Can you also do an extended temporal analysis on related snow-related
complaint types (e.g. heating, shelter, frozen pipes, etc.) over the same period
using both datasets, leveraging historical weather data and the Census?
Let me start by identifying all snow-adjacent complaint types in the 311 dataset. I need to find the data files and explore the complaint type taxonomy:
The qsv tool uses the host path. Let me use the simple filenames:
Excellent — I can see the snow-adjacent complaint types clearly. Let me now search for all winter-related categories and get their descriptors:
The search output went to a temp location. Let me redo it with a proper output path, but first let me be smarter — I’ll use SQL to extract exactly what I need:
The 24GB file is too large for direct SQL. Let me use the search approach but with a proper output file: Previous search is still running. Let me wait for it and then proceed:
Good — 4.38 GB of filtered rows. But I need to extract just the key columns for analysis. Let me select only the needed columns and work with the filtered data:
The file was saved to the Downloads folder, not testmcp2. Let me find it:
The file saved to Downloads (host) but isn’t visible to the VM. Let me use qsv’s host path. The working dir is /Users/joelnatividad/Downloads/testmcp2, and the file went to /Users/joelnatividad/Downloads/:
The select output also went to Downloads. Let me move it to the working directory and continue:
7M rows of snow-adjacent complaints. Let me now get the frequency breakdown and then use SQL for the temporal analysis:
Now let me categorize these into meaningful groups. I need to also look for more specific types — frozen pipes, etc. Let me also check the Descriptors:
Excellent data. Now let me also search for additional winter-specific types I may have missed — frozen pipes specifically and other cold-weather categories:
Good — Boilers (38K) and Non-Residential Heat (18.3K) are additional categories. Now let me build the comprehensive extraction. I’ll use the large filtered dataset plus these extras. Let me now do the temporal extraction using SQL on the slim file — I need year/month/day parsing:
Good, I can see the date format. Now let me use SQL to aggregate by month and complaint category. I’ll also need to pull in the Boilers/Non-Residential Heat data. Let me do the temporal aggregation:
The parquet conversion is causing issues with the date parsing. Let me force CSV mode:
DuckDB is trying to use the parquet file. The no_optimizations isn’t stopping the auto-conversion. Let me rename the file to avoid the parquet fallback and use Polars directly:
The file is on the host filesystem but qsv tools operate at host level. The issue is DuckDB’s parquet conversion failing on the date column. Let me try a different approach — use Python/pandas for the heavy analysis since the slim file is only ~390MB:
Let me find the qsv output file from the VM side and use Python for the aggregation:
The qsv temp files aren’t visible from the VM. The qsv working dir maps to /Users/joelnatividad/Downloads/testmcp2 on host. Let me use qsv to write the output directly to the testmcp2 folder which is accessible:
It saved to the temp location again, not the testmcp2 path. The output parameter seems to be getting overridden. Let me try a different approach — just use the original 24GB CSV from the VM side with Python, filtering in chunks:
Now I have clean aggregated data. Let me now pull historical weather data for the full period: I’ll pull daily temperature and snowfall data for NYC (Central Park) from 2010-2026. Let me get this in two calls to stay within API limits:
I’ll continue right where we left off — parsing the weather data and moving into the correlation analysis. Let me read the weather data files first.
The weather files are too large to read directly. Let me write a Python script to parse them and also re-fetch the weather data more efficiently with snowfall included.
Good, it’s JSON. Let me write a Python script to parse both files and also fetch snowfall data separately. Now let me also fetch snowfall data specifically, since Open-Meteo ERA5 has it:
Weather data is ready. Now let me move to the correlation analysis.
Excellent! Rich results. Now let me pull Census data for the borough-level equity analysis.
Good - I have all the FIPS codes. Now let me fetch Census demographic data for all 5 boroughs at once:
Now let me compile all the Census data and save it, then build the reports. Let me write the comprehensive analysis JSON with Census data included:
Striking equity findings — the Bronx has 816.9 heating complaints per 1,000 residents vs. Staten Island’s 72.9. Now let me build the interactive HTML report with all this data.
HTML report is done. Now let me read the skill files for DOCX and PPTX, then build both.
No genai-disclaimer skill file found. Let me also read the pptxgenjs.md for creating from scratch:
Good, I have the skill documentation. Let me now create the DOCX report using docx-js, and the PPTX using PptxGenJS. I’ll launch both in parallel.
Both packages are installed. Let me create the DOCX script:The file doesn’t exist yet, let me use Write directly:
I’ll continue right where we left off — executing the DOCX generation script, then moving on to the PPTX and verification steps.
DOCX generated. Let me validate it and then check the file size.
DOCX looks good — 58 paragraphs, 5 data tables, proper headings and content. Now let me update the todo and create the PPTX.
Now let me read the PPTX skill for the script template, then create the PPTX.
Now let me create the PPTX generation script. I’ll use a winter-themed color palette fitting the snow analysis topic.
PPTX generated (426KB). Now let me do the visual QA as required by the skill.
Good, 10 slides generated. Now let me send these to a subagent for visual QA.
Good QA feedback. Let me fix the key issues identified: slides 5, 9, 10 spacing, and slide 10 contrast.
Let me apply the fixes. The main issues are:
Slide 5: callout boxes too tightly stacked — increase spacing Slide 9: too much unused space below table — spread content Slide 10: recommendation boxes too dense + disclaimer contrast
Now re-render the fixed slides and verify:
Slides 5 and 9 pass. The disclaimer contrast concern is a minor point — it’s already set to ice blue (CADCFC) on dark navy which is actually quite readable. The haiku subagent may be over-flagging since LibreOffice rendering can slightly differ from actual PowerPoint. The disclaimer is intentionally subtle as a footer element. Let me proceed to verification.
Now let me verify data consistency across all three deliverables:
All numbers check out. Let me also verify all three output files exist and are properly sized:
All three deliverables are complete and verified. Here’s what was produced:
Interactive HTML Report (31KB) — 10-section report with Chart.js interactive charts covering correlations, lag analysis, thresholds, snow events, wind chill, borough equity, cold snaps, and annual trends.
Word Document Report (15KB) — Formal report with executive summary, methodology, 5 data tables, cold snap case studies, and policy recommendations.
PowerPoint Briefing Deck (418KB) — 10-slide deck with navy/ice color palette, embedded charts, data tables, and equity analysis. Visual QA completed with one fix-and-verify cycle.
Key findings across all three: temperature explains 61% of heating complaint variance (r = −0.78), snow/ice complaints surge 87× below freezing, plumbing complaints lag cold by 2 days, and the Bronx generates 11× more heating complaints per capita than Staten Island — driven by poverty, renter concentration, and aging housing stock.