Final_Maria Fernandez

The current educational system in Chile consists of three types of schools: public, subsidized or private. Education is based on a “voucher” system, where parents can choose where to place their children. Private schools charge a fee which varies from school to school. Subsidized schools receive public funding in the form of a fixed amount by student, and they can charge an additional tuition fee. Public schools are free of charge and also receive money from the central government for every student that attends that school. However, they are free of charge and are run by the local government (commune or municipality).

The Chilean educational system has proven to perpetuate the existing social differences among people, instead of overcoming them. Two standardized tests are taken each year as a tool for parents and the government to evaluate the schools. Results from one of those tests (PSU, taken to enter tertiary level education) show that students from public schools score significantly lower than those from private or subsidized schools.

Table 1. Average PSU Score by Type of School

Type of School

Average Score

Public

469.4

Subsidized

500.1

Private

607.8

Source: DEMRE, 2012.

In Chile, only a small percentage of children attend private schools (7%), while the rest of the students attend subsidized and public schools in almost equal parts. Nonetheless, those who attend private schools tend to come from the highest income families, while students in public schools have the lowest incomes (Brandt, 2010). This contributes to the inequities in the system, as poor children tend to go to the schools with the lowest performance.

Even though test scores are an important source of information, there are other aspects that must be taken into account if we want to know evaluate each school´s performance. Factors such as “family background and socioeconomic conditions, differential access to facilities and inherent ability” can also account for a large part of a student´s and a school´s performance (Suryadarma, et al, 2006: 2). This is also the case of teacher quality, which can explain the school´s educational results (Brandt, 2010).

The first layout of the presentation places the area of study—Chile—in its regional context. Chile is located in South America, in the south-west corner of the subcontinent. It has a population of approximately 17.2 million and its capital is Santiago. The government is organized as a centralized republic, with Presidents elected every four years.

The second layout introduces the political divisions of the country. Chile is organized in 15 regions, each of them dividing the country from east to west. At the same time, each region is subdivided into communes. The number of communes varies in every region, but the most densely populated areas in the center of the country are the ones with the most number of communes.

The following layout helps us understand the inequities in Chile´s educational system. Public schools get the lowest scores on standardized tests, and thus the students who attend them have a lower chance of going to the best Universities. As we have already mentioned, the majority of people who attend public schools come from the lowest-income families, so the level of education they are able to obtain is the lowest the system offers.

Therefore, the research question arises. Since resources are scarce and education seems to perpetuate social inequities, are there any areas (or hotspots) where the government should focus its attention in public education? The main focus is to find certain areas where the different variables to measure education are found, in order to determine whether there is a geographical area that requires special attention from the central government.

The following map tackles the first variable: are there enough schools in the country? Is there an area where there are not enough schools for the population? From the information in the map, we can see that there appears to be no shortage of schools in the most densely populated areas. The areas with the lightest colors are the ones with the least population, and with the least schools.

The data in this map was incorporated through a Euclidean distance raster of 10 km around the most populated areas (cities). This distance was chosen as it is a reasonable stretch to travel daily for students who attend school. Next, each school was entered into the map, since the X and Y coordinates were available (a Coordinate conversion had to be performed), and a Kernel density raster was made from the schools in the map. Both rasters were reclassified and then added intro one with Map algebra, to produce the map in the layout.

The information for the location of the schools was obtained from the Ministry of Education website, in an Excel spreadsheet. The information for the cities was obtained from the Integrated System of Territorial Information (SIIT) of Library of Congress (Chile) website, as a shapefile. In this case, there was a problem with the X and Y coordinates, as ArcMap did not place them on top of the country shapefile. After checking that the coordinates were correct (mapping them on Google Map), the problem was solved when a coordinate conversion was performed. When creating the point shapefile with the locations of the schools, ArcMap assumed they came in meter notation. After converting them to the same notation as the country shapefile, the points were located in the correct place.

The second map shows an index of the Average Scores in the standardized tests previously mentioned, at the commune level. From this map we can see that there is no clear hotspot, as the worst scores are not concentrated in any particular area. The colors are dispersed in the map, showing that if we only consider scores in standardized tests (the most common way to measure school performance), there would be no particular geographical area to focus on.

The index was created by taking an average of the tests, and then joining to the commune shapefile. The scores were divided into 5 categories, with natural breaks (Jenks): high, moderately high, medium, moderately low and low. The information for this map was obtained from the National System of Municipal Information (Chile), (Sinim) and the University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE) website. The commune shapefiles were retrieved from SIIT.

However, when analyzing the quality of teachers, we can clearly identify two hotspots in the country. Since the Ministry of Education states that a Basic or Insufficient evaluation is a fail grade, in those areas we can see that there is a high and moderately high concentration of teachers who do not have the necessary skills to be teaching their students.

In order to create this map, it was necessary to come up with the ratio of teachers who received an evaluation of Basic or Insufficient. This information was available, by school, in the Ministry of Education website. Then, only the schools where over 80% of the teachers received the lowest scores were put in the map (again with X, Y coordinates and Coordinate conversion). A Kernel density raster was performed on all the points, and this was later classified with natural breaks (Jens), to obtain the areas with the highest concentration of low-skilled teachers.

The next layout shows the concentration of schools where over 80% of the teacher´s skills were evaluated as basic or insufficient. The Chilean Ministry of Education considers that teachers who obtain either of these two scores are not qualified to be teaching in public schools. When analyzing the map we can see that there is clearly a concentration of schools where teachers are not qualified enough. We can expect that the quality of education they are imparting to the students will be very low.

To build this map a teacher quality index was built. The first step was to take a ratio of the teachers with insufficient or basic evaluation, to the total teachers. Only schools where over 80% of the teachers received the lowest scores were kept in the table. Each school was placed on the map (with X, Y coordinates and coordinate conversion). A kernel density raster was then performed in order to determine if there was a concentration of pootly evaluated teachers in any particular region of the country. This index was divided into 5 categories, sorted by natural breaks (Jenks).

The following layout shows the concentration of poor students in schools. The map shows that there is a clear hotspot where most of the poor students are concentrated. The Chilean government refers to these students as “vulnerable”, since their families have very low incomes, they live in precarious conditions, their parents may be missing, etc. The government divides the “vulnerable” students into 3 groups, according to their level of “vulnerability”. We can see that clearly there is an area of the country where more if these students reside, and when comparing this to our previous map, the location of the hotspot is almost the same.

To map the poverty information, a “vulnerability· index had to be built. The first step was to take a ratio of the poor students (2 priority levels of “vulnerability”) to the total students. Any school with over 75% of its students in either one of the two higher “vulnerability” groups was placed on the map (with X, Y coordinates and Coordinate conversion). A kernel density raster was then performed in order to determine if there was a concentration of poor students in any particular region of the country. This index was divided into 5 categories, sorted by natural breaks (Jenks).

The final analysis maps the communed with the lowest scored in an education index. As we have seen before, parent´s education is an important factor that determined the child´s academic performance. In this case, we can see that again there is a hotspot where the low and very low cases reside. The shaded area in the center of the country closely resembles the hotspots we had seen in the previous maps.

The education index was obtained at the commune level, for each commune where information was available. Since we could not obtain data on the years of education for each student or school, we used the education attainment level of the commune as a proxy. Also, the poverty index and median income level were added to the index, and they too serve as proxies, since we assumed people with lower educational attainment obtained lower-skilled jobs and received lower wages. Each one of these variables was joined to the commune shapefile table, and they were added to create a new index with the filed calculator. The education attainment variable was weighed at 50% and the other poverty-related variables were weighed at 25% each. The information for educational attainment was obtained from Sinim, and the information for income and povery level was obtained from the Ministry of Social Development website.

The final layout shows the schools that are located in the hotspot. The findings from the research show that these schools are the ones where the variables that determine education performance are lowest. Even though it is not clear from the test scores, this is the area where the poorest students live, where their parents have lower income and educational level, and they also have the worst teachers. It is therefore these two regions where the central government should place its attention, because these are the children who are in the most disadvantaged position with respect to the rest of the country.

GIS was very useful for this project because it allowed to visualize where the students with the most pressing educational need live in Chile. Another type of analysis would not have enabled us to see so clearly where the hotspot was, since it would not have showed this in a visual way.

 

BIBLIOGRAPHY

Brandt, Nicola. (2010). Chile: Climbing on Giants´ Shoulders: Better Schools for all Chilean Children. Organisation for Economic Co-operation and Development (OCDE).
Integrated System of Territorial Information (SIIT), Library of Congress Chile, siit.bcn.cl
National Statistics Institute Chile (INE), www.ine.cl
La Tercera, www.latercera.com
Ministry of Education Chile (Mineduc), www.mineduc.gob.cl
National System of Municipal Information Chile (Sinim), www.sinim.cl/indicadores/busq_serie_var.php
Suryadarma, Daniel, et al. (2006). Improving Student Performance in Public Primary Schools in Developing Countries. Evidence from Indonesia. World Bank Research Paper.
The Clinic, www.theclinic.cl
University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE), www.demre.cl/estadisticas.htm
University of Chile, Department of Education Evaluation, Measurement and Registry (DEMRE). (2012). Compendio Estadístico Proceso de Admisión Año Académico 2012. Santiago, University of Chile.

World Bank, Data Catalog, data.worldbank.org

 

 

Vernessa Shih – Rebuilding Louisiana Final Project BLOG write up

  Rebuilding a Community Post Hurricane Katrina (document link)

 Rebuilding a Community Post Hurricane Katrina

By: Vernessa Shih

Introduction

My topic of study was seeing the effects of the incredibly damaging Hurricane Katrina on the State of Louisiana and particularly a five parish area that was geographically closest to where the hurricane made landfall and saw the most damage.  I also wanted to see how Louisiana and the five parish area recovered from this natural disaster and what areas still needed focused attention.  My goal was to spatially show the areas in need of greater assistance and perhaps hypothesize about abnormalities or patterns shown in the maps. 

When I began my project I identified a few questions that I wanted to address.

  1. What happened?
  2. What were the rebuilding goals?
  3. What were parish priorities? (particularly those in the hardest hit region)
  4. What areas saw the most success in rebuilding efforts?
  5. What areas need the most attention as of NOW?

I started with looking at what actually happened by creating a map to show the exact path of Hurricane Katrina and the strength category of the hurricane as is progressed from August 23 2005 to August 31 2005.  Hurricane Katrina began as a category 1 tropical depression, peaked at a category 6 hurricane over the Gulf of Mexico before making landfall in Louisiana as a category 4 hurricane with a high of 140 miles per hour wind speed.

Area of Study: 5 Parishes

Orleans, St. Tammany, St. Bernard, Jefferson, and Plaquemines Parishes

As you can see below, Plaquemines parish, St. Bernard parish and St. Tammany parish took a direct hit from the hurricane path with neighboring areas Orleans and Jefferson parish also in very close proximity to the hurricane path.

The effects of Hurricane Katrina have deeply affected not just the residents of Louisiana or any other southern state that was physically affected, but it called into question how our nation as a whole responds to geographic disasters.  The actions of FEMA and the President were criticized as being too slow and not drastic enough to account for projected damage that would result from this hurricane.  The levee failures that increased the amount of flooding to up to 90% in some parishes called into question the work of the US Army Engineers Corps, who is tasked with building and maintaining levees all across America.

While accurate summaries of damage estimates are difficult to find due to changes in how government agencies were categorizing damage, I complied a few generally agreed upon facts.  Over 200,000 homes were severely damaged, 1.4 million people were displaced with 15 million total people affected by the hurricane, over 1,800 deaths, extreme coastal erosion (up to 50 years of coastal damage within one  week) and an estimated $75 to $110 billion dollars worth of damage.  Hurricane Katrina definitely earned the title of “Costliest Hurricane in History” and this is only the monetary estimate, there is no possible was to estimate the damage it has done the the culture and community of all the affected areas, as well as the questions and doubt that arose from the mishandling by FEMA.

Recovery Efforts

When the waters receded and determined citizens began looking to rebuild, the state of Louisiana formed the Louisiana Recovery Authority which produced “Louisiana Speaks – Long Term Recovery Plan”.  This joint effort plan held community meetings in 25 southern Louisiana parishes over the course of two days and asked them a series of questions  to rank their desired priorities of action.  Overwhelming, 98% of the parishes  strongly agreed that they should build back differently to address issues of poverty, hurricane/flood risk and environmental risk.  74% of surveyed respondents agreed that some places in Louisiana are too at risk to rebuild. And one perfectly divisive issue with a 50/50 split was whether or not everyone who wanted to return could come back to their original home site.  Overall, when the respondents were asked to rank their most important priorities from a selection of about 20 options they favored building better levees, encouraging development, improving schools, increasing business and job opportunities as well as devising a workable evacuation plan.

One of the major criticisms of the handling of Hurricane Katrina was regarding how  FEMA reacted to the hurricane.  It took multiple days to evacuate those in affected areas and then several more days to get the necessary supplies to the Superdome which sheltered up to 25,000 hurricane survivors.   In the map below, I aimed to show the approved FEMA evacuation routes for Louisiana and highlight what I believe to be one massive error.  The coastal areas of Louisiana are those which are most susceptible to damage and regardless of the lower population density, those regions should be more heavily covered with evacuation routes.

And as difficult as it was to track displaced citizens, I was able to calculate a breakdown of where residents were tracked going through FEMA aid requests.  It seems a majority of those that left the state went to neighboring Texas (about 90,000 people) and Mississippi and Georgia (about 18,00 people per state).  Those moving instate favored Baton Rouge with about 34,000 displaced residents.  Because of the transient nature of disaster victims, I chose to represent their immediate preferences (between 2006-2007) with these pie charts.

In further study of displaced residents, I wanted to see how many parishes recovered their lost residents by looking at population changes and changes in occupied housing between the years of 2000 and 2010.   As you can see with the Population Change map, St. Bernard and Orleans parish lost a significant percentage of their population with obvious gains in neighboring St. Tammany, Tangipahoa, Livingston, and Ascension parish.  Further evidence of this population shift is seen in the change in occupied housing.  Again, it is Orleans and St. Bernard Parish which saw the most drastic losses with gains in neighboring parishes that suffered less damage.

Considering that infrastructure and improvements in schools were listed as major priorities to the entire state of Louisiana as well as the 5 parish study area, I chose to look at the prevalence of schools and hospitals in Louisiana. While initially, it seemed that the spread of schools and hospitals was pretty even across the state of Louisiana, I decided to show a distance buffer of 5 and 10 miles over the current state population to analyze whether there were under served communities.

Looking at the Service Area for Hospitals map, I can identify many areas that are under served including much of Jefferson, St. Bernard, and Plaquemines parish.  There is also a significant under served population in highly populated Alexandria area.  When I created the Service Area for schools map, I saw that there was much more complete coverage, except in certain areas in St. Bernard and Plaquemines parish.  While theoretically, it makes sense for there to be more schools than hospitals, as hospitals have higher start up capital and associated costs, there are some highly under served  communities that will require increased investment in hospital expansion.  However, it is important to note that up to 30 hospitals were affected or damaged by Hurricane Katrina with about 10 closing permanently due to damage.

I then chose to look at dropout rates for students between 7th and 12th grade as an indicator for improvement of schools.   In order to do this I had to create an entirely new dataset that had specific dropout numbers for each of the individual schools in the five parish study area.  After geocoding each of these schools, I was able to compare the number of students who dropped out in 2005 and the number of students that dropped out in 2010 to see which areas saw the greatest decrease in dropout students. I was personally interested to see if there would be a correlation between dropout rates and race and as Orleans Parish in particular has a high rate of African American residents.  Therefore, I chose to overlay African American population data to see if there were any outstanding patterns.  The largest pattern I saw was that the majority of reduced dropout rates came from downtown metropolitan New Orleans City which has a densely populated African American population.  There actually seems to be no strong correlation between population density of African American residents and reduction or increase in dropout rates.

The final map I compiled was to look at multiple attributes and create an index to see the areas that I believe require the most focus and attention for additional recovery and growth.  I began by collecting data for Louisiana as a whole, for residents living below the poverty line, the number of vacant housing units per parish, the number of unemployed workers in the labor force and the number of high school dropouts per parish.  I created a raster for each of these attributes and reclassified them to show areas of highest percentages to be of higher risk and thus importance.  I then used map algebra to create a Priority Need Index for the entire state of Louisiana.  The findings from this map show that Orleans, Jefferson, Lafayette, Calcasieu, Baton Rouge, Ouachita, and Caddo parishes have the highest priority on the index.  While I understand that these regions might have been identified due to the economic downturn which increased unemployment rates across the entire nation, it is important to note that Louisiana still has devastating lingering affects due to Hurricane Katrina that needs to be addressed.  As more disasters occur, we cannot forget the communities already affected by disaster and call for change and growth with our nation’s disaster response, management and recovery.

Conclusions:

I found geospatial mapping to be extremely useful in showing dramatic areas of need or priority.  Using rasters and change maps, it is easy to quickly identify differences across multiple areas in a far more dynamic method than showing changes in data tables.  That being said, I identified several areas across that state of Louisiana that should be hailed for their decrease in dropout rates or low percentage of unemployed residents.  However, I also identified several areas that are struggling to this day.  Orleans and Jefferson parish were hit particularly hard by Hurricane Katrina and while they have made great strides to recovery, we can see with the Need index that they still have a long road ahead.

Complications:

I ran into several complications with data regarding access to databases and clearinghouses that have since been closed to public access.  Disaster data by nature is quite unreliable due to the constant changes in population and displaced residents changing locations.

Skills:

For the creation of the base maps and attribute tables, I used geoprocessing clipping and joining skills, as well as boundary sub-set selection, aggregating attribute fields and creating custom shape files to create the 5 parish map and joining for attaching attribute data.  To highlight features or points I used point/line graduated symbols and KML files.  I used images and pie charts to highlight additional information that was not shown spatially.  I then created original data by geocoding addresses and did distance analysis by creating concentric buffers.  I used spatial analysis and model building to create and reclassify rasters in order to show hotspot analysis for a Priority Need index.

Sources:

  • Esri Tiger for Geographic Shapefiles
  • Social Explorer/Census for attribute data
  • Google earth  – Hurricane Katrina Route
  • Louisiana Site Selection for Hospital and School data
  • Louisiana Department of Education for 7-12th grader dropout rates
  • Greater New Orleans Community Data Center for comparison data, Orleans Facts
  • FEMA.gov for FEMA evacuation routes and post Katrina reports

 

Model: Used Model builder to turn features to rasters, I then reclassified the rasters and used map algebra to create an index of Priority Need

Stella Kim – Final Write Up Blog and Powerpoint Presentation

Final PowerPoint:

CRIMEgisStellaKIM2

My final project was to examine crime incidence in the city of San Francisco. According to FBI reports this past year San Francisco is a huge city with a populace of over 805,235 people. It is also a minority- majority city where there are more ethnic populations than white as a whole.This presents an opportunity to examine where factors of poverty based on median income, high school drop out rate, and distance to police stations can influence the locations and incidence of crime. By examine several of these factors, I hoped to pinpoint areas of need and consideration for the city of San Francisco in terms of future crime prevention and areas of focus in resource allocation regarding public safety.

Map 1: Crime Incidents in San Francisco 2003

Map 1 displays the hotspots for crime incidence in San Francisco. In this large city, in 2003 there were nearly 15,000 crime incidents reported. This includes both violent and nonviolent crimes. As you can see from the map, they are concentrated mostly in the defined planning districts of the Downtown/Civic Center, South of Market, Chinatown, Bayview, and the Mission district. Data for the crime incidents in 2003 was found at https://data.sfgov.org/ and a point shapefile was derived from the latitude and longitude of this data to analyze it through a kernel density raster to find hotspots.

Map 2: Crime Incidents in San Francisco 2012

Map 2 is a kernel density analysis for hotspots of crime in 2012. By examining the hotspots of crime, although 2012 had only about 10,000 incidents of crime in comparison to 2003, it is clear that the hotspots for crime remain almost the same. Still in 2012, the areas of the Mission District, South of Market, Downtown/Civic Center, and Bayview seem to have the highest incidence of crime. In addition the Haight and Ashbury area and the Castro area have further defined areas of high crime in comparison to 2003. Overall in all types of crimes, Map 2 shows little difference in terms of areas of focus for crime prevention from 2003 to 2012. I was hoping to show a reduction in hotspot areas because according to report regarding FBI crime data for counties and cities, San Francisco’s crime rate was greatly reduced from 2004-2011.  Map 2 was also derived from data from https://data.sfgov.org/ for 2012 crime and made into a point file which was used for the kernel density analysis.
Map 3 : Daytime Violent Crime Hotspots in 2012

To further examine violent crimes: murder, rape, assault and robbery, I took a look at the density of violent crime during the day and the evening for the 2012 data only. To first weed out only the violent crimes, I used an excel formula function to select those that can be defined as violent crimes. I exported that into a separate excel sheet. I also coded the excel crimes that could be considered daytime crimes and nighttime crimes and left them in the violent crime data.To show only the violent crime during the day, I used the select by attribute tool in ArcMap to select only the “day” labeled crimes for the kernel density analysis.

As you can see from map 2, the hotspots of violent crime during the day are fairly consistent with the incidence of all crimes in 2012. So it goes to show that even during the day, violent crimes occur within the same location parameters as all crime occurrence.

Map 4: Daytime Violent Crime Hotspots in Proximity to Police Stations

In Map 4, I took that same violent crime data for daytime incidences and layered a buffer of San Francisco Police Stations. The light yellow buffers surrounding the police stations show a proximity of a 1 mile difference. The data for the police stations was taken from the http://sf-police.org website which allowed me to create an excel sheet and add in location and address information and geocode the information to create a new point shapefile through ArcGIS.

Looking at the police station proximity, one can see that in the highest risk areas, of the Mission District and Downtown/Civic Areas, and Bayview areas, they are all within a 1 mile distance from police stations. That doesn’t seem to be a deterrent for incidence of violent crime during the day.

Map 5: Night-time Violent Crime Hotspots 2012

Map 5 is taking a look at violent crime incidents at night from the 2012 data. Since I already defined night-time and daytime incidents, I selected only the incidents that occurred at night with the select by tool in ArcMap to create this kernel density analysis of hotspots of violent crime incidence at night. I thought there might be a larger area of night time incidents because overall there were more night-time violent incidents, but it was consistent with Map 2 and the overall hotspots identified for 2012.

Map 6: Night-time Violent Crime Hotspots in Proximity to Police Stations

Map 6 shows the proximity of violent crime hotspots from night-time incidents to 1 mile buffered areas around police stations. So you can see that that even though to a certain extent, police do have areas that cover most of  San Francisco within 1 mile buffered zones, that doesn’t seem to influence or affect the hotspot areas indicated by violent crimes that occurred at night.

 

Map 7: Population Density and Day-time Violent Crimes

Map 7 shows the population density for San Francisco by census tract which shows a graduated density with the deeper purple areas as the areas of most population density. One of the factors that could lead to more crime in any area could be population density and this map looks at the possible association. However, the violent crimes during the day are not inherently consistent with the most populated areas. For example, the Outer Sunset and Parkside areas are very populous but do not have as many incidences of crime as the aforementioned Civic/Center and Mission areas.

Map 8: Population Density and Daytime Violent Crimes with Proximity to Police Stations

Map 8’s data looks at the population density and the proximity to police stations interposed on the population density. Since police station jurisdiction ideally would influence the occurrence of violent crime, we can see that despite that, the areas that are most populated don’t exactly correspond to areas of highest crime.

 

Map 9: Night-time Violent Crimes and Population Density

Map 10:Population Density and Night-time Violent crimes with proximity to Police Stations.

Maps 9 and 10 examine population density and areas of violent crime occurrence during the night-time hours. These maps were taken by taking census data for population in census tracts and creating a graduated polygon ArcGIS shapefile. By looking at population density and the occurrence of night-time violent crimes, it is apparent that population density cannot be one or the only key factor to influence incidence of violent crimes.

Map 11: Service Areas of Police Stations through Network Analysis

Map 12: Service Areas of Police Stations and Violent Crime Incidence in 2012

Maps 11 and 12 are looking at a network analysis by juxtaposing San Francisco roads to create an area of service for the police stations. Starting with the geocoded police stations, I was able to find a roads shapefile from https://data.sfgov.org/ to create a network analysis file. From there using the network analysis tools for service areas around the defined locations of the police stations, I defined 2 minute, 4 minute and 6 minute service areas that could be easily reached from the police stations. The caveat being that network analysis does not always take into consideration traffic. I could not find all current traffic conditions to change the miles per hour, so from the network analysis most of the whole city of San Francisco can be reached within 6 minutes from the defined centers of the police stations.

Thus looking at all the incidences of violent crime for 2012, at first glance it seems that police stations for the most part were reachable within the 2-4 minute ranges. So once again it is seen that even with very close proximity and access to police stations, the violent crimes occurred regardless.

Map 13: Raster Analysis of Median Income, Unemployment Rate, Distance to Police Stations

Map 13 examines the potential for crime occurrence based on the factors of Median Income, Proximity to Police Stations, Unemployment Rate, and High School Drop out Rate. This information was derived from census data and modeled through a composite raster of these 4 determinants. First the unemployment rate, high school drop out rate, and unemployment rate were changed to rasters through the feature to raster function. The proximity to police stations was derived by measuring distance of each centroid of census tract to the police stations. Then all 4 were reclassified through a model and weighted equally to create this composite raster analysis of areas of greatest need.

Since population density, or proximity to police stations wasn’t enough to determine areas of greater crime, adding all four of these determinants allowed for a fuller picture of where could be the greatest areas of risk for the city of San Francisco. However, they do not exactly correspond with the incidence of hotspots for crimes in 2012. Still, though, gradation shows that the areas that are red and orange show the greatest need and those do correspond with the areas of need in the Civic/Center Downtown and Mission areas, which have been seen to have the greatest incidences of crime.

Map 14: Geographic Terrain and Violent Crime Incidence 2012

Just to examine a possible 3D analysis I took a terrain shapefile from https://data.sfgov.org/ to show hills and higher elevations. However, the 3D analysis in Arcscene didn’t exactly work out to create a 3D file. However, you can see the blue dots which are violent crime incidences can be seen as occurring on the flatter terrain versus the more hilly terrains. This doesn’t prove anything per se, but it was interesting to see that outcome.

Findings and Further Examination:

Through this project I was able to look at a variety of conditions and variables that could influence or indicate why crime was occurring in specific areas. For an analysis of where crime could occur based on median income, proximity to police stations, unemployment rate, and high school drop out rate, specific areas of need were determined.CRIMEgisStellaKIM2 Because these determinants could also contribute to other needs based socioeconomic factors, it would be interesting for further study to examine these areas more in depth and see if there are other mappable factors that are present besides crime incidence. For example, perhaps lower test scores ,less access to schools and other factors could be explored. As for the major areas of crime incidence, for future analysis, the data presented only represents reported crime incidence. However, there is data available in the crime data that indicates if that crime was “solved” or if someone was caught at the scene of the crime. It would be interesting with further study to analyze what areas have not only the highest rates of crime, but also to see the areas where the highest areas of crime “solving”.

Arcmap is always a bit fickle with data, and even though data was thoroughly prepped with excel beforehand, transferring the XY latitude longitude data from a spreadsheet to ArcGIS presented problems because of the projections. Even with the correct XY latitude longitudes, and the correct and consistent projections, the data kept on not meshing on the same plane. So that took hours and hours in order to make the rasters work. All in all, although I may not use all of these skills, in the future I hope to be able to map community needs through these mapping skills.

 

Sources

http://www.huffingtonpost.com/2012/06/18/san-francisco-crime-dropping_n_1606641.html

http://www.cityrating.com/crime-statistics/california/san-francisco.html#.UM52T4bgz2o

https://data.sfgov.org/

Models:

 

LA’s Groundwater Resources and Threats

LOS ANGELES WATER RESOURCES

A top dream job of mine would be to serve a stint as an early 19th century American explorer. Adventure aside, to a large degree the appeal of such work would be in the opportunity to chart regions of the world still unmapped. Well into the 21st century, it turns out this opportunity still exists. Despite a wealth of data, a number of supervisory agencies, and no shortage of technical expertise, no one has effectively mapped the groundwater resources of Los Angeles. This all stands in spite of the fact that over 7 million people depend on Los Angeles’s groundwater resources for their freshwater needs. Apparently the bureaucratic wilderness of Los Angeles’s water infrastructure is something the likes of Lewis and Clark never had to contend with.

Los Angeles’s ability to thrive as a megalopolis in the arid southwest has always deepened on its ability to access freshwater. This continuous balancing act, however, is threated by many uncertainties, not the least of which is the imposing threat climate change poses on Los Angeles’s freshwater future. Past solutions, such as importing water from elsewhere, are no longer economically, politically, or environmentally feasible. If Los Angeles hopes to continue to prosper, any further fresh water gains will have to come through better management of the resources the region already possesses.

Better utilization of Los Angeles’s groundwater resources has been identified as the best strategy for increasing the region’s water security. The details of this strategy entail the active participation and sacrifice of the public and business communities. The first step, then, is effectively communicating to the public that issues with Los Angeles’s groundwater exists.  The fact that to a large degree Los Angele’s citizens are not aware that the region possesses groundwater, or that   42% of their freshwater comes from these aquifers, speaks to the outreach that needs to be done.

Yet when trying to uncover information about Los Angeles’s groundwater resources one immediately becomes mired in conflicting documents, findings, and bureaucratic reports. This information is in large part made for and by technical experts, and utterly fails at communicating the issues at hand to the general public. The situation is not made any more transparent by the fact that Los Angeles’s groundwater basins are overseen by agencies at every level of local, state, and federal government. To navigate through this complicated nexus of data Los Angeles’s groundwater resources needs a modern day explorer to chart the relevant data and have it mapped.

The first section of maps serves as an introduction to the groundwater resources available in the Los Angeles Basin. The data from these maps was taken from a variety of water agency sources. Using the most up to date and reliable reports, these maps use the relevant data to draw attention to the location and extent of the region’s ten groundwater basins. Furthermore, important characteristics of each basin, such as productivity, size, and unused capacity, are outlined. Through these maps the resources and potential opportunities of Los Angeles’s groundwater basins are made legible. Further, they make the argument visually that the region possesses an important resource that can be utilized to increase Los Angeles’s freshwater security.

While the first section of maps ends by suggesting the potential of Los Angele’s groundwater resources, the second set of maps begins by showing what threats stand in the way.  Los Angeles has a compendium of noxious land uses spread across its landscape, and has long suffered the resulting consequences. Historically, attention has been given to how these uses have degraded air quality, but just as importantly, Los Angeles’s ground water has been severely impacted by pollution.  Using data collected from the California Water Resources Board, the Environmental Protection Agency, and the National Library of Medicine, Los Angeles’s largest contaminators of groundwater are mapped in juxtaposition with the region’s major groundwater basins. Furthermore, snapshots of individual polluters are mapped to give context into the types of land uses threatening Los Angeles’s groundwater.  Here the visual argument is made that if Los Angeles hopes to make better use of its groundwater resources, steps to undoing the damage done by major polluters must be taken.

As the second set of maps makes clear, Los Angeles has no shortage of threats to its groundwater resources. However, due to economic and political realities, each of these threats cannot be addressed simultaneously. The third section of maps seeks to establish a way to prioritize pollution mitigation. The variables of poverty, groundwater basins, and groundwater pollution are used, and through the maps it is identified where they coincide. The maps make the argument that it is at these locations where mitigation efforts should be prioritized and concentrated.

Together these maps seek to highlight in a legible and effective way the resources, opportunities and threats of Los Angeles’s groundwater basins. The audience is decidedly the public in general, for it is the everyday Angelino that depends on groundwater resources the most, yet, who is most likely to be unaware of groundwater’s existence.  If meaningful policy to improve Los Angele’s water security is to be enacted the public must be effectively sold the idea. These maps, above all, are an attempt to do that, by charting in a clear and transparent matter Los Angeles’s most important, yet misunderstood resource.

 

Final write-up

Tsunami Preparedness in Los Angeles

For my final GIS presentation, I investigated Tsunami preparedness in the areas along the coastline of Los Angeles County. Before doing this investigation, I first surveyed the Japanese Tsunami preparedness policy since northeastern part of Japan was hit by a massive earthquake and Tsunami on March 11, 2011. Since then, Japanese government has started examining various new policies for Tsunami preparedness.

What caused so big damages in the coastal areas of Japan? There are two major causes. One is that the height of Tsunami was much larger than that had been expected. The Tsunami was higher than two or three story buildings. As a result, the Tsunami easily overflowed the Tsunami barriers constructed by governments. The other is that the local people were not prepared for so big Tsunami

Fig. 1: A new scheme for Tsunami Preparedness (Figure Source: Shizuoka Prefecture,http://www.e-quakes.pref.shizuoka.jp/shiraberu/hinan/05/04.html)

As one of the Tsunami preparedness schemes after the 2011 Tsunami, a scheme of using existing tall buildings as Tsunami evacuation buildings is becoming popular in local governments of Japan (Fig.1). Among the tall buildings existing in the expected inundation areas, local governments selected the buildings that meet the requirements as Tsunami evacuation building. Each evacuation buildings are expected to cover the communities that are 200 m from the buildings. Once an earthquake occurs, community people are able to reach these buildings within a short time. The above Tsunami preparedness scheme hereinafter referred to as “the Tsunami evacuation building scheme” is mainly intended for near-field earthquakes where the evacuation time is very much limited. Under severe budget, this approach is very attractive to local governments.

By examining statistical data concerning Los Angeles coastal areas, I herein examine the problems and possibility of applying the Tsunami evacuation building scheme to these areas. I hope my maps could shed some new light on Tsunami preparedness policy of Los Angeles. My research focuses on the following three points.

  • Which areas are lacking in multistory buildings for evacuation?
  • Which areas will be seriously damaged by Tsunami?
  • Which areas need Tsunami evacuation buildings?

In the present study, it is assumed that Tsunami inundation area is within 2,000 m from coastline as shown in Map. 1.

Map. 1: Study Area

Map 2 shows the multistory buildings over 65 feet currently existing in the inundation area (Total number of buildings: 254,391). In this map, 200 m and 500 m buffers are taken for the respective buildings to show the covered areas. To reach the buildings of 200 m and 500 m distances, average male adults need 2.5 and 6.25 minutes, respectively, on foot, if their averaged walk speed is assumed 80 m/ minutes.  It can be seen from Map 2 that there are many areas that are not covered by tall building. Here, I did not consider whether or not these buildings have been upgraded for earthquakes.

Map. 2: Areas covered by existing buildings (200 and 500 m)     (Data Source:LA City Buildings with zoning codes, http://up206a.yohman.com/weeks/week-9/)

Next, I investigated which areas are strongly damaged by Tsunami. This is to look for the areas that among others need Tsunami evacuation buildings. As possible factors that increase the magnitude of Tsunami damage, I chose four factors; low elevation, old buildings, high percentage of elderly people, close distance from coastal line.

 

1. Elevation (Map. 3)

I selected areas with elevation (including structure height) under 100 feet. Especially, larger part of Santa Monica is located in lower elevation, compared to other areas.

Map. 3: Elevation(Data Source:LA City Buildings with zoning codes, http://up206a.yohman.com/weeks/week-9/)

2. Age of buildings(Map. 4)

I choose this factor because old buildings have a higher possibility of collapse during the earthquake prior to Tsunami and people in the community cannot make use of these buildings. According to previous research, buildings before 1940 are reported to have a high possibility of collapse when earthquake occurs (Yoshimura, 2005). Therefore, I classified the building in two categories, that is, the buildings constructed in and after 1940, and  those before 1940. It is observed from Map 4 that there is low correlation between height of buildings and age of buildings.

Map. 4: Age of buildings(Data Source: American Community Survey, 2011)

3. Proportion of elderly people (Map.5)

When the 2011 Tsunami hit Japan, many old people could not start the quick action of escape and failed to reach evacuation areas. From Map.5, it can be seen that the southern part of the study area has a higher proportion of elderly people.

Map. 5: Proportion of elderly people (Data Source: American Community Survey, 2011)

 4. Distance (Map.6)

As a distance , I herein used a straight-line distance from the coastline.

Map. 6: Distance

Next, I proceeded to figure out the areas with higher possibility of damages by considering the four factors shown in Maps 2~6. I employed spatial analysis in order to find out the heaviest damage areas. Parameters and weight are summarized in Table. 1. The smallest weighed of 10% is herein set to the age of buildings because the minimum survey size is limited to the average age of buildings existing in a block. In addition, it cannot be identified whether or not old buildings originally constructed before 1940 were upgraded for earthquake preparation after 1940.

Table. 1

In Map. 7, blue color denotes higher expected damage areas, while yellow color denotes lower expected damage areas.

Map. 7: Possible damage areas

To find out high priority areas for Tsunami preparedness, I overlaid Map. 2 showing the areas covered by existing tall evacuation buildings on Map. 7of Tsunami damage areas. The result is shown in Map.8. It is demonstrated by Map. 8 that the areas close to Malibu Beach coast, around Santa Monica Airport, areas nestled between Marina Del Rey and Lax and coast areas close to Manhattan and Hermosa Beach have higher damage possibility of Tsunami but not covered by Tsunami evacuation buildings.

Map. 8: High priority areas for Tsunami preparedness

In conclusion, Malibu Beach coast, around Santa Monica Airport, areas nestled between Marina Del Rey and Lax and coast areas close to Manhattan and Hermosa Beach have higher priority for Tsunami preparedness. I would like to say that the proposed scheme for Tsunami preparedness using existing tall buildings as evacuation areas is feasible to adopt since the government does not need bigger budget. My analysis is still rough and insufficient. However, I hope that the present research suggest something to the real world.

All maps are created by Nobuko Goto

Sources: Guideline of Tsunami evacuation building, Cabinet Office, Japanese Government, http://www.bousai.go.jp/oshirase/h17/tsunami_hinan.html

Los Angeles County Tsunami Inundation Map, http://www.conservation.ca.gov/cgs/geologic_hazards/Tsunami/Inundation_Maps/LosAngeles/Pages/LosAngeles.aspx

Miho Yoshimura, The introduction of incentives to earthquake vulnerability of buildings (September, 2005) http://repository.dl.itc.u-tokyo.ac.jp/dspace/handle/2261/50136

Model Used: reclassify rasters

 

 

 

Final Write-up – Xingzhou Li

The topic of my project is building a bike rental system in San Francisco. I choose this project because the San Francisco city government has proposed to promote biking as an alternative to driving, which is an important strategy in the city’s climate change action plan. I think bike rental is a great way of promoting biking, and I want to integrate the bike rental system with the current bus system in San Francisco by putting the bike rental stations close to the bus stops so that people can switch between these two different transportation modes conveniently.

Here is the structure of my project:

  1. Find 5 best locations to build bike rental stations in San Francisco.
  2. Analyze my proposed stations. Specifically, I would create buffers to get information within the buffers, create service areas to see which neighborhoods are covered by these stations and use OD matrix to see how the stations are integrated with the bus system.
  3. Analyze how the bike rental system would expand over time.

My first layout shows the percentage of workers who use bike as the main means of going to work in San Francisco. Apparently, biking is not a popular way of going to work in San Francisco, at least now.

My target is to locate 5 bike rental stations in San Francisco. I choose 4 criteria for selecting the best location:

  • Median household income: I want to locate the stations where household income is low;
  • Population density: I want to locate the stations where population density is high;
  • Young population percentage: I think young people are more likely to ride bikes so I want to locate the stations where there are more young people (I define young population as people from 14 to 34 years old);
  • Distance to the nearest bus stop: Since I want to integrate the bike rental system with the bus system, I want to locate the stations as close to the bus stop as possible.

This layout shows these 4 criteria in San Francisco.

Each criterion is given the same weight. Using hotspot analysis, I get the priority map to help me locate the rental stations.

These are the models I used to run the hotspot analysis.

 

However, the priority map only illustrates the high-priority rasters, and I still need to choose the specific locations from these rasters manually. To do this, I first check the buildings and facilities in each bunch of raster. I try to locate the rental station in an open space in the center of the area. For instance, in this map, I locate the station in the circle in the center, which seems to be an open space.

Then I use Google Map to get the specific address for the location I just chose.

By repeating this process, I get 5 specific locations to place the stations in the map.

By geocoding their addresses I show these proposed stations in the following map.

In this layout I create 1000-feet buffers for each station and extract information from the buffers. The young population percentage is relatively high within the buffers, but the percentage of workers who use bikes to go to work varies from station to station.

Here is the model I used to create the buffers.

This layout shows the service areas of each station within 3, 5 and 10 minutes’ walk. I checked Wikipedia and found the “preferred walking speed”, or the speed humans tend to walk at without extreme circumstances, is 1.4m/s, so I used this speed to calculate the service areas.

This layout shows the OD matrix within 10 minutes’ walk from proposed bike rental stations to bus stops. From any of the 5 rental stations, people can walk within 10 minutes to many bus stops, which means the bike rental system is well integrated with the bus system.

The last thing is using the time-based analysis to see how the bike rental system would expand over time until 2020. I have already proposed 5 rental stations to be built in 2013. For 2014, I would choose 5 new stations, using the same 4 criteria as before as long as one more criterion: the distance to the existing rental stations. We do not want the rental stations to be too close to each other. Each criterion is still given the same weight. After using hotspot analysis to get 5 locations for 2014, I merge these new locations with the 2013 locations, add a field indicating year in the attribute table and use the merged file in the analysis of 2015. By repeating this process and adding 5 new stations every year (it takes some time!), I finally get the movie in the presentation (See the movie in my presentation… I forgot to copy the original movie from the lab computer so I currently have no access to the original movie).

The main findings of this project include where to locate the bike rental stations, what the service areas of these stations are, how the system is integrated with the bus system and how the system would expand until 2020.

My project has several limitations. First, there might be disagreements on the criteria and weights I choose in the hotspot analysis. For instance, because I give each criterion the same weight, the distance to the existing rental stations only counts 1/5 in the hotspot analysis, so the southwest area in San Francisco never gets a chance to get high scores (they are far from the existing stations, but might not be competitive in other criteria), and the system never expands there even after several years. Another limitation is that in the time-based analysis, I assume some factors like median income and young population percentage to be constant, because it is both difficult and beyond the scope of this project to make projections about how these factors would change in the future.

Last words about data sources: The census trace demographic data are from ACS 2006-2010 found in Social Explorer. The San Francisco bus stops data are from SFMTA.