MAP PORTFOLIO:
COURSE - Cartography and Map Design
01/2016-03/2016
Soccer is a world widely famous and exciting sport. The Union of European Football Associations Champions League (UCL) is famous in soccer, since it concentrates the highest level of soccer teams in Europe. The purpose is to visualize soccer teams’ performance during seasons and qualifiers for the UCL.
Map 1 was aimed to show the study area of this map portfolio.
Map 2 showed information of Premier League teams using Season 2014/2015 as an example. If a team qualified for the Union of European Football Associations Champions League (UCL), it would be represented by a red point; otherwise it would be a blue point.
Map 3 was aimed to show average points of regions in the United Kingdom, which was calculated from Premier League Teams performance during seasons. Based on the rule of “win gets three points; draw gets one point; loss gets zero point”, calculated average annual points for each team for five seasons (2010/2011-2014/2015). Then determined average points for each region in the United Kingdom.
For Map 4, there were thirty teams in Premier League during season 2010/2011-2014/2015. Based on the rule of “win gets three points; draw gets one point; loss gets zero point”, calculated average annual points for each team for five seasons. These values were then averaged for each region in the United Kingdom.
Map 1 was aimed to show the study area of this map portfolio.
Map 2 showed information of Premier League teams using Season 2014/2015 as an example. If a team qualified for the Union of European Football Associations Champions League (UCL), it would be represented by a red point; otherwise it would be a blue point.
Map 3 was aimed to show average points of regions in the United Kingdom, which was calculated from Premier League Teams performance during seasons. Based on the rule of “win gets three points; draw gets one point; loss gets zero point”, calculated average annual points for each team for five seasons (2010/2011-2014/2015). Then determined average points for each region in the United Kingdom.
For Map 4, there were thirty teams in Premier League during season 2010/2011-2014/2015. Based on the rule of “win gets three points; draw gets one point; loss gets zero point”, calculated average annual points for each team for five seasons. These values were then averaged for each region in the United Kingdom.
Web Map Application (WMA) of Clark University Campus Map, Clark University
COURSE - Web Mapping and Open Source GIS
09/2015-12/2015
Used Google Maps API to develop a WMA by HTML & CSS & JavaScript. The WMA allows user to 1) display on-campus points of interest and off-campus facilities, such as academic building, escort service area and nearby food places; 2) search route between on-campus buildings, or from off-campus location to a certain building; 3) click the polygon or type the building name in the search box to query detailed information of on-campus buildings.
The project members include Zhuoyue Zhou, Xiaoyan Hu and myself.
The project members include Zhuoyue Zhou, Xiaoyan Hu and myself.
Implementation of Local Moran’s Correlation Coefficient Tool for ArcGIS Users
COURSE - Computer programming for gis
02/2015-04/2015
The capabilities for visualization and identification of local patterns of spatial data are an important concern for spatial analysis but are limited. GeoDa software and Xiang YE, UB PhD Candidate and AAG prize winner, are currently two of few that enable a Local Moran's Correlation Coefficient (LMCC) analysis, yet there is still no option in ESRI's ArcGIS.
Therefore, we wrote a script calculating LMCC, developed scripts for map and scatter plot outputs, customized LMCC tool in ArcToolbox. The result was a ArcTool that users can use it in ArcMap.
Users can import a polygon shapefile they want to analyze, and select two attribute fields as x and y variables, select spatial weight matrix type and output path. Then users can get three output mxd files for LMCC, Z-score and Significance, and a pdf document including three maps and a scatter plot graph.
The equation we used: LMCC(X, yj0) = z(yj0) ∑ wij0 z(xi), (Ye, 2014)
The project members include Cathleen Torres Parisian, Mengyi Chen and myself.
Therefore, we wrote a script calculating LMCC, developed scripts for map and scatter plot outputs, customized LMCC tool in ArcToolbox. The result was a ArcTool that users can use it in ArcMap.
Users can import a polygon shapefile they want to analyze, and select two attribute fields as x and y variables, select spatial weight matrix type and output path. Then users can get three output mxd files for LMCC, Z-score and Significance, and a pdf document including three maps and a scatter plot graph.
The equation we used: LMCC(X, yj0) = z(yj0) ∑ wij0 z(xi), (Ye, 2014)
The project members include Cathleen Torres Parisian, Mengyi Chen and myself.
Exploring the Spatial Relationship between Coal Industry Activity and Lung Cancer
Course - Advanced Vector GIS
02/2015-04/2015
This project explores whether there is a correlation between lung cancer rates and coal industry activity at a county level in the Appalachian region. This project also determines the extent to which socioeconomic data such as smoking and poverty rates have explanatory power for lung cancer rates. By using Exploratory Regression, Ordinary Least Squares, and Geographically Weighted Regression, this analysis shows that coal fired power plant capacity and coal mine production have a correlation with lung cancer incidence. These analyses also show that socioeconomic factors have higher explanatory power over lung cancer rates than coal industry activities.
The project members include Ariel Walcutt and myself.
The project members include Ariel Walcutt and myself.
Assessing the Current Trends in Sea Level Rise due to Global Warming and how the Effects may Potentially Modify the Urban Landscape of North Carolina by the Year 2100.
COURSE - ADVANCED Raster GIS
02/2015-04/2015
It has been predicted that sea levels are projected to increase between 0.09m and 0.88m by the year 2100 (Poulter 2008). In some areas of the USA, this phenomenon of sea level rise is more apparent than in others and as such we examined the eastern seaboard, specifically North Carolina to see the potential impacts of sea level rise here. This was achieved through an assessment of the trends of the rise in ocean levels and attempt to predict what the levels will be in the future up to the year 2100. The results of these predictions were then applied to the land cover of the state and how vulnerable certain locations will be to these changes.
The project members include Garfield Barclay, Zehan Li and myself.
The project members include Garfield Barclay, Zehan Li and myself.