Manhattan Temperature Analysis

This map shows live temperatures and elderly population concentration for each census tract in Manhattan. Risk analysis uses standard deviation (σ) from mean temperature to identify areas where people face temperature extremes.

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Temperature at Last Update

Mean temperature: --°F

Standard deviation: --°F

Range: --°F - --°F

Map Layers

Temperature Scale
Cool Average Hot
Elderly Population (65+)
Low: 0-500
Medium: 501-1,000
High: 1,001-1,500
Very High: 1,500+
Heat Standard Deviation

Shows temperature deviation above current daily mean

Extreme - ≥2σ above mean
Very High - 1.5σ to 2σ above mean
High - 1σ to 1.5σ above mean
Elevated - 0.5σ to 1σ above mean
Below threshold (< 0.5σ)
Heat Vulnerability Risk

Combined score: 60% temperature deviation + 40% elderly population density

Extreme - Risk Score ≥ 1.5
Very High - Risk Score 1.2-1.5
High - Risk Score 0.9-1.2
Elevated - Risk Score 0.5-0.9
Below threshold (< 0.5)
Cumulative Heat Risk

Historical deviation patterns since project start (based on cumulative average)

Most frequently Extreme (≥2σ)
Most frequently Very High (1.5-2σ)
Most frequently High (1-1.5σ)
Most frequently Elevated (0.5-1σ)
Cold Standard Deviation

Shows temperature deviation below current daily mean

Extreme - ≥2σ below mean
Very High - 1.5σ to 2σ below mean
High - 1σ to 1.5σ below mean
Elevated - 0.5σ to 1σ below mean
Above threshold (> -0.5σ)
Cold Vulnerability Risk

Combined score: 60% temperature deviation + 40% elderly population density

Extreme - Risk Score ≥ 1.5
Very High - Risk Score 1.2-1.5
High - Risk Score 0.9-1.2
Elevated - Risk Score 0.5-0.9
Below threshold (< 0.5)
Cumulative Cold Risk

Historical deviation patterns since project start (based on cumulative average)

Most frequently Extreme (≥2σ)
Most frequently Very High (1.5-2σ)
Most frequently High (1-1.5σ)
Most frequently Elevated (0.5-1σ)

Cumulative Data

Census Tracts: -- (310 total, 193 residential analyzed)
Collection Began: --; -- days
Last update: --/--/----, --:--:--
Next update: --:--
Cumulative Average Temperature: --°F
Temperature Std Dev: --°F
All-Time Range: --°F - --°F (--°F span)

Temperature Distribution at Last Update

Mean temperature: --°F
Standard deviation: --°F
Current range: --°F - --°F (--°F span)

Heat Risk Analysis

Current hottest: Tract -- at --°F (--°C)
Extreme (≥2σ): -- tracts (-- elderly)
Very High (1.5-2σ): -- tracts (-- elderly)
High (1-1.5σ): -- tracts (-- elderly)
Elevated (0.5-1σ): -- tracts (-- elderly)

All-time hottest: Tract -- at --°F (--°C)
Extreme (≥2σ): -- tracts (-- elderly)
Very High (1.5-2σ): -- tracts (-- elderly)
High (1-1.5σ): -- tracts (-- elderly)
Elevated (0.5-1σ): -- tracts (-- elderly)

Cold Risk Analysis

Current coldest: Tract -- at --°F (--°C)
Extreme (≥2σ): -- tracts (-- elderly)
Very High (1.5-2σ): -- tracts (-- elderly)
High (1-1.5σ): -- tracts (-- elderly)
Elevated (0.5-1σ): -- tracts (-- elderly)

All-time coldest: Tract -- at --°F (--°C)
Extreme (≥2σ): -- tracts (-- elderly)
Very High (1.5-2σ): -- tracts (-- elderly)
High (1-1.5σ): -- tracts (-- elderly)
Elevated (0.5-1σ): -- tracts (-- elderly)

Data Collection & Statistical Analysis Methodology

1: Temperature Data Collection
  • OpenWeather API returns modeled temperature estimates for each census tract using ground-based weather stations, satellite thermal imaging, and other weather prediction models.
  • Collection Schedule: 3 times daily (6am, 2pm, 10pm EST)
2: Demographic data collection
  • Elderly population (65+) and total population data from 2020 US Census
  • 310 census tracts (filtered to 301)
3: Mapping and data visualization
  • JavaScript + Leaflet.js fetches data
  • All historical data stored in PostgreSQL
4: Statistical Analysis
  • Mean Temperature (μ): Average temperature across all census tracts at time of measurement
    Formula: μ = (Σ Ti) / n
    where Ti is the temperature of tract i, and n is the number of tracts
  • Standard Deviation (σ): Measure of temperature variability across Manhattan
    Formula: σ = √[(Σ(Ti - μ)²) / n]
  • Z-Score: Standardized temperature deviation for each tract
    Formula: Z = (Ttract - μ) / σ
    Number of standard deviations a tract's temperature is from the mean
5: Risk Scoring
  • Risk analysis combines temperature deviation with elderly population density to identify areas where statistical temperature extremes overlap with vulnerable populations.
    • Temperature Component (60% weight):
      Tempcomponent = |Z-Score| × 0.6
      where Z-Score is calculated as above
    • Normalized Elderly Density (40% weight):
      First, calculate elderly density per tract:
      Densityi = Elderlyi / max(Elderlyall tracts)
      Then apply weight:
      Elderlycomponent = Densityi × 0.4
    • Combined Vulnerability Score:
      Vscore = (|Z-Score| × 0.6) + (Normalized Elderly Density × 0.4)
      Example calculation for a tract:
      • Z-Score = 2.1 (very hot)
      • Elderly population = 2,500
      • Max elderly in any tract = 3,340
      • Normalized density = 2,500 / 3,340 = 0.748
      • Vscore = (2.1 × 0.6) + (0.748 × 0.4) = 1.26 + 0.299 = 1.559
  • Vulnerability Risk Thresholds:
    • Extreme Vulnerability: Vscore ≥ 1.5
    • Very High Vulnerability: 1.2 ≤ Vscore < 1.5
    • High Vulnerability: 0.9 ≤ Vscore < 1.2
    • Elevated Vulnerability: 0.6 ≤ Vscore < 0.9

Sources