Blog Post

Emergency Planning for the City of Beverly Hills:

“Where should our containers go?” – An Initial Analysis of

Container Locations


Brief Introduction of Planning Issue

As an intern for the City of Beverly Hills Office of Emergency Management, I am often required to order, reorder, and track various supplies that the City has set aside for its populace in case of any local or large scale disaster. These supplies are located throughout the City in 8 different storage containers. The addresses for each of these are as follows:

  1. 241 Moreno Drive, Beverly Hills, CA 90210
  2. 605 N. Whittier Drive, Beverly Hills, CA 90210
  3. 624 N. Rexford Drive, Beverly Hills, CA 90210
  4. 200 S. Elm Drive, Beverly Hills, CA 90210
  5. 8701 Charleville Blvd. Beverly Hills, CA 90210
  6. 8400 Gregory Way, Beverly Hills, CA 90210
  7. 471 S. Roxbury Drive, Beverly Hills, CA 90210
  8. 1100 Coldwater Canyon, Beverly Hills, CA 90210

These 8 different containers all store equal amounts of water, food, first aid supplies, over the counter medications, blankets, cots, and various other valuable materials that can be crucial for the survival of our community during any prolonged emergency. However, these containers are all located in very “convenient” locations, by which I mean City owned property, such as parks and public school campuses. The problem with this is that these containers have no real strategic placement within the City, especially when considering vast variations in demographic characteristics, such as age or population density. Thus, it may be possible that the City can better place these containers in such a way that they are closer to higher-risk groups, such as the elderly, who are more prone to chronic diseases, disability, and the effects of disasters in general.

Therefore, the planning question I am addressing is, can we strategically place some of these containers near high risk groups, based on GIS techniques and Census data?

Layout Descriptions, Methods Used, and Limitations 




This is my introductory map. It is meant to orient the viewer to the location and boundaries of the City of Beverly Hills. As explained in the presentation, the City of Beverly Hills is rather small when compared to other cities in Southern California, or Los Angeles County for that matter. The City has a total land area of approximately 5.7 sq. miles and a population of 34,109. The data for this map was gathered from Census Tiger Data, which was found online. This map features an inset map, a smaller shapefile that was created through an attribute sub-sets selection (Beverly Hills boundary clipped from CA Places shapefile), and an extent field (red) that was added to show the area shown on the larger window.



This is the second map. It shows the relative locations of all the emergency conatiners. The emergency container locations were geocoded (by using the addresses above) and added as a separate shapefile to the layer. However, some of these locations did not display correctly, so a couple of them were added directly as a custom shapefile to the layer. Once all locations were verified to be accurate, the locations were aggregated into one layer and displayed as a separate shapefile (pictured). As one can begin to see, there is a cluster of emergency containers in the Southern portion of the City, especially in the Southeastern sector.



This is the third map. The Census Tract population data was gathered from the Census American FactFinder website. The map was constructed using the Boundary sub-set selection method in order to make the smaller Shapefile from the larger Los Angeles County census tract file. This map is meant to show the size of the population in each census tract relative to the position of each container. Graduated colors were used to ease the interpretation of this data, in which darker colors represent larger population totals and lighter colors lower population totals.



This is the fourth map. The Census Block Group population data was gathered from the Census American FactFinder website. This map displays the same type of data (population) as the map above, except its focus is the block group level, instead of census tract level. Therefore, this map is a bit more detailed for the purposes of this analysis than the previous map. Thus, one can begin to distinguish smaller geographic areas within the City with higher populations. From personal knowledge, the areas that are darker tend to be areas of the City that have a higher proportion of apartments and other multi-family housing units. It is important to locate such sectors of the City since more people within a small geographic boundary means more people may be in need of services during an emergency, especially if those people live in apartments (smaller living areas means less space to store personal emergency supplies). A similar graduated color symbology was used as before.



This map shows the total housing units by the Block Group Level. The Census Block Group population data was gathered from the Census American FactFinder website. This map is meant to complement the conclusion reached above, in which I postulate that the darker areas of the map (above) represented sectors of the City with more housing units. Again, from an emergency planning perspective, placing emergency containers closer to these high population areas may be beneficial to both the City and the recipients of such services. A similar graduated color symbology was used as before.



This map was created through the generation of my own data. I was able to take the total land area, given by the attribute table for the Census Block Group level, convert this figure from Sq. Meters to Sq. Miles, and then take the Population Totals for each Census Block Group (in the population attribute table) and divide this figure by the Sq. Miles number. These new figures were then added to a new field within the attribute table for this layer. The same graduated symbology (colors) were used to represent the data. We can now see the population density of each Census Block Group within the City of Beverly Hills. By doing this, we can see that only one container is located within an area of one of the highest population densities.



With this map, we turn our focus from high population areas which may need more services and supplies from the City simply because they have more people per square mile, to areas with a higher risk group, the elderly (defined as those >65 years). This map shows the areas with high owner occupied units by the elderly. The elderly tend to live by themselves or with their significant others, and because of their age, may need more medical attention or supplies. Because of mobility issues that arise with advancing age, they may also need distribution centers to be closer to their homes for them to use these services.



This map shows the areas with a high proportion of renter occupied units by the elderly. This specific population may be of greater concern to us since elderly that are renting may have even fewer resources than their home-owning counterparts.



This is a spatial analysis (distance) in which the emergency container locations are the centroids, and areas of darker color represent areas within the City that lie furthest away from emergency containers. As one can see, the Southern section of the City contains no dark-red colors, whereas the North and Northwestern sections of the City do contain substantial areas of red. These areas may be of concern since its inhabitants may be reluctant to travel far distances to gather supplies in the event of an emergency.



This map is similar to the one above, with the exception that it contains 0.5 mile buffers around the emergency containers in order to better understand the distances we are seeing with the spatial analysis.



In order to perform a weighted analysis in which I gave different weights to different characteristics we have observed thus far, I needed to convert some of these layers to rasters and then converting them into indices. I was able to reclassify these rasters (using the model above) and give certain characteristics appropriate weights.



In this index, I was able to combine the raster for total population and distance from containers (Total Pop + Distance). Both were weighted in a similar manner, in which 5 classifications were given (1 for lowest [distance or population] to 5 for highest [distance or population]). The resulting analysis revealed a couple areas, some extremely near containers, that can be considered high risk. This is maily due to the fact that those areas contain extreme numbers of people (when compared to other sectors of the City). One limitation with this analysis is that I incorrectly set the cell size analysis (it was way too big), and thus a better map was possible that could have revealed a better interpretation of this data.



This was yet another weighted analysis consisting of Total Elderly Owners + Total Elderly Renters = Total Elderly Population, which was then added to the Distance Raster. The weights were highest for greater distance and greater numbers of elderly in each block group, and lowest for less distance and less elderly people. Again, the limitation with this analysis is the mistake I made in setting the cell size.



This is the last analysis. I added the population raster to the distance raster by using the map algebra function (same as before). I gave greater weights to areas with higher population densities and greater distances from emergency containers, and less weight to areas of low population density and lesser distances from emergency containers. The same limitation appears here as before.


Even though my analysis was compromised by the mistake in cell size, I can undoubtably say that the City of Beverly Hills may be able to relocate emergency containers in such a way that they can better address issues of need and risk. Most of the analyses above point to a specific location that is lacking containers in both distance and all characteristics (population density and elderly population) included in the weighted analysis. This area of the City lies in the Eastern boundary of the City near the center. If at least one container is relocated from the clusters found in the Southwestern sector, it may be able to diminish the theoretically high-risk profile of this Eastern border.

Yet, I cannot help but to think whether a clearer picture could have been achieved if my mistake in cell size was corrected. Moreover, I believe the next step for the City to take is to use GIS technology to determine the best way to distribute supplies within each container (use unequal distribution as opposed to equal distribution). For instance, containers within sectors of the City with a high population of toddlers should have higher supplies of baby formula and pampers, wheres sectors of the City with more elderly populations should contain less of these items.

I believe GIS is extremely useful under these conditions, and the topic of emergency management planning in general. Despite my mistake, I was still able to locate sectors of the City that may be in greater need for a closer emergency container location. I was also able to influence my boss, Pamela Mottice-Muller, to consider relocating one of the emergency containers, and to begin plans for unequal distribution of supplies based on these slides.

Sources of Data

US Census Bureau – Tiger Data

US Census Bureau – American FactFinder