bamboo-forest-rain Goto Github PK

30-days-of-javascript

30 days of JavaScript programming challenge is a step-by-step guide to learn JavaScript programming language in 30 days. This challenge may take more than 100 days, please just follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

assignment02

bamboo-forest-rain

Config files for my GitHub profile.

bikeshare-boston

"Re-balancing", in terms of a bike share system, refers to the process of reallocating available bikes at a given time according to the demand. Failure to meet the demand is undesirable for both the users and the bike share companies -- the former loss a mean of transportation and the later loss potential revenue. Therefore it is important to have a sense of how many people will pickup a bike at a certain time and station -- in order to redistribute excessive bikes at other locations to stations that actually have high demand. Usually re-balancing will take place manually by small trucks to move the bikes around. The analysis below will examine the bike demand in Boston, MA in August and September 2019. "Bluebikes" is a private company that operates the bike share system in the city. It employs 4-5 rebalancing vans, each with a payload of 20-25 bikes, to redistribute bicycles 24 hours a day, 7 days a week. A regression model is developed based on weather and one-hour to one-week time lag to predict the number of trips that occur at a particular time and station. Since the company is redistributing bikes every hour, one-hour time lag is appropriate, because that will allow the van-driver to know an hour beforehand on the demand of bikes at different locations. Other time lags are added to strengthen the model.

bluebike-orange-line

chicago-tod

This brief compares the data from 2009 and 2019. The year 2009 is chosen, because some data is only available starting from 2009. A period of ten years provides a long enough time frame to see changes. Since the most recent data has been updated in 2019, the data set also reflects a more accurate situation close to the year we are in now (2021).

class-dossier

cpln505pbn-land-use-chester

debunch-septa-app

dispatch-amublances-in-santa-monica

Santa Monica has one of the country’s most inefficient emergent medical service (EMS) system. The national average time for an ambulance to respond a emergency call is 7 minutes. Even in a rural setting, the average response time is 14 minutes. Santa Monica, however, based on the city data from March to November 2021, takes averagely 30 minutes from receiving the call to arrive the destination, much longer than that of the national average. We located the problem to be its ambulance dispatch system. The traditional way to respond an EMS call is to dispatch an ambulance after the station receives a call. The problem is when the nearest station runs out of ambulances, the station needs to request an ambulance from another station further away and adds on to the wait time. Thus, we aim to develop the app beepo to make ambulance dispatch more efficient in Santa Monica. Our purpose is to manage ambulance vehicles in all four stations citywide, predict when and where the demand of emergency calls rises, and dispatch ambulances to the nearest station when the demand rises.

effect-of-lumen-on-pedestrian-crashes

fbi

A client for the FBI's Crime Data Explorer API - contains UCR and NIBRS data

geo-data

This repository contains geographic data created by Azavea

housing-repair-tax-credit-analysis

This analysis is dedicated to Housing and Community Development (HCD) to maximize the the number of eligible homeowners to take repair tax credit with limited allocation resources.

javascript-week3

modeling-pedestrian-safety

musa-508-final-project

Let's suffer (on fair)

musa-fall-week-2

Data Visualization Fundamentals

musa-page

musa508_wk3_2021

Code from MUSA 508 Week 3 Lab

musa508_wk4_2021

Lab Markdown for Introduction to Spatial Machine Learning - MUSA 508

musa_508_lab

Tutorials and Materials for MUSA 508 - Public Policy Analytics - University of Pennsylvania Weitzman School of Design

musa_508_lab2_sf

PENN MUSA 508 Lab2 - {sf} + Rmd + Github

musa_508_wk8

People Based ML

predict-housing-price-boulder-co

Zillow's housing market predictions are an integral part of its business model, helping the company achieve a greater understanding of how the market will value properties. Our team is confident that through our geospatial machine learning-based model that considers not only attributes of homes, but local factors as well, we can improve Zillow's house price predictions for the Boulder County study area and provide a template which can be adapted to other localities.

predicting-stalking-risk

This report evaluates the effectiveness of geo-spacial processes in controlling selection bias (i.e. reducing errors) when predicting stalking risk in Chicago, Illinois.

public-policy-analytics-landing

All the data for the Public Policy Analytics book by Ken Steif can be downloaded from this repo

school-explorer

septa-debunched-app

week-1

Exploratory Data Science in Python