Final Project: The Ideal Neighborhood

In my final project I used Geographic Information System software, ArcGIS, to help a fictitious family find ideal neighborhoods to potentially live in. Mr. Wilson is the name of the dad of the family who reside in a suburb on the outskirts of Seattle, Washington. Recently though, Mr. Wilson has been informed that his business is transferring him to the city of Berkeley, located in Bay Area of Northern California. Mr. Wilson has never been to the Bay Area and knows that moving his family to the right neighborhood will not be an easy task. Since there are a lot of cities located near Berkeley, he knows that he will not have enough time to do all of the research. He has hired me, a freelance GIS specialist, to facilitate the process.

Berkeley is located east of San Francisco in Alameda County. Mr. Wilson wanted me to focus my work on Alameda County because a friend had informed him that its cities have a good variety of housing prices and good public schools. The main downside that his friend warned him of is the number of violent crimes per year in the area. Mr. Wilson informed me that I should research and analyze my data based on his friend’s advice. With this request I researched and analyzed the average school ranking, violent crimes, and average housing prices of each city in Alameda County. I used statistics from the year 2010 to help compare each city. To do the research I used the skills that I have acquired while attaining my degree from UCLA. I scoured the Internet until I found recent information that was reliable. With this information I made three different ArcGIS maps, each one representing one of the aspects Mr. Wilson was looking for in a city. To input the data that I found I used the edit tool bar and started an edit session. Once the edit session was started, I manually inputted the numbers in their respective maps. Once the numbers were finalized, I stopped and saved the edit session. I went through each map and opened up the preferences. From preferences I went to symbology tab, chose category, and made it so different number ranges would be represented by different colors on each map. Once I had the statistics  and three easily readable maps, I placed the maps on one sheet. I also added a North arrow, a scale bar, labels, and a title to make the map presentable.

I presented this map to Mr. Wilson before I did more research and analysis. I told him that GIS gives an objective representation of data and provides a visual representation of the problem we are solving. Ultimately though, it would be his subjective interpretation of the data and the maps that would lead to the choice that he thought best fit the needs of himself and his family. The best city for schools and the least violent was Pleasanton. There were two problems with Pleasanton though; the first was the extremely high housing prices and the second was that it was the farthest city from Berkeley in the county. Fremont is also far, relatively expensive, and relatively lower education ranking than other close by cities. Oakland was instantly crossed off the list because of its extremely high numbers of violent crimes. Hayward would not work either because of the low school rankings. The only cities left were Berkeley and Alameda. Although Alameda had a lower violent crimes number, Berkeley had cheaper housing and a better education ranking. Mr. Wilson decided that Berkeley was the winner because it had two out of the three criteria that he had set for a perfect city. 

With Berkeley as the choice of city, it was time for some more in depth spatial analysis. I asked Mr. Wilson to choose the paramount neighborhood detail he did not want his family to live near and the one  that he wanted. He chose Registered Sex Offenders as something he did not want to live near and local parks as something that he did want to live near. He told me that he chose registered sex offenders as something he did not want to live by because of Berkeley’s crime numbers in 2010; conversely, he chose parks to live by because he loves playing ball with this two sons. He told me that he did not want to live within 2,000 feet of a registered sex offender but wanted to live within 1,000 feet of a park. With this information I went to work again in GIS in order facilitate my spatial analysis of Berkeley. 

I did more research and found the name and address of every Registered Sex Offender living in the City of Berkeley from the City of Berkeley Police Department website. I inputted this information into an excel sheet, separating the name, address, and zip code into different columns. Once I finished the excel sheet I opened ArcCatalogue and exported the data into a dbf file. Then I right-clicked in ArcCatalogue and chose create address locator. I chose to create one that found both the address and zip code because that information was present in my newly created dbf file. I imported the dbf file into ArcMap and activated my address locator. Then I clicked on view Tools then Geocoding; geocoding allows me to take the information that was present on the dbf file and visualize the data, in order to perform spatial analysis with the GIS software. Once I had all of the address from the dbf file geocoded, I added a 2,000 feet buffer zone around them by using the buffer tool in the toolbox menu. This allowed me to see all of the neighborhoods in Berkeley that were not within 2,000 ft of a sex offender and thus suitable for the Wilson family. The next step was to add parks. The city of Berkeley has free GIS data to download and the city’s parks were available. Once I downloaded the parks I imported them into the GIS to match them up with the streets. I then added a 1,000 feet buffer zone around each park. To finalize my spatial analysis of Berkeley I put the both sets of buffer zones on the same map. This created a simple visualization to easily analyze the best neighborhoods where the Wilson’s could live near a park while avoiding registered sex offenders. I found three top neighborhoods that worked. I then zoomed in on these neighborhoods and made them presentable, labeling the streets and parks. I gave Mr. Wilson the maps and he thanked me for the great work. Mr. Wilson had narrowed down the neighborhoods that he liked without even visiting Berkeley. Now when he did visit he knew exactly where to look to find the right house and could be efficient with his valuable time.

The use of Geographic information systems (GIS) in solving important problems in our society is augmenting rapidly. In this case, GIS’s ability to spatially analyze substantially facilitated Mr. Wilson’s moving process. At first he was looking at possible neighborhoods in a whole county. Doing the research on every good neighborhood in each city would have been a long and arduous process. Research in congruence with GIS data allowed me to go from a plethora of cities and possible neighborhoods to three ideal neighborhoods for Mr. Wilson and his family. Although I could have further spatially analyzed all of the final neighborhoods, there are limitations to this process. The deeper you go into spatial analysis, the more questions of invasion of privacy are introduced; data could be acquired to analyze each neighbor’s house, their profession, information about their children, and more, until the perfect house for sale in the most ideal neighborhood was found. In this project though, this was not the case. I used GIS in a responsible way to help the average citizen with a very hard decision. Overall, GIS greatly helped Mr. Wilson and his family and will continue to be used to solve some of the formost problems of our time.

References:
http://www.census.gov/geo/www/tiger/http://www.ci.berkeley.ca.us/police

http://www.ci.berkeley.ca.us/ContentDisplay.aspx?id=55708

/http://www.rereport.com/alc/

http://school-ratings.com/counties/Alameda.htmlhttp://www.neighborhoodscout.com/ca/berkeley/crime/
http://www.neighborhoodscout.com/ca/oakland/crime/
http://www.neighborhoodscout.com/ca/alameda/crime/
http://www.neighborhoodscout.com/ca/pleasanton/crime/
http://www.neighborhoodscout.com/ca/hayward/crime/
http://www.neighborhoodscout.com/ca/fremont/crime/

Lab # 7: Spatial Interpolation

The purpose of this lab was to practice and learn spatial interpolation techniques in ArcGIS. In this lab Los Angeles County wants to compare and analyze its precipitation levels from the current season to the average and has hired me to create a series of maps to present this information. Los Angeles County was nice enough to put its precipitation data from the county’s Water Resource department on the web and thus greatly facilitated the process of retrieving the data.

Spatial interpolation can be used in useful ways when attempting to extend and analyze spatial data. Interpolation is the process of predicting the values of locations that lack sample points. It measures the relationship between objects using spatial autocorrelation and spatial dependence principles. Once the sample point’s data is found it can predict the data in the remaining area. In terms of rainfall, counties will analyze their cities that are experiencing drought and which have a healthy supply of rain. Spatial interpolation facilitates the county’s ability to make good estimations of total precipitation levels from a relatively small set of sample points, which greatly facilitates the decision process.

The maps of Los Angeles County precipitation levels show that the majority of it received similar rainfall as the normal. East Los Angeles County received more rainfall than normal as well as the western tip and the south. Inland to the north western tip showed lower rainfall than normal. To use spatial interpolation on this map I used the IDW and Spline methods. Spline makes estimations of the cell values by using math to minimize the surface curvature. The result of this is a smooth surface. Inverse Distanced Weighted (IDW) is best when the set of points is dense and can capture local surface variation. It determines the values of each cell by using the sample points. IDW worked in this case, but I do not think it was the best method because of the size of Los Angeles County. When looking at the spread of precipitation throughout the county, the spline spatial interpolation method shows more detail. The IDW has large areas that show the same precipitation levels while in the Spline map, the precipitation levels are shown with more variation throughout the county over smaller areas. The more detail found on the spline map is why I believe that it was the best spatial interpolation method to use in this case.

Lab # 6: Fire Hazard Mapping

The undertaking of creating a fire map took determination and patience. Raster spatial analysis is very interesting but requires immense attention to detail. The tutorial was very helpful in learning these processes and giving a me a base to try them with my own data. The process of finding the correct data was an adventure of its own. On the FRAP website there were a plethora of shapefiles that were alike and could easily trick you. This lab entailed an understanding and knowledge of the reclassifying of layers, analysis masking, using the raster calculator and more to create the station fire hazard map.

This lab really accentuated the unique differences between the manipulation of raster data and that of vector data. These two methods are distinct and require the user to think differently about each one. The biggest problem I encountered was reclassifying the slope of the DEM. Originally the shapefile had too many classes and could not be used in the raster calculation. This took patience to look back through the tutorial and learn the proper way to reclassify the data.

Although some of the raster spatial analysis techniques were difficult, overall they gave me a better understanding of how valuable they can be in GIS. This lab showed the importance that GIS can have when attempting to mitigate fire hazards and other natural disasters. GIS might be a computer program, but the data is real and the calculated fire hazard is real too. GIS can save lives if used properly and fully understood. This lab moved me closer to understanding the ins and outs of raster data manipulation as well as the mitigation analysis of hazards.

Lab # 5: Landfill Analysis

As my GIS map shows, there is a plethora of different land analysis that must be considered in order to build something as controversial as a landfill. Landfills can contain dangerous chemicals and materials and can be dangerous to nearby human populations and the land itself. The data for the analysis is real and must be taken seriously. The data can be used by local and federal governments along with land-use planners in order to figure out the best place to put a new landfill.

In this GIS lab I analyzed the slope of elevation of the land, distance of already in place landfills, soil drainage, proximity to streams, and land cover. Once I analyzed all of these different unique aspects of the surrounding landscape, I was able to make a final analysis in order to collaborate the information and allow for an informed decision to be made.

As the article "Feinstein, Boxer call for delay on plans to expand Central Valley Landfill" shows placing a landfill, or in the article expanding a landfill, is a complicated process that may affect communities daily lives. In the article California senators Boxer and Feinstein want to delay the expansion of a central valley landfill so that a inquiry can be made about the birth defects and deaths of local children that live in communities near the landfill. The landfill questions these allegations and claims that their landfill has nothing to do with birth defects.

GIS maps, like the ones I made in the lab, can be used to help solve this problem. If real data is given then it is possible to map the area round the landfill and which areas it possibly affects. If the information shows that there is no way that the landfill radiation or pollution reaches the community then they shouldn't be punished. If the data shows there is a high probability that the community is affected then they should take responsibility.  Although the company denies their involvement, they have been fined by the EPA for previous violations. Also, GIS can be used to be used to figure out the best place for the expansion of the landfill and the effects it will have to local land and communities. 

GIS is vital to solving real world problems like these. Analyzing individual land data then combining it to make a comprehensive report is crucial to the decision making process. Toxic waste companies should use GIS before expanding or creating new landfills in cooperation with local authorities in order to make the best decisions. GIS gives an objective analysis of information that can be easily presented.

Quiz #1

 

After deep analysis and the use of ArcGIS technology, I have come to the conclusion that I am against the proposed ordinance requiring 1,000-foot Buffer Zones around places where children congregate. If you look at my GIS map I have mapped every school, library, and park in the City of Los Angeles and also put the proposed 1,000-foot buffer-zone around each one. As you can see from the map there are a lot of areas where Medical Marijuana facilities will not be allowed to be located. One may object that even with the buff-zones there are spaces where medical marijuana clubs could be located. This is true and I do believe that there should be some regulation on how close the dispensaries can be to children, but this ordinance is too ambiguous. It will open up the door for anyone trying to find a way to close down medical marijuana clubs. Anyone could potentially be able to argue and add more places where children might congregate. A policy like this should have more specific rhetoric.

Another reason I am against this ordinance is that it may close down medical marijuana clubs that are already fall into one of the proposed 1,000 feet buffer zones. If a medical marijuana club is breaking this ordinance once it is passed, are they subject to criminal charges? Will the city facilitate the sale of their property? These questions need to be answered before I could support such a policy. If at the time any store opened legally and then later it is deemed that they cannot be there anymore, then it is not their fault and they should not be penalized.

The emergence of so many medical marijuana clubs in The City of Los Angeles is a problem and should be dealt with. This specific policy is not the right way to go. It is too ambiguous and penalizes people who unintentionally are in the wrong. I would support a policy that restricted clubs locations near specific places to keep them away from children and at the same time proposed ways to help the owners of clubs that violate the new policy.

Lab # 4 Digitizing in ArcGIS

Digitizing the Image of Iraq into ArcGIS was a very interesting process. Digitizing a country like Iraq allowed me to gain experience in digitizing points for the cities, polylines for the rivers, and polygons for the provinces.  In addition to the shapefiles that the lab asked me to make, I made a shapefile specifically for Baghdad, Iraq’s capital, in order to make it stand out and one for the international border, to highlight Iraq’s overall shape. Once I had everything digitized, I made the city names smaller and colored them green in order to distinguish them from the province names and colored the river names blue. 

After my experience with digitizing, I know why they say 99% perspiration, 1% inspiration. Digitizing was a unique experience because it was the first time we took data that wasn't formatted for ArcGIS and turned it into data that could be read and used in the program. There are many useful applications of digitizing and it will continue to be important so long as we continue to digitally map. 

Lab #3 Geocoding

The topic that I chose is a family wants to move to the city of Berkeley, California and is looking to live near a school (within 400 meters) and also (if possible) live walking distance (400 meters) from a famous Berkeley Bowl super market. The first step I took was downloading the Alameda county data from the US census website, then I cut out the Berkeley city limits and hid everything else.

To solve this problem I used Geocoding, which is the process of finding geographic coordinates from other geographic data I downloaded, from the street addresses of the schools to locate and label their locations in the city limits of Berkeley. Since there were only two Berkeley Bowls I manually found their streets on the map and made an educated guess of where they should be located. Once they were labeled I added a 400-meter buffer zone around each one to see if any neighborhoods were both 400 meters from a school and from a Berkeley Bowl. In the final result there were multiple neighborhoods that overlapped with both preferences. The schools that were located near the Berkeley Bowl west were Sunshine Pre-School and Ecole Bilingua de Berkeley and near the original Berkeley bowl were Longfellow Middle School, LeConte Elementary, and Malcolm X Elementary. Now the family can do research on theses schools to make their final decision.

Geocoding facilitated the narrowing down of the right neighborhoods to live in, and thus the solution to the problem. There are a lot of schools in Berkeley and as I experienced from the two Berkeley Bowls, manually locating the address of every school on a map would take a sufficient amount of energy and time. Geocoding allowed me to take information from an excel sheet and visualize it directly onto the GIS data of Berkeley that I had already uploaded onto the GIS software. The type of geocoding I used is called address interpolation. Since the GIS data already had the street network data, the geocoding matched with only some small problems appearing.

Although my topic only needed to be relatively accurate, one of the major barriers in geocoding is the lack of exact accuracy. Accuracy is extremely important when professionals use geocoding. An ambulance can’t only rely on geocoding because they may end up close to, but in the wrong place. As geocoding becomes increasingly more important to business, the accuracy of the street data and geocoding will only improve and it will become ever more important.