NY Shootings and Disadvantaged Areas
2025-02-17
Neighborhood Disadvantage and Gun Violence in NYC: A 2023 Analysis
Introduction
This study examines the relationship between neighborhood disadvantage scores and gun violence in New York City in 2023. With ongoing discussions about public safety and crime rates, this research provides a data-driven approach to understanding the link between socioeconomic conditions and shootings at the neighborhood level. Using shooting data from the New York Police Department (NYPD) and disadvantaged neighborhood classifications from the New York State Energy Research and Development Authority (NYSERDA), this study seeks to determine whether neighborhoods with higher disadvantage scores experience more gun violence. The findings reveal a strong correlation between these two factors, indicating that disadvantaged communities face disproportionately higher rates of shootings per 100,000 people.
Data Sources
This analysis relies on two primary datasets:
NYPD Shooting Incident Data (2023): • Obtained from the New York Police Department (NYPD), this dataset provides detailed information on all recorded shootings in NYC from 2006 to 2023. • Includes variables such as incident date/time, location (latitude/longitude), victim demographics (age, race, sex), and whether the shooting was fatal or non-fatal. • This data set enables the calculation of shooting rates per 100,000 residents at the neighborhood level by matching incidents to NYC census tracts.
New York State Disadvantaged Communities Data (2023): Defines disadvantaged neighborhoods based on environmental burdens, climate risks, and socioeconomic vulnerabilities. • Published by the New York State Energy Research and Development Authority (NYSERDA), this dataset identifies disadvantaged communities based on a combination of socioeconomic, environmental, and health-related factors. • Neighborhoods are assigned a Disadvantaged Score ranging from 0 (least disadvantaged) to 1 (most disadvantaged), determined by indicators such as poverty rates, unemployment, median income, racial composition, housing burden, and environmental risks. • This dataset allows for a quantitative measure of neighborhood-level disadvantage, which is used to assess its relationship with gun violence.
By integrating these two datasets, this study evaluates whether higher levels of neighborhood disadvantage correlate with increased rates of gun violence.
Methodology
- Geo-Spatial Analysis: Mapping Shootings to Census Tracts • Since both data sets use different geographic units (point-level data for shootings vs. census tract-level data for disadvantage), shootings were spatially matched to NYC census tracts using GIS tools and R’s sf package. • The total number of shootings within each census tract was calculated, allowing for neighborhood-level comparison.
2023 Most Disadvantaged Neighborhoods in NYC with Demographics
Shootings in NYC Since 2006 with Statistical Markers
## [1] "INCIDENT_KEY" "OCCUR_DATE"
## [3] "OCCUR_TIME" "BORO"
## [5] "LOC_OF_OCCUR_DESC" "PRECINCT"
## [7] "JURISDICTION_CODE" "LOC_CLASSFCTN_DESC"
## [9] "LOCATION_DESC" "STATISTICAL_MURDER_FLAG"
## [11] "PERP_AGE_GROUP" "PERP_SEX"
## [13] "PERP_RACE" "VIC_AGE_GROUP"
## [15] "VIC_SEX" "VIC_RACE"
## [17] "X_COORD_CD" "Y_COORD_CD"
## [19] "Latitude" "Longitude"
## [21] "Lon_Lat"Heatmap of Shootings in NYC since 2006
2023 NYC Shootings with Demographics and Statistical Markers
## [1] "INCIDENT_KEY" "OCCUR_DATE"
## [3] "OCCUR_TIME" "BORO"
## [5] "LOC_OF_OCCUR_DESC" "PRECINCT"
## [7] "JURISDICTION_CODE" "LOC_CLASSFCTN_DESC"
## [9] "LOCATION_DESC" "STATISTICAL_MURDER_FLAG"
## [11] "PERP_AGE_GROUP" "PERP_SEX"
## [13] "PERP_RACE" "VIC_AGE_GROUP"
## [15] "VIC_SEX" "VIC_RACE"
## [17] "X_COORD_CD" "Y_COORD_CD"
## [19] "Latitude" "Longitude"
## [21] "Lon_Lat"2023 NYC Shootings and Disadvantaged Neighborhoods Map
Top 15 Most Disadvantaged Census Tracts in NYC (2023) with Shooting Counts
## package 'kableExtra' successfully unpacked and MD5 sums checked
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## The downloaded binary packages are in
## C:\Users\mikem\AppData\Local\Temp\RtmpGq9Tka\downloaded_packages| GEOID | County | Percentile_Rank_Combined_NYC | Shooting_Count |
|---|---|---|---|
| 36005009300 | Bronx | 1.000000 | 2 |
| 36005011700 | Bronx | 0.999522 | 0 |
| 36005001900 | Bronx | 0.999045 | 0 |
| 36005011502 | Bronx | 0.998090 | 3 |
| 36005005100 | Bronx | 0.997135 | 3 |
| 36005009000 | Bronx | 0.996657 | 2 |
| 36005005300 | Bronx | 0.995702 | 1 |
| 36005006300 | Bronx | 0.994747 | 2 |
| 36005002702 | Bronx | 0.993314 | 1 |
| 36005009600 | Bronx | 0.990926 | 0 |
| 36005018900 | Bronx | 0.989971 | 1 |
| 36005003300 | Bronx | 0.989494 | 3 |
| 36005019300 | Bronx | 0.989016 | 4 |
| 36005020100 | Bronx | 0.987584 | 2 |
| 36005005200 | Bronx | 0.987106 | 0 |
NYC Census Tracts in NYC during 2023 that had atleast 1 intcident of gun violence
| GEOID | County | Shooting_Count | Disadvantaged_Score |
|---|---|---|---|
| 36005036901 | Bronx | 16 | 0.932665 |
| 36005041400 | Bronx | 10 | 0.683859 |
| 36047110400 | Kings | 9 | 0.769341 |
| 36005017702 | Bronx | 8 | 0.924546 |
| 36005038100 | Bronx | 8 | 0.849570 |
| 36081035200 | Queens | 8 | 0.460363 |
| 36005002000 | Bronx | 7 | 0.824737 |
| 36005022901 | Bronx | 7 | 0.927889 |
| 36005037900 | Bronx | 7 | 0.954632 |
| 36005040100 | Bronx | 7 | 0.856734 |
| 36047028300 | Kings | 7 | 0.929322 |
| 36061019400 | New York | 7 | 0.983286 |
| 36081008700 | Queens | 7 | 0.977555 |
| 36081012601 | Queens | 7 | 0.458930 |
| 36005012901 | Bronx | 6 | 0.902101 |
| 36005023704 | Bronx | 6 | 0.875836 |
| 36005039500 | Bronx | 6 | 0.961318 |
| 36005039902 | Bronx | 6 | 0.927412 |
| 36047032500 | Kings | 6 | 0.410697 |
| 36047051602 | Kings | 6 | 0.585482 |
| 36047115600 | Kings | 6 | 0.837631 |
| 36081014202 | Queens | 6 | 0.794174 |
| 36081018200 | Queens | 6 | 0.792264 |
| 36005006000 | Bronx | 5 | 0.979943 |
| 36005007900 | Bronx | 5 | 0.952245 |
| 36005014500 | Bronx | 5 | 0.886342 |
| 36005017300 | Bronx | 5 | 0.950334 |
| 36005017701 | Bronx | 5 | 0.905444 |
| 36005023302 | Bronx | 5 | 0.891595 |
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| 36005039100 | Bronx | 5 | 0.893505 |
| 36047002901 | Kings | 5 | 0.965138 |
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| 36047035900 | Kings | 5 | 0.839542 |
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| 36047025500 | Kings | 1 | 0.946036 |
| 36047025902 | Kings | 1 | 0.784623 |
| 36047026700 | Kings | 1 | 0.567813 |
| 36047027300 | Kings | 1 | 0.670487 |
| 36047027500 | Kings | 1 | 0.518625 |
| 36047029500 | Kings | 1 | 0.571633 |
| 36047030100 | Kings | 1 | 0.903534 |
| 36047030300 | Kings | 1 | 0.855301 |
| 36047031100 | Kings | 1 | 0.777459 |
| 36047031500 | Kings | 1 | 0.627507 |
| 36047032100 | Kings | 1 | 0.497612 |
| 36047034100 | Kings | 1 | 0.563037 |
| 36047034300 | Kings | 1 | 0.731137 |
| 36047034500 | Kings | 1 | 0.644222 |
| 36047034700 | Kings | 1 | 0.710124 |
| 36047035100 | Kings | 1 | 0.762655 |
| 36047035300 | Kings | 1 | 0.659503 |
| 36047035500 | Kings | 1 | 0.624164 |
| 36047036100 | Kings | 1 | 0.883477 |
| 36047036501 | Kings | 1 | 0.660936 |
| 36047037000 | Kings | 1 | 0.482808 |
| 36047037100 | Kings | 1 | 0.693887 |
| 36047038500 | Kings | 1 | 0.550621 |
| 36047038700 | Kings | 1 | 0.781757 |
| 36047038900 | Kings | 1 | 0.761223 |
| 36047039300 | Kings | 1 | 0.808023 |
| 36047039900 | Kings | 1 | 0.788443 |
| 36047040000 | Kings | 1 | 0.413563 |
| 36047040100 | Kings | 1 | 0.798949 |
| 36047041700 | Kings | 1 | 0.913563 |
| 36047042400 | Kings | 1 | 0.357689 |
| 36047042900 | Kings | 1 | 0.690067 |
| 36047048600 | Kings | 1 | 0.690544 |
| 36047048900 | Kings | 1 | 0.918816 |
| 36047049100 | Kings | 1 | 0.720153 |
| 36047049500 | Kings | 1 | 0.452722 |
| 36047051002 | Kings | 1 | 0.506686 |
| 36047051200 | Kings | 1 | 0.371060 |
| 36047052300 | Kings | 1 | 0.861509 |
| 36047052700 | Kings | 1 | 0.911175 |
| 36047054500 | Kings | 1 | 0.831901 |
| 36047054600 | Kings | 1 | 0.411652 |
| 36047057200 | Kings | 1 | 0.709647 |
| 36047057400 | Kings | 1 | 0.140879 |
| 36047059600 | Kings | 1 | 0.463228 |
| 36047060000 | Kings | 1 | 0.367717 |
| 36047069601 | Kings | 1 | 0.296562 |
| 36047069602 | Kings | 1 | 0.244986 |
| 36047073600 | Kings | 1 | 0.398758 |
| 36047074000 | Kings | 1 | 0.160936 |
| 36047074400 | Kings | 1 | 0.073066 |
| 36047078600 | Kings | 1 | 0.527698 |
| 36047079802 | Kings | 1 | 0.337154 |
| 36047080800 | Kings | 1 | 0.683381 |
| 36047081800 | Kings | 1 | 0.320439 |
| 36047083000 | Kings | 1 | 0.389685 |
| 36047083600 | Kings | 1 | 0.491882 |
| 36047084600 | Kings | 1 | 0.504776 |
| 36047085600 | Kings | 1 | 0.446991 |
| 36047086200 | Kings | 1 | 0.581662 |
| 36047086600 | Kings | 1 | 0.613658 |
| 36047087000 | Kings | 1 | 0.583572 |
| 36047089000 | Kings | 1 | 0.712989 |
| 36047090200 | Kings | 1 | 0.876313 |
| 36047093200 | Kings | 1 | 0.407832 |
| 36047095400 | Kings | 1 | 0.415473 |
| 36047096400 | Kings | 1 | 0.400191 |
| 36047098200 | Kings | 1 | 0.649952 |
| 36047098600 | Kings | 1 | 0.432665 |
| 36047100600 | Kings | 1 | 0.435053 |
| 36047101000 | Kings | 1 | 0.442216 |
| 36047102800 | Kings | 1 | 0.297517 |
| 36047103400 | Kings | 1 | 0.733524 |
| 36047107000 | Kings | 1 | 0.746418 |
| 36047107800 | Kings | 1 | 0.623209 |
| 36047109800 | Kings | 1 | 0.749284 |
| 36047112200 | Kings | 1 | 0.782713 |
| 36047112400 | Kings | 1 | 0.553486 |
| 36047116000 | Kings | 1 | 0.689589 |
| 36047116600 | Kings | 1 | 0.692932 |
| 36047116800 | Kings | 1 | 0.825692 |
| 36047117201 | Kings | 1 | 0.739255 |
| 36047117601 | Kings | 1 | 0.726839 |
| 36047117800 | Kings | 1 | 0.899236 |
| 36047118202 | Kings | 1 | 0.645177 |
| 36047118400 | Kings | 1 | 0.797039 |
| 36047120800 | Kings | 1 | 0.725883 |
| 36061000800 | New York | 1 | 0.910697 |
| 36061001402 | New York | 1 | 0.877268 |
| 36061001600 | New York | 1 | 0.614613 |
| 36061002201 | New York | 1 | 0.850048 |
| 36061002700 | New York | 1 | 0.544890 |
| 36061003100 | New York | 1 | 0.085482 |
| 36061006200 | New York | 1 | 0.616523 |
| 36061009100 | New York | 1 | 0.133238 |
| 36061009500 | New York | 1 | 0.109838 |
| 36061009700 | New York | 1 | 0.563515 |
| 36061010300 | New York | 1 | 0.227316 |
| 36061011000 | New York | 1 | 0.127507 |
| 36061011300 | New York | 1 | 0.000000 |
| 36061011600 | New York | 1 | 0.074976 |
| 36061012700 | New York | 1 | 0.543935 |
| 36061012900 | New York | 1 | 0.732569 |
| 36061013400 | New York | 1 | 0.051576 |
| 36061013500 | New York | 1 | 0.881566 |
| 36061013800 | New York | 1 | 0.067813 |
| 36061014700 | New York | 1 | 0.055396 |
| 36061015100 | New York | 1 | 0.715377 |
| 36061015200 | New York | 1 | 0.681948 |
| 36061015300 | New York | 1 | 0.033906 |
| 36061016600 | New York | 1 | 0.917383 |
| 36061016800 | New York | 1 | 0.842407 |
| 36061017000 | New York | 1 | 0.981853 |
| 36061017402 | New York | 1 | 0.709169 |
| 36061017500 | New York | 1 | 0.209169 |
| 36061018000 | New York | 1 | 0.992837 |
| 36061019701 | New York | 1 | 0.209647 |
| 36061020901 | New York | 1 | 0.813754 |
| 36061021100 | New York | 1 | 0.758357 |
| 36061021303 | New York | 1 | 0.869628 |
| 36061021600 | New York | 1 | 0.610315 |
| 36061022800 | New York | 1 | 0.720630 |
| 36061023300 | New York | 1 | 0.896848 |
| 36061023501 | New York | 1 | 0.688634 |
| 36061023502 | New York | 1 | 0.858644 |
| 36061023700 | New York | 1 | 0.929799 |
| 36061024000 | New York | 1 | 0.000000 |
| 36061025100 | New York | 1 | 0.882044 |
| 36061026300 | New York | 1 | 0.765998 |
| 36061027900 | New York | 1 | 0.701528 |
| 36061028700 | New York | 1 | 0.801815 |
| 36061029900 | New York | 1 | 0.982330 |
| 36061030300 | New York | 1 | 0.409265 |
| 36081000100 | Queens | 1 | 0.516714 |
| 36081001800 | Queens | 1 | 0.587870 |
| 36081002500 | Queens | 1 | 0.981375 |
| 36081004200 | Queens | 1 | 0.603629 |
| 36081004300 | Queens | 1 | 0.973257 |
| 36081004700 | Queens | 1 | 0.796084 |
| 36081005300 | Queens | 1 | 0.551576 |
| 36081006501 | Queens | 1 | 0.514327 |
| 36081010100 | Queens | 1 | 0.454632 |
| 36081012100 | Queens | 1 | 0.499522 |
| 36081014100 | Queens | 1 | 0.865330 |
| 36081014201 | Queens | 1 | 0.613181 |
| 36081014300 | Queens | 1 | 0.688157 |
| 36081015600 | Queens | 1 | 0.564470 |
| 36081015801 | Queens | 1 | 0.593601 |
| 36081016800 | Queens | 1 | 0.556829 |
| 36081017200 | Queens | 1 | 0.455587 |
| 36081018102 | Queens | 1 | 0.360076 |
| 36081018502 | Queens | 1 | 0.372015 |
| 36081019000 | Queens | 1 | 0.412607 |
| 36081019200 | Queens | 1 | 0.603152 |
| 36081020400 | Queens | 1 | 0.744986 |
| 36081020800 | Queens | 1 | 0.802292 |
| 36081021200 | Queens | 1 | 0.811843 |
| 36081026300 | Queens | 1 | 0.553964 |
| 36081026902 | Queens | 1 | 0.555396 |
| 36081027000 | Queens | 1 | 0.472779 |
| 36081027300 | Queens | 1 | 0.647087 |
| 36081027700 | Queens | 1 | 0.486628 |
| 36081028200 | Queens | 1 | 0.385864 |
| 36081036300 | Queens | 1 | 0.858166 |
| 36081037900 | Queens | 1 | 0.646609 |
| 36081038302 | Queens | 1 | 0.000000 |
| 36081042600 | Queens | 1 | 0.000000 |
| 36081044301 | Queens | 1 | 0.862464 |
| 36081044602 | Queens | 1 | 0.726361 |
| 36081046500 | Queens | 1 | 0.588825 |
| 36081046800 | Queens | 1 | 0.488061 |
| 36081047000 | Queens | 1 | 0.620821 |
| 36081049600 | Queens | 1 | 0.419771 |
| 36081050000 | Queens | 1 | 0.494269 |
| 36081050800 | Queens | 1 | 0.351958 |
| 36081051500 | Queens | 1 | 0.231614 |
| 36081052400 | Queens | 1 | 0.228749 |
| 36081053100 | Queens | 1 | 0.670010 |
| 36081054000 | Queens | 1 | 0.517192 |
| 36081056500 | Queens | 1 | 0.457975 |
| 36081058000 | Queens | 1 | 0.300382 |
| 36081059100 | Queens | 1 | 0.316619 |
| 36081062900 | Queens | 1 | 0.486151 |
| 36081064600 | Queens | 1 | 0.216332 |
| 36081068000 | Queens | 1 | 0.205349 |
| 36081079200 | Queens | 1 | 0.421203 |
| 36081079702 | Queens | 1 | 0.329513 |
| 36081080302 | Queens | 1 | 0.288443 |
| 36081080900 | Queens | 1 | 0.369150 |
| 36081084900 | Queens | 1 | 0.878223 |
| 36081090700 | Queens | 1 | 0.953677 |
| 36081094500 | Queens | 1 | 0.736867 |
| 36081095400 | Queens | 1 | 0.718720 |
| 36081097202 | Queens | 1 | 0.657116 |
| 36081099200 | Queens | 1 | 0.672397 |
| 36081099801 | Queens | 1 | 0.326648 |
| 36081100802 | Queens | 1 | 0.404489 |
| 36081101002 | Queens | 1 | 0.527221 |
| 36081103201 | Queens | 1 | 0.750239 |
| 36081122702 | Queens | 1 | 0.829990 |
| 36081137700 | Queens | 1 | 0.268386 |
| 36085000600 | Richmond | 1 | 0.694842 |
| 36085000700 | Richmond | 1 | 0.905922 |
| 36085002100 | Richmond | 1 | 0.868195 |
| 36085002700 | Richmond | 1 | 0.955110 |
| 36085006400 | Richmond | 1 | 0.574021 |
| 36085007000 | Richmond | 1 | 0.380611 |
| 36085013301 | Richmond | 1 | 0.930755 |
| 36085014700 | Richmond | 1 | 0.064947 |
| 36085017011 | Richmond | 1 | 0.032951 |
| 36085019800 | Richmond | 1 | 0.071156 |
| 36085030301 | Richmond | 1 | 0.665234 |
Census Tracts with 0 Shootings in NYC during 2023
| GEOID | County | Percentile_Rank_Combined_NYC |
|---|---|---|
| 36081005000 | Queens | 0 |
| 36081009900 | Queens | 0 |
| 36005024900 | Bronx | 0 |
| 36005027600 | Bronx | 0 |
| 36005028400 | Bronx | 0 |
| 36005031900 | Bronx | 0 |
| 36005033400 | Bronx | 0 |
| 36081024600 | Queens | 0 |
| 36081065501 | Queens | 0 |
| 36081121100 | Queens | 0 |
| 36081042600 | Queens | 0 |
| 36081091602 | Queens | 0 |
| 36081091800 | Queens | 0 |
| 36081060701 | Queens | 0 |
| 36081062400 | Queens | 0 |
=============================================== # Understanding
Percentile_Rank_Combined_NYC
What Does This Score Represent?
The Percentile_Rank_Combined_NYC variable represents the
relative disadvantaged ranking of a census tract within New York
City (NYC).
Range: 0 to 1 (or 0% to 100%) Higher values (closer to 1.00) indicate that the neighborhood is more disadvantaged relative to other NYC census tracts. Lower values (closer to 0.00) indicate that the neighborhood is less disadvantaged…
How Is It Calculated?
NYC assigns this percentile rank based on a composite index that combines two major factors:
- Environmental Burden Score:
- Measures pollution exposure (e.g., air quality, hazardous waste sites, toxic releases).
- Includes climate risks (e.g., flood vulnerability, extreme heat).
- Population Vulnerability Score:
- Includes socioeconomic factors (e.g., poverty rate, education levels, housing burden).
- Considers health risks (e.g., asthma rates, access to healthcare).
Each census tract is ranked based on these combined scores, and the percentile rank is assigned accordingly.
Interpretation of the Score
The table below explains how to interpret the percentile rank:
Percentile_Rank_Combined_NYC |
Interpretation |
|---|---|
| 1.00 (100%) | Most Disadvantaged Neighborhood in NYC |
| 0.90 (90%) | Among Top 10% Most Disadvantaged Areas |
| 0.50 (50%) | Median Level of Disadvantagedness |
| 0.10 (10%) | Among Least Disadvantaged Areas |
| 0.00 (0%) | Least Disadvantaged Neighborhood in NYC |
For example: - A neighborhood with a percentile rank of 0.98 means that it is more disadvantaged than 98% of all census tracts in NYC. - A neighborhood with a percentile rank of 0.25 means that it is less disadvantaged than 75% of census tracts.
Why is This Score Important?
- Helps identify neighborhoods that need the most resources (e.g., funding, clean energy initiatives, public health interventions).
- Used to prioritize disadvantaged communities for government programs and policy planning.
- Supports environmental justice efforts by addressing communities with high pollution and socioeconomic struggles.
Example Interpretation
Consider two neighborhoods: A Bronx census tract
with: - GEOID: 36005009300 -
Percentile_Rank_Combined_NYC: 1.00 (100%)
👉 This means it is the most disadvantaged neighborhood in all
of NYC.
A Manhattan census tract with: - GEOID:
36061000100 - Percentile_Rank_Combined_NYC: 0.15
(15%)
👉 This means it is among the least disadvantaged neighborhoods
in NYC.
Key Takeaways
✔ The higher the percentile rank, the more
disadvantaged the neighborhood.
✔ The score is relative to NYC, so it compares
neighborhoods only within the city.
✔ Used by policymakers and researchers to
target resources where they are needed most.
Scatterplot of Disadvantaged Score vs. Shootings per 100,000
# Scatterplot Explination Each blue dot represents a geographic area
(GEOID). The X-axis (Disadvantaged Score) → How disadvantaged an area is
(higher = more disadvantaged). The Y-axis (Shootings per 100,000 people)
→ The number of shootings in that area, adjusted for population size.
The Red Line (Trend Line) → Shows the overall pattern
Scatterplot Analysis
As the Disadvantaged Score increases, the number of shootings per 100,000 people also increases. This positive correlation suggests that more disadvantaged areas tend to have higher rates of gun violence.
Scatterplot Conclusion
Key Takeaway The scatter plot supports the idea that neighborhoods with higher social disadvantage experience more shootings per 100,000 people. While there is some variation, the upward trend (red line) indicates that disadvantage is linked to higher gun violence.
Regression Analysis Table
| Variable | Estimate | Std. Error | T-Statistic | P-Value |
|---|---|---|---|---|
| (Intercept) | -2.301339 | 1.042038 | -2.208498 | 0.0273608 |
| Disadvantaged_Score | 30.426136 | 1.756877 | 17.318305 | 0.0000000 |
==========================================
Interpretation of Regression Results
==========================================
Relationship Between Disadvantaged Score & Shootings per 100k
Since Disadvantaged Score is a percentage (0 to 1), we interpret the coefficient in percentage point changes. Each 1% increase in Disadvantaged Score (0.01 increase) is associated with an increase of ~0.30 shootings per 100,000 people. A 10 percentage point increase (e.g., from 20% to 30%) is linked to ~3 additional shootings per 100,000 people. A 50 percentage point increase (e.g., from 20% to 70%) is linked to ~15 additional shootings per 100,000 people.
- Conclusion: There is a strong positive correlation between neighborhood disadvantage and gun violence.
Statistical Significance (T-Statistic & P-Value)
The T-Statistic (17.32) is very high, indicating that the effect of Disadvantaged Score on Shootings per 100k is strong and unlikely to be random. The P-Value (0.000) confirms that this relationship is highly statistically significant—meaning the chance that this effect is due to randomness is virtually zero.
- Conclusion: The data strongly supports the idea that higher disadvantage leads to higher rates of shootings per 100,000 people.
Interpreting the Intercept (-2.30)
The intercept represents the expected number of shootings per 100,000 when Disadvantaged Score = 0 (i.e., in the least disadvantaged areas). Since the intercept is negative (-2.30 shootings per 100k), it is not meaningful in a real-world context—shootings cannot be negative. This happens because the regression line is trying to fit the data, but very few areas have a Disadvantaged Score of exactly 0, making the intercept less relevant.
- Conclusion: We should focus on the slope (30.43 per full unit or ~0.30 per 1%), which provides meaningful insights into how shootings change with increasing disadvantage.