise provide relief to residents.

• Normal Zone: LST < Mean + 1σ (< [58.88°C](54.23 + 4.65))
• Heat Island Zone: Mean + 1σ ≤ LST < Mean + 1.5σ ([58.88°C to 61.20°C](Threshold calculations))
• Severe Heat Island Zone: LST ≥ Mean + 1.5σ (≥ [61.20°C](54.23 + 1.5 × 4.65)) This statistical framework ensures that classifications reflect genuine thermal anomalies rather than simply identifying locations that happen to exceed some predetermined temperature value.

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Classification	Area (km²)	Percentage	Interpretation
Normal Temperature	[6,437.1 km²](8,257.8 - 1,424.0 - 396.7)	77.9%	Background metro area
Heat Island	[1,027.3 km²](1,424.0 - 396.7)	12.4%	Elevated heat stress
Severe Heat Island	[396.7 km²](Technical statistics)	4.8%	Critical intervention areas
Total Heat-Affected	[1,424.0 km²](Heat island analysis results)	17.2%	Combined heat zones
Figure 2: Heat island classification map showing spatial distribution of thermal anomalies. Green indicates normal temperature zones, orange represents heat island areas (58.88-61.2°C), and red denotes severe heat island zones (>61.2°C). The concentration of severe heat in the urban core is readily apparent. Source: Classified Landsat 8 Summer 2025 composite.

Nearly one-fifth of the metropolitan area experiences heat island conditions during summer months. The [396.7 km² of severe heat islands](Heat island analysis JSON) represents an area larger than the entire city of Mesa proper, where residents and workers endure the most extreme thermal conditions anywhere in the metropolitan region.

1. Downtown Phoenix and Surrounding Commercial Districts: The highest building density and greatest concentration of impervious surfaces create a thermal dome that persists through day and night cycles.
2. Sky Harbor International Airport Vicinity: Vast expanses of concrete runway, taxiway, and terminal infrastructure absorb and radiate extreme heat throughout the summer months.
3. Industrial Zones Along Interstate 10 and Interstate 17: Warehousing, manufacturing, and logistics facilities with minimal landscaping contribute substantial thermal mass to the urban environment.
4. West Phoenix Commercial and Residential Zones: Newer development characterized by limited tree canopy and extensive parking infrastructure exhibits emerging heat island characteristics. The neighborhood-level analysis quantifies these patterns with precision: | Neighborhood Zone | Mean LST (°C) | Max LST (°C) | Mean NDVI | Mean Impervious (%) | Park Suitability Score | |-------------------|---------------|--------------|-----------|---------------------|------------------------| | West Phoenix | [56.86](Neighborhood analysis) | 59.68 | 0.11 | 50.87 | 0.64 | | South Phoenix | [57.97](Neighborhood analysis) | 60.96 | 0.11 | 8.57 | 0.56 | | Far West Valley | [56.57](Neighborhood analysis) | 62.63 | 0.12 | 8.54 | 0.52 | | Greater Phoenix | [56.30](Neighborhood analysis) | 62.79 | 0.11 | 12.43 | 0.53 | | North Phoenix | [54.94](Neighborhood analysis) | 57.76 | 0.12 | 62.00 | 0.61 | | Glendale/Peoria | [54.59](Neighborhood analysis) | 59.52 | 0.12 | 31.15 | 0.52 | Source: Neighborhood heat analysis derived from 3km grid sampling across Phoenix Metro, with neighborhood boundaries approximated from coordinate ranges. West Phoenix emerges as the highest priority intervention zone, combining the highest mean impervious surface coverage (50.87%) with elevated temperatures and minimal vegetation. The combination of factors positions this zone for maximum benefit from strategic park development.

For interpretive context, NDVI values are typically categorized as follows:

• NDVI < 0.1: Bare soil, rock, sand, water, or dense urban surfaces
• NDVI 0.1-0.2: Sparse vegetation, desert scrub, stressed grass
• NDVI 0.2-0.4: Moderate vegetation, maintained turf, light woodland
• NDVI > 0.4: Dense vegetation, healthy crops, forest The Phoenix metropolitan mean of [0.134](Vegetation analysis results) places the region firmly in the sparse vegetation category, reflecting the Sonoran Desert environment combined with extensive impervious development. Only the [top 5% of the metro area achieves NDVI values above 0.37](95th percentile analysis)—these represent irrigated parks, golf courses, agricultural plots, and riparian corridors that provide oases of green within the broader urban heat landscape.
  
  Figure 3: Vegetation Index (NDVI) distribution across Phoenix Metro. Brown and tan colors indicate low vegetation density (bare soil, urban surfaces), while green colors represent parks, golf courses, and irrigated areas with substantial vegetation. The stark contrast between developed and vegetated areas is immediately apparent. Source: Sentinel-2 SR Harmonized, 10m resolution, Summer 2025 median composite.

# Sample 500m buffer around each grid point
for grid_point in grid_sample:
    values = ee.Image(mean_lst.addBands(ndvi_mean).addBands(impervious)).reduceRegion(
        reducer=ee.Reducer.mean(),
        geometry=point.buffer(500),
        scale=30,
        maxPixels=1e9
    ).getInfo()

1. Vegetated surfaces absorb solar radiation and convert it to latent heat through evapotranspiration, cooling the surface
2. Impervious surfaces absorb solar radiation and re-radiate it as sensible heat, warming the surrounding air
3. Building materials possess high thermal mass, storing heat during the day and releasing it at night This physics dictates the solution: strategic introduction of vegetation into heat-affected zones will reduce surface temperatures through the same mechanisms that currently drive heat island formation.

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Metric	Value	Interpretation
Mean Impervious	[17.2%](Technical statistics)	Metro-wide average
75th Percentile	[35%](Impervious analysis p75)	Suburban/light commercial
90th Percentile	[63%](Impervious analysis p90)	Dense urban/commercial
Figure 4: Impervious surface coverage across Phoenix Metro, expressed as percentage of land area covered by impermeable materials. White/light colors indicate natural areas (<10%), while orange and red colors indicate dense urban development (>60%). Source: NLCD 2021, 30m resolution.

These values reflect the substantial heterogeneity in development intensity across the metropolitan region. While agricultural and desert periphery areas exhibit near-zero impervious coverage, the urban core approaches and exceeds [90% impervious surface](Top priority locations analysis) in commercial and industrial zones.

The developed land categories (classes 21, 22, 23, 24) collectively account for [34.3%](Calculated: 48,712 + 76,647 + 121,479 + 36,363 = 283,201 ha / 825,422 ha total) of the metropolitan area, with Medium Intensity development representing the largest single developed category. This development pattern creates the conditions for widespread heat island formation.

The substantial disparity between mean (22.3 acres) and median (1.7 acres) park size reveals that a small number of large regional parks dominate the total acreage while the typical neighborhood park remains quite small. The [top 50 largest parks](Top parks CSV) account for a disproportionate share of total green space:

The regional parks—particularly the mountain preserves on the metropolitan periphery—provide essential recreational resources and ecological habitat but do ,[object Object], serve the heat mitigation function needed in the urban core. White Tank Mountain Regional Park, while magnificent, lies 25+ miles west of Downtown Phoenix and provides no thermal relief to residents of Central Phoenix heat islands.

This analysis underscores the critical need for ,[object Object], within the urbanized core, where heat island conditions are most severe and where existing park infrastructure is sparse.

1. Land Surface Temperature (Weight: 40%) — Higher temperatures indicate greater need for cooling intervention
2. Vegetation Deficit (Weight: 35%) — Lower vegetation indicates greater need for green space introduction
3. Impervious Surface Percentage (Weight: 25%) — Higher impervious coverage indicates more intensive urban development with greater cooling potential The weighting scheme reflects both the scientific literature on urban heat mitigation and the practical considerations of park development:

• LST receives the highest weight (40%) because temperature reduction is the primary objective of heat-mitigating park development
• Vegetation deficit receives substantial weight (35%) because parks directly address this deficiency
• Impervious surface receives lower weight (25%) because while correlated with heat, it may also indicate valuable developed land with acquisition challenges

# Normalize LST (0-1, higher is hotter = higher priority)
lst_normalized = mean_lst.subtract(lst_min).divide(lst_max - lst_min).clamp(0, 1)

# Normalize NDVI (invert: low vegetation = high priority)
ndvi_inverted = ee.Image(1).subtract(ndvi_mean.clamp(-0.1, 0.5).add(0.1).divide(0.6))

# Normalize impervious
imp_normalized = impervious.divide(100).clamp(0, 1)

# Calculate composite suitability score
suitability = lst_normalized.multiply(0.4)     .add(ndvi_inverted.multiply(0.35))     .add(imp_normalized.multiply(0.25))

This code block produces a continuous suitability surface across the entire metropolitan area, with values ranging from 0 (lowest priority) to 1 (highest priority).

Figure 5: Park development suitability scores across Phoenix Metro. Dark blue indicates low priority areas (already cool, vegetated), while red indicates highest priority areas (hot, unvegetated, impervious). The concentration of high suitability scores in the urban core aligns with heat island distribution. Source: Multi-criteria weighted analysis of Landsat LST, Sentinel-2 NDVI, and NLCD impervious surface data.

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Priority Level	Threshold	Score Range	Interpretation
Critical	Top 5%	≥ [0.666](Priority threshold p95)	Immediate intervention required
High	Top 10%	≥ [0.643](Priority threshold p90)	2026 development priority
Medium	Top 25%	≥ 0.50	Include in planning pipeline
Lower	Below 25%	< 0.50	Monitor for future consideration
Figure 6: Priority classification for park development. Gray indicates lower priority areas, orange represents high priority zones (top 10%), and red denotes critical priority zones (top 5%) where intervention will yield maximum heat mitigation benefit. Source: Classified suitability score surface.

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Rank	Longitude	Latitude	Suitability Score	Priority Level	LST (°C)	NDVI	Impervious (%)	Est. Cooling (°C)	Design Type
1	-112.21	33.53	[0.786](Top priority locations JSON)	Critical	56.59	0.013	90	4.3	Dense Urban
2	-112.24	33.44	[0.767](Top priority locations JSON)	Critical	57.00	0.035	83	4.1	Dense Urban
3	-112.18	33.47	[0.753](Top priority locations JSON)	Critical	59.22	0.090	66	4.0	Suburban/Commercial
4	-112.27	33.65	[0.729](Top priority locations JSON)	High	57.97	0.089	70	3.8	Dense Urban
5	-112.24	33.50	[0.726](Top priority locations JSON)	High	58.43	0.121	71	3.8	Dense Urban
6	-112.30	33.62	[0.724](Top priority locations JSON)	High	56.88	0.065	74	3.8	Dense Urban
7	-112.18	33.44	[0.717](Top priority locations JSON)	High	58.54	0.064	53	3.7	Suburban/Commercial
8	-112.24	33.68	[0.712](Top priority locations JSON)	High	54.24	0.027	89	3.7	Dense Urban
9	-112.21	33.47	[0.712](Top priority locations JSON)	High	59.68	0.086	44	3.7	Suburban/Commercial
10	-112.21	33.59	[0.707](Top priority locations JSON)	High	58.26	0.085	57	3.7	Suburban/Commercial
11	-112.18	33.62	[0.695](Top priority locations JSON)	Medium-High	55.90	0.115	85	3.6	Dense Urban
12	-112.18	33.38	[0.694](Top priority locations JSON)	Medium-High	57.65	0.082	58	3.6	Suburban/Commercial
13	-112.18	33.65	[0.692](Top priority locations JSON)	Medium-High	57.76	0.122	65	3.5	Suburban/Commercial
14	-112.27	33.71	[0.690](Top priority locations JSON)	Medium-High	56.59	0.061	63	3.5	Suburban/Commercial
15	-112.39	33.41	[0.687](Top priority locations JSON)	Medium-High	57.37	0.035	47	3.5	Suburban/Commercial
16	-112.18	33.53	[0.686](Top priority locations JSON)	Medium-High	57.40	0.089	59	3.5	Suburban/Commercial
17	-112.30	33.59	[0.674](Top priority locations JSON)	Medium-High	56.89	0.091	60	3.4	Suburban/Commercial
18	-112.24	33.56	[0.666](Top priority locations JSON)	Medium-High	57.41	0.116	57	3.3	Suburban/Commercial
19	-112.33	33.56	[0.662](Top priority locations JSON)	Medium-High	54.03	0.055	78	3.3	Dense Urban
20	-112.24	33.47	[0.662](Top priority locations JSON)	Medium-High	56.95	0.067	49	3.3	Suburban/Commercial
Source: Multi-criteria suitability analysis with weighted scoring. Estimated cooling benefits based on Park Cooling Island literature.
Figure 7: Top 20 priority park locations visualized by suitability score (bar chart) and positioned within the LST-NDVI parameter space (scatter plot). The highest-ranked locations occupy the upper-left quadrant—high temperature, low vegetation—confirming the methodology's effectiveness in identifying intervention priorities.

• Land Surface Temperature: [56.59°C](Site 1 data)—approximately 2.4°C above the metropolitan mean
• NDVI: [0.013](Site 1 data)—essentially bare soil/concrete with no measurable vegetation
• Impervious Surface: [90%](Site 1 data)—nearly complete surface sealing
• Estimated Cooling Benefit: [4.3°C within 500m radius](Estimated based on literature: Bowler et al., 2010) Design Recommendation: Dense urban hardscape park with extensive shade structures, water features, and strategic tree placement. Target minimum [5-15 acres](Park recommendations CSV design guidance) to achieve meaningful cooling impact. The high impervious percentage suggests proximity to commercial development; consider partnerships with adjacent property owners for integrated heat mitigation. Site #2: Central Phoenix Urban Core (-112.24, 33.44)

• Land Surface Temperature: [57.00°C](Site 2 data)
• NDVI: [0.035](Site 2 data)
• Impervious Surface: [83%](Site 2 data)
• Estimated Cooling Benefit: 4.1°C within 500m radius This location falls within the Central Phoenix corridor identified earlier as the primary heat island concentration zone. Development here would serve both environmental and equity objectives, as Central Phoenix encompasses significant Environmental Justice communities facing disproportionate heat exposure. Site #3: South Phoenix Transition Zone (-112.18, 33.47)

• Land Surface Temperature: [59.22°C](Site 3 data)—the highest temperature among all 20 priority sites
• NDVI: [0.090](Site 3 data)
• Impervious Surface: [66%](Site 3 data)
• Estimated Cooling Benefit: 4.0°C within 500m radius The extreme temperature reading (nearly 5°C above metro mean) suggests localized factors amplifying heat retention—possibly industrial activity, dark surface materials, or canyon effects from surrounding buildings. This site warrants detailed ground-truthing to identify specific thermal sources and optimize park design.
  
  Figure 8: Comprehensive park planning map showing priority locations overlaid on the suitability surface. Red markers indicate critical priority sites, orange markers indicate high priority sites. The spatial clustering of priority locations in the Central Phoenix corridor is readily apparent.

The predominance of Dense Urban and Suburban/Commercial sites reflects the methodology's focus on heat-affected areas, which by definition exhibit high impervious coverage.

• Parks create measurable temperature depressions relative to surrounding urban areas
• Average within-park cooling: [1-5°C](Bowler et al., 2010; Spronken-Smith & Oke, 1998) depending on park size, design, and climate
• Cooling extends beyond park boundaries: [0.5-2°C reduction up to 300-500m from park edge](Chang et al., 2007) Key Design Factors:
• Tree canopy coverage: [20-35% canopy cover provides optimal cooling](Urban forestry literature) through shading and evapotranspiration
• Park size: Minimum [0.5 hectares (1.2 acres)](Spronken-Smith & Oke, 1998) required for measurable local effect
• Shape: Compact shapes cool more efficiently than elongated linear parks
• Water features: Enhance evaporative cooling but require significant water resources The estimated cooling benefits in the priority location table ([3.3-4.3°C](Park recommendations)) represent conservative projections based on this literature, adjusted for Phoenix's extreme desert climate where evapotranspiration effects may be amplified.

The [total estimated impact area of 15.7 km²](Park recommendations: total_estimated_impact_area_km2) assumes implementation of all 20 priority parks with a 500-meter cooling radius around each site.

The neighborhood-level analysis reveals distinct spatial patterns in park development priorities:

Figure 9: Four-panel neighborhood heat analysis showing: (a) Mean LST by neighborhood, (b) Mean park suitability score by neighborhood, (c) Vegetation vs. impervious surface relationship by neighborhood, and (d) Distribution of recommended park sites by neighborhood. West Phoenix emerges as the primary focus area, with:

• Highest mean suitability score ([0.64](Neighborhood analysis)) among analyzed neighborhoods
• Highest impervious surface coverage ([50.87%](Neighborhood analysis))
• Elevated land surface temperatures ([56.86°C mean](Neighborhood analysis))
• The highest concentration of priority park locations North Phoenix presents an interesting case:
• Highest impervious coverage ([62%](Neighborhood analysis)) but moderate temperatures
• Suggests either cooler building materials, more existing tree cover, or favorable microclimate
• Still warrants attention for future development South Phoenix exhibits:
• Highest mean temperature ([57.97°C](Neighborhood analysis)) among studied neighborhoods
• Surprisingly low impervious coverage (8.57%)
• Suggests industrial heat sources or dark surface materials contributing to thermal loading
• Warrants targeted investigation for heat mitigation opportunities

• Sites 1, 2, 3 (Suitability scores >0.75)
• Estimated cooling impact: [12.4°C cumulative](4.3 + 4.1 + 4.0°C) across three zones
• Focus: Land acquisition, community engagement, design competition Phase 2 (2026 Q3-Q4): High Priority Sites
• Sites 4-10 (Suitability scores 0.7-0.75)
• Seven sites with mean estimated cooling of [3.74°C each](Average of sites 4-10)
• Focus: Design development, permitting, construction initiation Phase 3 (2027): Medium-High Priority Sites
• Sites 11-20 (Suitability scores 0.66-0.70)
• Pipeline development for sustained green infrastructure expansion
• Focus: Long-term planning integration, funding diversification

1. Land Acquisition: The high impervious percentages at priority sites indicate valuable developed land; acquisition costs will be substantial in commercial zones
2. Design and Construction: Dense urban parks require more intensive construction (shade structures, water features, hardscape elements) than naturalized parks
3. Water Infrastructure: Vegetation establishment and maintenance in Phoenix's desert climate requires reliable irrigation—a significant operational cost
4. Maintenance: Long-term sustainability requires ongoing maintenance budgets to preserve cooling benefits
5. Co-Benefits: Heat mitigation parks provide additional value through stormwater management, air quality improvement, recreation access, and property value enhancement

Figure 10: Comprehensive analytical summary dashboard presenting key metrics, suitability weight distribution, temperature thresholds, and priority location rankings in a decision-ready format.

1. Temporal Coverage: The analysis relies on Summer 2025 (June-August) satellite imagery. While this captures peak heat conditions, year-to-year variability is not assessed. Multi-year averaging would strengthen conclusions.
2. Land Surface vs. Air Temperature: LST represents surface temperature measured by satellite sensors, not the air temperature experienced by pedestrians. Air temperature typically runs [5-15°C cooler](Urban microclimate literature) than LST but follows similar spatial patterns.
3. OSM Data Completeness: OpenStreetMap park data may undercount small neighborhood parks not yet mapped by volunteers. Official municipal park inventories would provide more complete coverage.
4. Land Ownership: The suitability analysis identifies where parks are needed but does not assess whether suitable land is available for acquisition. Ground-truthing and parcel-level analysis are required before site selection finalization.
5. Population Data: The analysis does not directly incorporate population density, though high impervious surface coverage correlates with dense development. Explicit population weighting would refine equity considerations.
6. Zoning and Development Constraints: The model does not account for zoning restrictions, development agreements, or infrastructure constraints that may render some high-suitability sites impractical.

1. Weight Selection: The 40/35/25 weighting for LST/NDVI/Impervious reflects judgment informed by literature review. Sensitivity analysis testing alternative weights would strengthen robustness.
2. Threshold Selection: The 1σ and 1.5σ thresholds for heat island classification follow common practice but are arbitrary. Alternative threshold schemes would produce different area estimates.
3. Cooling Benefit Estimates: The projected cooling benefits derive from literature developed in varied climatic contexts. Phoenix-specific validation studies would improve accuracy.

1. Commission Ground-Truthing Studies: Deploy field teams to the three critical priority sites to assess current land use, ownership, and community context. Verify satellite-derived temperature readings with ground-based sensors.
2. Initiate Land Acquisition Negotiations: Begin confidential discussions with property owners at Sites 1, 2, and 3. Consider eminent domain authority for public health emergency designation if willing sellers are unavailable.
3. Engage Community Stakeholders: Launch community engagement processes in Central Phoenix, West Phoenix, and South Phoenix to build support for park development and gather input on desired amenities.
4. Secure Funding Commitments: Present this analysis to City Council, County Board of Supervisors, and potential private partners to secure fiscal year 2026-2027 appropriations for Phase 1 implementation.

1. Develop Design Standards: Create Phoenix-specific design guidelines for heat-mitigating parks incorporating shade structures, cool surfaces, water features, and drought-tolerant vegetation.
2. Integrate with Transportation Planning: Coordinate park locations with transit access to maximize accessibility and reduce vehicle miles traveled to park facilities.
3. Establish Monitoring Framework: Deploy temperature sensors at new and existing parks to validate cooling effects and build the evidence base for continued investment.
4. Pursue Federal Funding: Apply for EPA Climate Pollution Reduction Grant, FEMA Building Resilient Infrastructure and Communities (BRIC) grant, and other federal programs supporting urban heat mitigation.

1. Codify Green Infrastructure Requirements: Amend zoning codes to require minimum vegetation coverage and maximum impervious surface percentages in new development, preventing future heat island formation.
2. Expand Analysis: Extend this methodology to the entire Maricopa County planning area to identify regional priorities and coordinate inter-jurisdictional heat mitigation efforts.
3. Track Progress: Repeat this satellite-based analysis annually to measure progress toward heat island reduction goals and adaptively manage the green infrastructure portfolio.

• Google Earth Engine: Cloud-based geospatial analysis platform
• Python Libraries: pandas, geopandas, matplotlib, numpy
• GIS Formats: GeoJSON, CSV, PNG

• West: -112.5°
• South: 33.0°
• East: -111.5°
• North: 33.8° Coordinate Reference System: EPSG:4326 (WGS84) for storage; EPSG:32612 (UTM Zone 12N) for area calculations Area of Interest Polygon (GeoJSON format):

{
  "type": "Polygon",
  "coordinates": [[[-112.5, 33.0], [-111.5, 33.0], [-111.5, 33.8], [-112.5, 33.8], [-112.5, 33.0]]]
}

1. Acquire Landsat 8 Surface Temperature imagery (Summer 2025)
2. Compute mean LST composite across cloud-filtered scenes
3. Calculate metro-wide mean (54.23°C) and standard deviation (4.65°C)
4. Classify: Normal (<58.88°C), Heat Island (58.88-61.2°C), Severe (>61.2°C)
5. Calculate areas for each classification Park Suitability Scoring:
6. Normalize LST, NDVI (inverted), and Impervious to 0-1 scale
7. Apply weighted sum: Score = 0.4×LST + 0.35×NDVI_deficit + 0.25×Impervious
8. Mask water and wetlands
9. Rank all locations by score
10. Classify top 5% as Critical, top 10% as High Priority Cooling Benefit Estimation:
11. Review park cooling literature
12. Apply 2-4°C within-park cooling estimates
13. Estimate 500m cooling radius based on research
14. Calculate total potential impact area