Idea #1 Justin Wong

ideas/Idea #1.md

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+# Idea 
+Justin Wong
+Data 400
+1/30/2025
+
+### Research Question:
+How do major sporting events impact crime rates in surrounding areas after games?
+
+### Data Collection:
+I plan to collect crime data by scraping public crime mapping platforms to obtain timestamps, locations, and types of crimes reported. To gather game data, including results and locations, I can scrape sports team websites, ticketing platforms like Ticketmaster, and use ESPN as a source of information.
+### Scope:
+I plan to focus specifically on the New York area due to my personal interest in NY basketball, particularly the passionate fanbase of the New York Knicks. Tracking crimes based on geographical proximity to the arena may provide insight into whether winning or losing has an effect on crime in the area.
+
+### Data preprocessing:
+For crime data collection, I can either web scrape public crime mapping platforms or use pre-existing datasets. The NYPD provides crime mapping data for NYC, which I have already identified as a potential source (https://compstat.nypdonline.org/). Game day data is publicly available online and can also be found in Kaggle datasets, so scraping may not be necessary. However, game attendance and factors like playoff games may require scraping from team websites or ticketing platforms.
+##### Potential Additions:
+Spatial Analysis (Geospatial Crime Mapping & Heatmaps)
+Method: Use GIS (Geographic Information Systems) or kernel density estimation (KDE) to visualize crime hotspots before and after games.
+
+Helps determine whether crime shifts spatially based on game events.
+
+
+### Correct Specification of the Model:
+For my proposed model, I plan to use regression analysis to determine whether my dependent variable—crimes/incidents in the post-game period—is affected by independent variables such as game outcomes, attendance levels, and game type. I will analyze both historical and current data to assess whether team performance influences crime rates.
+
+### Implications for Stakeholders:
+Regarding the legality of scraping data, it is possible and legal to scrape the various websites I have listed. Additionally, I will ensure that no personal names or identifying information are included in my analysis, maintaining confidentiality. The majority of the data I will be using is publicly available, and this project is for research purposes, not for profit.
+
+### Ethical, Legal, and Societal Implications:
+In terms of legality of scaping for the proposed data, it is possible and legal to scrape the varous websites I have listed. I am also refraining from using names in my analysis, so people information will remain confidential. Most of the data being obtained is public information. As well as I am not selling this project for profit. 
+### Conclusion
+-Summarize findings and their relevance to stakeholders.
+
+-Propose actionable recommendations for public safety and event management.
+
+-Discuss potential extensions, such as analyzing additional cities or types of events.
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ideas/Idea 2.md

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+# IDEA 2
+## Research Question  
+How do major geopolitical events, elections, or sporting events (e.g., Super Bowl, Olympics) impact stock market volatility and investor sentiment?  
+
+## Data Collection  
+
+### Stock Market Data  
+- **Stock Prices & Indices:** S&P 500, Dow Jones, NASDAQ, and sector-specific indices (Yahoo Finance). 
+- **Volatility Measures:** VIX (CBOE Volatility Index) as a measure of market uncertainty.  
+- **Historical Stock Returns:** Event-related stock price fluctuations.  
+
+### Event Data  
+- **Political Events:** U.S. presidential elections, Brexit, major policy announcements, wars, trade agreements.  
+- **Sporting Events:** Super Bowl, FIFA World Cup, Olympics, UEFA Champions League final.  
+- **Unexpected Crises:** COVID-19, financial crashes, or natural disasters.  
+
+### Sentiment Data  
+- **News & Media Sentiment:** Scrape financial news (CNBC, Bloomberg, WSJ) and analyze headlines using sentiment analysis (Natural Language Processing).  
+- **Social Media Sentiment:** Scrape Twitter, Reddit, and investor forums (e.g., r/wallstreetbets) to gauge real-time sentiment.  
+
+## Data Preprocessing  
+- Align event dates with stock price movements before, during, and after events.  
+- Convert textual sentiment (news, social media) into numerical scores.  
+- Normalize stock price data to remove seasonal trends and unrelated market movements.  
+
+## Methodology  
+
+### 1. Event Study Analysis  
+- Measure **Abnormal Returns (AR)** and **Cumulative Abnormal Returns (CAR)** around event dates.  
+- Compare stock returns during event windows (pre-event, event-day, post-event) to expected returns.  
+
+### 2. Volatility Analysis  
+- Measure volatility spikes.  
+- Compare volatility for event periods vs. non-event periods.  
+
+### 3. Sentiment Analysis  
+- Use NLP techniques (e.g., VADER or BERT models) to score news headlines and social media sentiment.  
+- Correlate sentiment shifts with stock price movements.  
+
+
+## Implications for Stakeholders  
+- **For Investors:** Helps investors hedge risks associated with major global events.  
+- **For Policymakers:** Provides insights into how economic policies influence markets.  
+- **For Financial Analysts:** Guides trading strategies based on historical event-driven market behavior.