Kwi's RTBD

1. FALL 2017 / EDUCATION AND GENTRIFICATION, CITY OF AUSTIN, AUSTIN, TX Figure 5. Various different Queen's contiguity of Local Moran’s I of dependent variable 2015 Total STAAR; 1st line is American Community Survey 2015, 5 year estimate Median Household Income; 2nd line is American Community Survey 2015, 5 year estimate Median Gross Rent; American Community Survey 2015, 5 year estimate Median Monthly Housing Cost. The hypothesis on this research is the area has higher school performances tend to have higher housing values. ●   Identified current gentrification process in the city of Austin ●   Examined correlations between education and in-migration by using ArcGIS  and GeoDa ●   Discovered the patterns both maps and graphs which interpreted accordingly

1. FALL 2018 / ASKOT LANDSCAPE, UTTARAKHAND, INDIA This project is re-establish by suing existing research " Highest elevation record of tiger presence from India ". The project is to find suitable land cover and land change for local habitat in Askot Landscape, Uttarakhand, India. I collected LandSat data and extracted satellite imagery from Google Earth Engine (used open source JavaScript). The analysis started with implementing satellite data to ERDAS Imagine and Google Earth Engine. I used model builder matrix to create classifications and published map with ArcGIS.  2. 2016 Monitoring Land and Vegetative Changes using Remote Sensing: Fukushima Daiichi Nuclear Disaster

1. FALL 2018 / AUSTIN B-CYCLE, AUSTIN, TX In 2018, I took network analysis online class with Esri and origin/destination matrix was really fascinating. I thought of using this idea from 2017 when I was  looking for an affordable and fun ways to get around downtown Austin and I was looking for bike sharing service and Austin B-Cycle caught my eye. At that time, the  company promoted, if you pay certain amount of money, you will have 24 hr access to B-Cycle anywhere in Austin but  required to check in any bike station every 30 min. I immediately pictured travel time operation between each station and started collecting road data and spatial layers. Also, I used python to reduce the .csv file volumes and developed basic structure with geoprocessing open source. The final map is extracted by using ArcGIS. The result of this analysis was interesting. The average travel time was in between 35 min to 60 min without considering any obstacles. Lat...