辅导SSCI 599课程、写作R语言程序、R编程设计调试

” 辅导SSCI 599课程、写作R语言程序、R编程设计调试SSCI 599 Spatial Topic: Spatial Econometrics Project 2USC Spatial Sciences Institute 2020 1SSCI 599 Project 2 Explanatory Spatial Data Analysis MultipleLinear RegressionPrepared by An-Min Wu, PhD, Lecturer of Spatial Sciences, University of Southern CaliforniaDue Date: Monday, October 26, 11:59 pm Pacific TimeSubmit Project 2 as a Word document into the corresponding assignment link on BlackboardValue 7% of the course gradePenalty for late delivery: 2 points deduction up to 4 days late; no points will be given over 4 dayslate.The purpose of this project is for you to apply the concepts and skills learned in the classes tothe datasets that you are interested in exploring in spatial economics. As you have done somepreliminary research work in Project 1, I hope the data comes handy for you to dive into theanalysis in this project.In this project, you will import the data of your own interest in spatial economics into R andconduct explanatory data analysis, exploratory spatial data analysis (including kernel densityestimation, spatial weights and global spatial autocorrelation) and multiple linear regression.Before going into spatial data analysis, read through the entire document first. Next, go throughthe hands-on R practices that we did in Week 6-7 if you have not done so, so you are familiarwith the libraries, functions and their arguments required in R to complete this project.Learning Objectives To identify available spatial datasets for investigating the spatial economic topic area ofyour interest To use kernel density estimation, global Morans I, and Moran scatterplot To conduct multiple linear regression including the pre- and after-assessments of thedataset for its fit-for-use in regression analysis To interpret the outputs of kernel density estimation, spatial autocorrelation and multiplelinear regressionAssignment DescriptionThis project looks to further your topic of interest into some practical exercises in spatial andstatistical analysis in R. To complete that, follow the instructions below:1. From your chosen spatial economic research topic and variables in Project 1, identify spatialdatasets of the variable(s) for investigation in spatial analysis and import the data into R.Focus on the main variable that you are interested in learning to start with. Consider thespatial extent and unit of analysis so the data size is not too large to manage (e.g. the numberof units is greater than 50 units and not more than 500 units). Sometimes your spatialSSCI 599 Spatial Topic: Spatial Econometrics Project 2USC Spatial Sciences Institute 2020 2location data (e.g. county boundaries) and attributes (e.g. employment rates) might need tocome from different sources and join together before using. Do the pre-processing in Excelor ArcGIS as needed (We will cover how to do that in R soon).For importing shapefiles, use readOGR( ) in the rgdal package. Use ??readOGR to open thehelp file in RStudio. If your data is not projected, you will have to retrieve the geographiccoordinates from polygons then use the spTransform method in the rgdal library.If your non-spatial attribute table contains latitude and longitude, you can use read.csv( ) orread.table( ) to import the non-spatial data first, then make your data spatial by creating aSpatial* object (see the Week 4 handout for how to promote the data spatial).For any question about import here or the remaining of the project, I would suggest you tosearch for online resources (e.g. httpss://rdocumentation.org) and post your question/issueson the Discussion Forum on Blackboard.2. Explore the imported data distribution first by conducting explanatory data analysis (EDA)in R. For running any statistical or spatial analysis, you should always examine your data first.Run descriptive statistics (the R function should be the one that shows at least: sample size,minimum, mean, median, maximum, standard deviation) and make a scatterplot, ahistogram, and a boxplot for your main variable(s) doing all EDA here for one variable issufficient, but more is fine (e.g. running EDA for both variables that you want to know theassociation between the two). Consider transformation if the data shows non-normaldistribution and show its normality after transformation.3. Explore the imported spatial data by conducting explanatory spatial data analysis (ESDA) ofyour main interested variable(s). including kernel density, building spatial weights matrixfollowed by global Morans I and Moran Scatterplot.4. Execute standard linear regression to investigate the association of the variables in the topicof interest using lm( ) function. The number of independent variable can vary but make surethat your final model contains only the explanatory (a.k.a independent or predictor) variablesthat have their partial coefficients statistical significant.5. Write a report that include the following items: Introduction: A brief description of your interested spatial economic topic, the variablesyou selected (including unit of analysis and spatial extent), and the sources where youfind the data (include the organization that you obtained the data and their URL ifavailable). EDA: R code, their resulting table/plots, and a short paragraph describing othedistribution (i.e. central tendency and dispersion) of the data and if you performedtransformation or not. ESDA: R code, their resulting display, and 1-2 descriptive paragraphs that interpret theresults. Here your results should consist of the KDE map, neighbor list object detail,visualization of your spatial weights objects, Morans I results, and Moran scatterplot.Whether you run Morans I using Monte Carlo approach is your choice. Describe whateach of these analysis results tells you about your data.SSCI 599 Spatial Topic: Spatial Econometrics Project 2USC Spatial Sciences Institute 2020 3 Standard linear regression: R code, their results, and a paragraph that interpret theresults. Reflection: A short paragraph (less than 200 words) reflect about the experience youhad when working on this project. What do you find easy? What do you findchallenging? What questions do you still have after you complete the project? Anyadjustment you might consider, either on data or operation, to improve your experience?DeliverablesSubmit a project report with the components requested above in a Word document. Include acover page that contains at least the information about the class number (SSCI 599), semester(Fall 2020), project number/title and your name. Save your Project 2 report document asProject2_[YourLastName].docx and submit it via the appropriate assignment link in Blackboard.Additional Resources I: Data HubsIf you have a hard time to find the appropriate datasets, you may consider to use the followingsources and adopt the datasets mentioned here to use in your project.1. City of Los Angeles GeoHub: httpss://geohub.lacity.org. Datasets you may consider touse include, but not limited to, Los Angeles index of displacement pressure, trafficcollision or traffic accidents data.2. COVID-19 GIS Hub: httpss://coronavirus-resources.esri.com. If you are interested inunderstanding COVID-19 impact of our social and economic aspects of life, you mightfind this data hub useful. Additionally, as I want you to make a story map for the finalpresentation that combines the analysis and information for all of your projects thissemester, you might also check out how Esri utilizes its ArcGIS Story Map to tell thestory of its work in COVID-19 ( httpss://esri.com/about/newsroom/blog/gis-toachieve-equitable-speedy-vaccine-distribution)3. The U.S. Censuss American Community Survey 5-year Data: httpss://census.gov/data/developers/data-sets/acs-5year.html. The Census Bureau notonly offers spatial data (TIGER/Line data), but also include various socio-economic anddemographic factors that are surveyed every year in various census administrative levelsyou can download for use.4. IPUMS: httpss://ipums.org. As a part of the Institute for Social Research and DataInnovation at the University of Minnesota, IPUMS provides census and survey datafrom the U.S. and around the world. IPUMS integrates the census type data to make iteasy to study and research. For your information, you may also want to check theABOUT tab if you look for the data analysis type of employment in the near future.Additional Resources II: Creating neighbor object list for a point dataAssume that we want to explore a dataset that contains three columns including latitude, longitudeand the average math score of schools in one district. We can import this data (.csv), transform itto a spatial object (sp), and assign its datum WGS84:SSCI 599 Spatial Topic: Spatial Econometrics Project 2USC Spatial Sciences Institute 2020 4To create an object that describes the neighbor relationship from the point data, here we use thek nearest neighbor (knn) method:The resulting neighbor object is a knn class of object. Now we can convert the knn object into amore generic class of neighbors nb:Now you can convert the nb object to the listw object using nb2listw.如有需要，请加QQ：99515681 或邮箱：99515681@qq.com

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