Modeling and Analytics
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Settlement Pattern of Hispanic Immigrants In Piedmont Region, North Carolina, Using ArcGIS Pro
Settlement Pattern of Hispanic Immigrants In Piedmont Region, North Carolina, Using ArcGIS Pro
Fig.1. Census data depict where Hispanics settle
Problem
Problem
Immigration has always been a hot topic in United States. The Census Bureau Decennial census captures the settlement pattern of immigrants. The census data is in csv format and the census tracts are in TIGER shape files. Mapping out the Hispanics population allows federal agencies to put resources in the right places.
Results
Results
The resulting map documents were published as pdf. It was found that Hispanics settled in Chatnam, Durham, Lee, Randolph, Montgomery, Mecklenburg, Wake, and Union counties of North Carolina.
Fig.2. The Getis_Ord Gi test shows the clustering effect
Analysis
Analysis
The census data was converted to Excel and loaded into ArcGIS Pro. The TIGER file was projected to NAD83 NC State Plane FIPS 3200 feet. Data type for the fields to be joined must be the same. Field GEOid2 was converted to text, and joined to the county ID of the TIGER file. The data was displayed by symbolizing field D002 of census attribute table (Fig.1). Hot Spot Analysis tool was ran at 6, 14, 20 neighbor counts to get bands at 28000, 44000 and 57000 feet, with best results for 57000 feet (Fig.2).
Reflection
Reflection
The decennial census data is different from the American Community Survey(ACS). The census is the actual population, the ACS is only a sample of the population. The Hispanics data is contained in the census, the ACS covers employment, education and such. Such data is used when we want to find impact on certain population. This project can be further developed by mapping the settlement pattern for 2020 and compare the patterns for 2010 and 2020.
Predicting Location Of Wildfire Ignition Points in Sierra Nevada Region Using Landsat 8 data and ArcGIS Pro
Predicting Location Of Wildfire Ignition Points in Sierra Nevada Region Using Landsat 8 data and ArcGIS Pro
Fig.1. Prediction Map for locating areas of high possibility for fire ignition.
Problem
Problem
No state has ever had more land damaged by wildfires than the state of California, and with the most human-caused wildfires as reported by the National Interagency Fire Center (Sep 2, 2021). There is a lack of research that takes into account both climatic and non-climatic factors. Two things are needed to start a fire: an ignition and fuel to burn. Either component missing and a fire will not happen. Warmer climate causes the biomass to be drier and become fuel for fire (Parisien & Moritz, 2009). This study will use remote sensing vegetation index and ignition factors to predict fire ignition points in northern California.
Results
Results
The prediction map generated showed the orange areas as high possibility for fire ignition and the green areas as low possibility for fire ignition (Fig.1). The actual fire ignition points recorded by the US Forest Services were then overlaid to verify that the prediction map is accurate (Fig.2). It can be observed that the fire ignition points did cluster around the orange areas, the high possibility areas for fire ignition. Thus, confirming the validity of the analysis.
Fig.2. Actual fire ignition points were overlaid and the fire points clustered around the orange areas (high possibility of fire ignition) confirming the analysis used is sound.
Analysis
Analysis
Normalized Difference Moisture Index (NDMI) can be used for the monitoring of biomass that was used by fires as fuel, also known as Live Fuel Moisture. Raster function can be applied to Landsat data using NDMI equation to obtain a moisture raster on the biomass. Unsupervised classification was used to classify NDMI. Other statistically significant factors Wildland Urban Interface, proximity to roads and trails (Syphard et al., 2007) were included in the analysis. Site suitability approach was applied to create a prediction map.
Reflection
Reflection
Landsat 8 derived vegetation index such as the NDMI is demonstrated, together with WUI, and proximity to roads and trails, accurately locate the areas at high risk for fire ignition. Hopefully, this study can be used to predict future high risk areas for fire prevention planning. The variables in this study uses a resolution of 30m. A further study should be done with Sentinel data products to enhance the resolution.
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