Wednesday, September 14, 2011

Final Project: Urban Runoff and Hydrology Analysis for South Bay, LA



Introduction:
                  Storm water runoff pollution is considered a major public health issue for just about any coastal urban area. The south end of the Santa Monica Bay of Los Angeles experiences quantifiable fluctuations of oceanic pollutants. These urban runoff toxins include fecal bacteria, trash, and chemical pollutants that are harmful to the local ecology as well as Los Angeles’ beach goers.   During periods of high runoff, the poor water quality of the bay can cause skin infection, respiratory irritation, and even hospitalizing sickness. Southern California is recognized worldwide for its amazing weather and beautiful beach. It is truly a shame to witness such deteriorating anthropogenic impacts within our coastal zones. Environmentally minded communities, such as the City of Santa Monica, began capturing and treating urban runoff to combat this issue. This effort has improved water quality directly off Santa Monica Pier, but the southern reaching regions of the bay are still experiencing a surplus of harmful runoff. I propose a project to better understand “runoff hotspots” within the South Bay of Los Angeles. Through GIS-based, terrain analysis and a customized reclassification I can quantify the best suited locations for storm water capture. By systematically approaching this issue, coastal communities can implement minimum infrastructural investments while still diverting maximum urban runoff. It is important to note that I am not exploring the best locations to install a storm water treatment center. My project is centered on step 1 of the mitigation of coastal pollution: street level water capture.
Methods:
                  First and foremost, I need to define my study area of interest. The South Bay of Los Angeles is comprised of 6 coastal cities. These include El Segundo, Manhattan Beach, Hermosa Beach, Redondo Beach, Torrance, and Palos Verdes. I will be exploring and manipulating a digital elevation model (DEM) that includes all 6 of these defined regions. This will be acquired for no cost though the USGS Seamless Viewer website. With this DEM, I can efficiently run my complete terrain analysis. It is important to define all of the factors that will be utilized within this specific suitability analysis. A combination of steep slope, western facing aspect (orientation), and large volume of street runoff define the perfect scenario for storm water capture. For the sake of mitigating ocean pollution, I will also define a coastal buffer of 1 mile to insure the catch basin technology will be fully diverting runoff from entering our beaches. Each of these 4 factors is then reclassified accordingly. The slope data frame is broken down into five categories ranging 1 (worst) to 5 (best). The value 1 represents minimal slope while 5 represents the steepest terrain. The watershed data frame is broken into ten categories ranging 1 (worst) to 10 (best). Again a value of 1 is equivalent to very low runoff regions while a 10 indicates high flow rates. The top watershed value is exactly double the top slope value. This is my way of weighting the watershed factor. Street runoff is the most important factor within my analysis. These two data frames will be summed giving a total range of 2 (worst) to 15 (best). My other two factors, aspect and buffer, are categorized in a binary fashion. This serves to provide a quick yes or no threshold. For aspect analysis, any orientation containing a form of west (W, SW, or NW) will receive a 1 (yes) while any other direction will receive a value of 0 (no). For the buffer analysis, any area contained by the buffer will receive a 1 while any area outside the mile threshold will receive a 0. The sum of the slope and watershed factors will be multiplied by these binary factors. This serves to eliminate any region that doesn’t face west or exceeds the defined distance from the coastline.
Map Algebra Expression:
                  Final Analysis = (“slope analysis” + “flow intensity”) * “aspect reclass” * “buffer reclass”
Analysis:
                  The four different factors that contribute to my overall analysis are each represented within individual data frames. These data frames were individually reclassified to best consider the problem of storm water runoff. The slope analysis reveals that the steepest regions are located within Palos Verdes. This area is dominated by high-ranking slope indexes. Torrance and Manhattan Beach both contain steep coastal regions as well. The flow intensity analysis reveals the highest watershed values are within Palos Verdes, but it’s also important to note the strip of orange coloring running along the entire South Bay region. This implies that there is a large volume of water running off during wet periods and high prospect for storm water capture systems. The aspect analysis is very simple and only contains two colors. The purple represents any land facing west, northwest, or southwest while the yellow contains all the remaining orientations. Overall, the strip closest to the coastline offers mostly all favorable aspect indicating a directional flow towards the ocean. Lastly, the buffer analysis serves to define the zone of interest. This zone doesn’t exceed a distance of one mile from the ocean. Through the map algebra expression (defined within the “methods” section) the final analysis reveals ultimate insight into the runoff hot spots within the South Bay. The highest scoring zones occur in Palos Verdes revealing very high potential for runoff diversion. The coastal zone of Torrance also received relatively high values as well. Redondo Beach and Hermosa Beach yielded the lowest scores indicating less storm water running off these beach communities. Moving north into Manhattan Beach and El Segundo, raster pixel values increase in the form of a consistent strip located very close to the coastline.
Conclusion:
                  By overlaying my final analysis calculations with a detailed street map of Los Angeles, I am able to define specific streets containing or adjacent to the top runoff hotspots within the South Bay of Los Angeles. Palos Verdes contained the highest index scores (purple coloring). The best candidate streets within this city are Paseo La Cresta, Via Fernandez, Via Del Monte, Paseo Del Sol, and Paseo Del Mar. These are the urban streets that would be responsible for catching the highest volume of storm water. The city of Torrance also contained solid prospects. Paseo De La Playa, Via Mesa Grande, and Via La Soledad all scored highly within my final analysis and therefore should be considered for catch basin implementation. Overall, Redondo Beach scored relatively low within the final analysis calculations, but most of the cities urban runoff could potentially be captured off Gertruda Avenue. Hermosa Beach yielded the lowest scores within the South Bay and therefore should be skipped when considering storm water capture. Manhattan Beach’s best candidate is located within the El Porto Beach parking lot. This region scored highest for the city. Finally, the city of El Segundo would capture most of their urban runoff by focusing on Vista Del Mar. This street runs parallel to the coast and therefore could serve as a solid diversion for polluting runoff. It is important to note that I did not factor in the toxicity of the individual runoff locations; this could potentially alter the focal points of my analysis. Overall, implementing a system to capture and treat storm water can juristically improve water quality throughout the entire Santa Monica Bay. My analysis yielded these specific streets as the top candidates for the South Bay region.

Wednesday, September 7, 2011

Lab 5 Interpolations


      Although these different methods of interpolation yielded slightly different estimations, the results remain constant and demonstrate that this season to date has experienced more rainfall than an average year. Every collecting station is registering rain collection that surpasses the norm. I believe the kriging method yielded more accurate, detailed results than the spline method. Comparing both maps to the original LADPW template demonstrates that the kriging method of interpolation estimated values closer to the actual measurements (for those I did not include in my table). Overall, interpolation is an easy form of estimation that reveals great spatial data.













Rain_Seas Rain_Norm Rain_Diff
























Name Latdeg Latmin Latsec LatDD Longdeg Longmin Longsec LongDD Rain_Seas Rain_Norm Rain_Diff
North Lancaster Precip   34 45 41 34.761 118 7 30 -118.125 5.31 5.19 0.12
Redman Precip   34 45 52 34.764 117 55 30 -117.925 6.85 5.18 1.67
Avek Precip   34 32 21 34.539 117 55 23 -117.92306 8.26 5.78 2.48
Rocky Buttes Precip   34 39 0 34.65 117 51 48 -117.86333 8.43 4.84 3.59
Palmdale Water Dist Precip   34 35 41 34.595 118 5 31 -118.09194 9.49 6.93 2.56
Acton Camp Precip   34 27 2 34.451 118 11 54 -118.19833 10.71 10.03 0.68
Scott Ranch Precip   34 46 59 34.783 118 28 10 -118.46944 12.24 8.25 3.99
Mescal Smith Precip   34 28 3 34.468 117 42 40 -117.71111 13.19 7.8 5.39
Quartz Hill Precip   34 38 53 34.648 118 14 24 -118.24 13.29 7.97 5.32
La Mirada Precip   33 52 59 33.883 118 1 0 -118.01667 13.58 13.45 0.13
Ballona Crk Precip   33 59 55 33.999 118 24 5 -118.40139 14.17 12.39 1.78
Neenach Check 43 Precip   34 47 40 34.794 118 37 15 -118.62083 14.32 15.1 -0.78
Little Rock Crk Above Dam Percip   34 28 41 34.478 118 1 24 -118.02333 15.28 9.16 6.12
Sanberg Airways Station Precip   34 44 47 34.746 118 43 27 -118.72417 15.71 12.85 2.86
Bouquet Cyn @ Urban Precip   34 26 54 34.448 118 30 21 -118.50583 15.79 7.98 7.81
Gorman-Sheriff Precip   34 47 47 34.796 118 51 27 -118.8575 16.18 12.87 3.31
Mint Cyn @ Fitch Precip   34 26 48 34.447 118 25 39 -118.4275 16.26 9.05 7.21
Signal Hill City Hall Precip   33 47 48 33.797 118 10 3 -118.1675 16.38 12.18 4.2
Pnt Vicen Ligh Precip   33 44 30 33.742 118 24 38 -118.41056 16.5 11.07 5.43
El Segondo Yard Precip   33 55 1 33.917 118 23 14 -118.38722 16.57 13.23 3.34
Castaic Junct Precip   34 26 17 34.438 118 36 42 -118.61167 16.89 15.78 1.11
Del Valle Training Center Precip   34 25 46 34.429 118 40 1 -118.66694 17.36 15.67 1.69
Domin Wat Co Precip   33 49 53 33.831 118 13 29 -118.22472 17.76 12.11 5.65
LA Ducommun St Precip   34 3 9 34.053 118 14 12 -118.23667 17.8 15.63 2.17
RollingHills Fire Station Precip   33 45 25 33.757 118 21 16 -118.35444 18.03 16.97 1.06
Imperial Yard South FMD Precip   33 55 49 33.93 118 10 22 -118.17278 18.94 15.34 3.6
Electric Ave Pumping Plnt Precip   33 59 35 33.993 118 28 23 -118.47306 19.19 17.34 1.85
Fire Station 149 Precip   34 29 48 34.497 118 36 47 -118.61306 19.25 16.38 2.87
La Tuna DB Precip   34 14 12 34.237 118 19 36 -118.32667 19.8 16.44 3.36
Agoura Precip   34 8 8 34.136 118 45 7 -118.75194 20.16 17.84 2.32
Northridge- LADPW Precip   34 13 52 34.231 118 32 28 -118.54111 20.93 15.3 5.63
La Habra Hgts Precip   33 56 54 33.948 117 57 51 -117.96417 21.1 15.85 5.25
Eaton Wash Precip   34 4 29 34.075 118 3 17 -118.05472 21.26 12.4 8.86
Eagle Rock Rsvr Precip   34 8 44 34.146 118 11 24 -118.19 22.05 18.14 3.91
San Gab Pow House Precip   34 9 20 34.156 117 54 28 -117.90778 22.28 22.98 -0.7
Tujunga Spreading Ground Precip   34 14 6 34.235 118 24 27 -118.4075 22.45 19.36 3.09
Walnut Crk Precip   34 4 14 34.071 117 52 14 -117.87056 22.95 13.29 9.66
Castic Powerhouse Precip   34 35 17 34.588 118 39 24 -118.65667 22.99 20.05 2.94
Sepulv cyn @ Mulholland Precip   34 7 50 34.131 118 29 25 -118.49028 24.09 21 3.09
Big Rock Mesa Precip   34 2 21 34.039 118 37 0 -118.61667 24.37 16.58 7.79
San Gabriel East Fork Percip   34 14 9 34.236 117 48 18 -117.805 25.55 26.04 -0.49
Chiloa-St Hwy Precip   34 19 4 34.318 118 0 29 -118.00806 25.67 22.9 2.77
Pine Canyon Patrol Station Pcp   34 40 24 34.673 118 25 45 -118.42917 25.87 19.14 6.73
Pacoima Dam Precip   34 19 48 34.33 118 23 58 -118.39944 25.87 19.54 6.33
School House D.B. Precip   34 19 32 34.326 118 27 29 -118.45806 25.98 20.33 5.65
Road Mant. Yard 417 Precip   33 59 42 33.995 117 52 3 -117.8675 27.1 23.41 3.69
Brown's Canyon Precip   34 18 42 34.312 118 36 26 -118.60722 27.95 19.55 8.4
San Dimas Dam Precip   34 9 8 34.152 117 46 17 -117.77139 29.41 22.78 6.63
Lechuza Pat Sta Precip   34 4 37 34.077 118 52 46 -118.87944 29.49 22.4 7.09
Aliso Canyon Precip   34 19 42 34.328 118 33 17 -118.55472 30.24 22.82 7.42
Flintridge Precip   34 10 54 34.182 118 11 8 -118.18556 32.36 22.09 10.27
Cedar Springs Precip   34 21 21 34.356 117 52 34 -117.87611 36.14 29.95 6.19
Santa Anita Dam Precip   34 11 3 34.184 118 1 11 -118.01972 36.54 26.21 10.33
Cogswell Dam Precip   34 14 36 34.243 117 57 48 -117.96333 36.69 34.21 2.48
San Gabriel Dam Precip   34 12 20 34.206 117 51 38 -117.86056 37.6 28.86 8.74
Clear Crk School Precip   34 16 37 34.277 118 10 12 -118.17 40.98 30.54 10.44
Tanbark Precip   34 12 19 34.205 117 45 39 -117.76083 54.33 28.04 26.29




























































































































































































































































































































































La Habra Hgts Precip   33 56 54 33.948 117 57 51 -117.96417 21.1 15.85 5.25
Eaton Wash Precip   34 4 29 34.075 118 3 17 -118.05472 21.26 12.4 8.86
Eagle Rock Rsvr Precip   34 8 44 34.146 118 11 24 -118.19 22.05 18.14 3.91
San Gab Pow House Precip   34 9 20 34.156 117 54 28 -117.90778 22.28 22.98 -0.7
Tujunga Spreading Ground Precip   34 14 6 34.235 118 24 27 -118.4075 22.45 19.36 3.09
Walnut Crk Precip   34 4 14 34.071 117 52 14 -117.87056 22.95 13.29 9.66
Castic Powerhouse Precip   34 35 17 34.588 118 39 24 -118.65667 22.99 20.05 2.94
Sepulv cyn @ Mulholland Precip   34 7 50 34.131 118 29 25 -118.49028 24.09 21 3.09
Big Rock Mesa Precip   34 2 21 34.039 118 37 0 -118.61667 24.37 16.58 7.79
San Gabriel East Fork Percip   34 14 9 34.236 117 48 18 -117.805 25.55 26.04 -0.49
Chiloa-St Hwy Precip   34 19 4 34.318 118 0 29 -118.00806 25.67 22.9 2.77
Pine Canyon Patrol Station Pcp   34 40 24 34.673 118 25 45 -118.42917 25.87 19.14 6.73
Pacoima Dam Precip   34 19 48 34.33 118 23 58 -118.39944 25.87 19.54 6.33
School House D.B. Precip   34 19 32 34.326 118 27 29 -118.45806 25.98 20.33 5.65
Road Mant. Yard 417 Precip   33 59 42 33.995 117 52 3 -117.8675 27.1 23.41 3.69
Brown's Canyon Precip   34 18 42 34.312 118 36 26 -118.60722 27.95 19.55 8.4
San Dimas Dam Precip   34 9 8 34.152 117 46 17 -117.77139 29.41 22.78 6.63
Lechuza Pat Sta Precip   34 4 37 34.077 118 52 46 -118.87944 29.49 22.4 7.09
Aliso Canyon Precip   34 19 42 34.328 118 33 17 -118.55472 30.24 22.82 7.42
Flintridge Precip   34 10 54 34.182 118 11 8 -118.18556 32.36 22.09 10.27
Cedar Springs Precip   34 21 21 34.356 117 52 34 -117.87611 36.14 29.95 6.19
Santa Anita Dam Precip   34 11 3 34.184 118 1 11 -118.01972 36.54 26.21 10.33
Cogswell Dam Precip   34 14 36 34.243 117 57 48 -117.96333 36.69 34.21 2.48
San Gabriel Dam Precip   34 12 20 34.206 117 51 38 -117.86056 37.6 28.86 8.74
Clear Crk School Precip   34 16 37 34.277 118 10 12 -118.17 40.98 30.54 10.44
Tanbark Precip   34 12 19 34.205 117 45 39 -117.76083 54.33 28.04 26.29

Monday, August 29, 2011

Wildfire Risk Analysis


Data:
Specific data was necessary to fully analyze wildfire risk analysis within the Los Angeles Mountains. I first acquired a digital elevation map from the USGS seamless server. This DEM included all of Los Angeles County. Next I utilized a free data portal within the California Forestry and Fire Protection services website to obtain a raster image and data table of vegetation within LA county. Finally, a simple station fire parameter shapefile was needed to specify location and overall area of interest. By reclassifying and combining my two, raster models I compromised a working risk analysis map of the area around the 2009 station fires.
Analysis:
            The first step in my analysis was obtaining and reclassifying terrain slope in respect to fire risk. To do this, I modified my slope map to contain only 5 categories of value ranging from 1-10. Low sloping areas received a low value (less likely to assist wildfire) while increasingly steep slopes received higher values. Overall, fire burns in an upward direction, steep slopes allow wildfires to spread much quicker creating a larger hazard. My next step was vegetation analysis. My approach was very similar to terrain slope. I utilized the reclassification tool to rank vegetation types according to their burning characteristics. To effectively achieve this, I used “WHR10NAME” category within the attribute table. This category specifies overall vegetation type, not just specific species. In my reclassification, shrub or chaparral contains the highest burn index and received a value of 10. Wetland, agriculture, desert and barren areas received low values. Overall this reclassification provided a ramping scale from 1-10 representing the fuel capacity of vegetation subtypes. At this point, my final analysis required me to combine these two reclassifications. Both slope reclass and vege reclass were in raster form. This allowed me to simply sum up new fuel indexes within the raster calculator. The output was a scale ranging from 2-20. The higher the value, the more likely the area will burn out of control. Within my calculations, I chose to weight vegetation and slope exactly the same. The final touch was activating the station fire parameters and utilizing this boundary as a reference location. It is very evident that this area contains very high fire hazard.
           

Monday, August 15, 2011

Quiz 1

Medical marijuana dispensaries have a prominent presence in Los Angeles. The LA city council is now setting a communal regulation on these businesses by   enforcing a 1000 foot barrier of separation between any dispensary in the city of Los Angeles and any “child-friendly” location. Specifically, this includes parks, schools, and public libraries within the city. It is important to also consider and respect the rights of those that utilize medicinal marijuana for pain management, daily wellness, and overall increased standard of living. By analyzing the current distribution of cannabis dispensaries and the location of schools, libraries, and parks, I have determined that this ordinance can be implemented without eradicating “holistic medicine”.
To begin my analysis, I chose to visually display the specific street addresses of 42 cannabis dispensaries within the city of Los Angeles. This allows me to not only view the relative distribution, but perform the appropriate analysis required to make an educated decision. By placing 1000 foot buffers around every dispensary and uploading spatial data for schools, parks, and libraries, I can identify the exact businesses that would be in violation of the new city ordinance. Of the original 42 locations, only 5 dispensaries breach the 1000 foot “barrier rule”. Although implementation of the new ordinance would close these 5 locations, the majority would remain valid and legal.
Does it make sense to implement this “barrier rule” in the city of Los Angeles? With the new ordinance, families can afford to be less concerned about the placement of dispensaries, enforcement would be cheap and easy, and medical marijuana patients would still have more than enough options. Economically speaking, medical marijuana within Los Angeles is a taxable good. Cannabis clubs are therefore a valid contributor to the strength of Southern California’s economy and GDP. Because of this, it is important to respect these establishments as professional businesses. The City’s ordinance is therefore beneficial because it wouldn’t eradicate this entire sector. Ideally the community will respect these retailers even more because they are abiding by “family friendly” law.
Allow I firmly believe that implementing the medical marijuana ordinance is a positive decision and a step forward, it is still important to consider the drawbacks of this choice. Business will be shut down meaning individuals will lose jobs. Although enforcement will be fairly cheap, it requires a special police force unit to ensure that proper action is being followed. Most importantly, any major controversial decision atomically generates a societal dichotomy. Overall, I feel these drawbacks are outweighed by the benefits of the city ordinance and “buffer rule”.

References:
UCLA Map Share
Enterprise GIS
California NORML: Medical Marijuana Collective Index

Geocoding Time



Jaryd Block
703660706
8-15-11
Geocoding: Applied to Communal Agriculture
The greater metropolis of Los Angeles can be a difficult location to partake in personal gardening.  Due to steep real estate prices, obtaining enough land to farm fresh produce requires a notable amount of capital. Because of this I choose to explore the spatial distribution of community gardens within this county. These individual sites are completely maintained, farmed, and managed by the public. More importantly, individuals from just about any income background can easily participate.
By compiling and tabulating address data, geocoding allows me to easily transform an excel spreadsheet into visual information. By spatially plotting over 50 different community garden locations, relative distribution is easily established. My map also contains my current address so I can easily determine the most accessible gardens. Astonishingly, many of these communal sites are located within low-income neighborhoods providing a constructive space for rough communities. As a newer member of the LA area, a visual display of address data is much more useful than the simple tabulated format. Geocoding extended my personal knowledge of community garden locations within the county.

NAME ADDRESS ZIP TYPE
Tarzana Community 18702 Erwin St 91335 Community Garden
New City Farms 225 E 15th St 90813 Community Garden
Monterey Eco 870 Monterey Rd 91206 Community Garden
Wrigley Village 2044 Pacific Ave 90806 Community Garden
Raymond Ave 2632 Raymond Ave 90007 Community Garden
Arleta 8800 Canterbury Ave 91331 Community Garden
Bougainvillia E 103rd St & Grape St 90002 Community Garden
Bell Gardens 7800 Scout Ave 90201 Community Garden
Baldwin Park 13067 Bess Ave 91706 Community Garden
Central Hollywood 1259 N Mansfield Ave 90038 Community Garden
Columbia Park 4045 W 190th St 90504 Community Garden
Crenshaw 1423 Crenshaw Blvd 90019 Community Garden
Culver City 10860 Culver Blvd 90230 Community Garden
Dan McKenzie 4324 160th St 90260 Community Garden
Eagle Rockdale 1003 Rockdale Ave 90041 Community Garden
El Sereno 5466 Huntington Dr N 90032 Community Garden
Enrique Noguera 6614 Fountain Ave 90038 Community Garden
Francis Ave 2909 Francis Ave 90005 Community Garden
Salad Bowl Garden 16003 Rinaldi St 91344 Community Garden
Greater Watts 600 E 118th Pl 90059 Community Garden
Howard Finn 7747 Foothill Blvd 91042 Community Garden
Hudson 2335 Webster Ave 90810 Community Garden
Jardin Del Rio 2363 Riverdale Ave 90031 Community Garden
Gibson's Community 1401 S Harbor Blvd 90731 Community Garden
Lakewood 5200 Carfax Ave 90713 Community Garden
La Mirada 13518 Biola Ave 90638 Community Garden
Loma Alta 3330 Lincoln Ave 91001 Community Garden
Long Beach 7600 E Spring St 90815 Community Garden
Manzanita St 1101 Manzanita St 90029 Community Garden
Mar Vista 5075 S Slauson Ave 90230 Community Garden
North Hollywood 11800 Weddington St 91607 Community Garden
North Long Beach 6895 N Myrtle Ave 90805 Community Garden
Norwalk 12739 Studebaker Rd 90650 Community Garden
Norwich 417 Norwich Dr 90048 Community Garden
Ocean View Farms 3300 S Centinela Ave 90066 Community Garden
Orcutt Ranch 23600 Roscoe Blvd 91304 Community Garden
Paramount 7200 Cortland Ave 90723 Community Garden
Parkman 20800 Burbank Blvd 91367 Community Garden
Pico Rivera 8606 Beverly Rd 90660 Community Garden
Pomona 1120 W Fremont St 91766 Community Garden
Proyecto Jardin 1718 Bridge St 90033 Community Garden
Rosecrans Farms 561 W 146th St 90248 Community Garden
Rosewood 4160 Rosewood Ave 90004 Community Garden
San Pedro 1400 S Gaffey St 90731 Community Garden
Santa Fe Springs 10145 Pioneer Blvd 90670 Community Garden
Santa Monica 2300 Main St 90405 Community Garden
Santa Monica 1400 Park Dr 90404 Community Garden
Santa Monica 1525 Euclid St 90404 Community Garden
Sepulveda Center 16633 Magnolia Blvd 91316 Community Garden
Solano Canyon 545 Solano Ave 90012 Community Garden
Stanford Avalon 658 E 111th Pl 90059 Community Garden
Summit Ave 1282 N Summit Ave 91103 Community Garden
Union Ave 1136 S Union Ave 90015 Community Garden
Urban Oasis Foods 5010 11th Ave 90043 Community Garden
Van Nuys 16400 Chase St 91343 Community Garden
Vermont Square 4712 S Vermont Ave 90037 Community Garden
Wattles Farm 1714 N Curson Ave 90046 Community Garden
Winston Smoyer 1006 Clay Ct 91801 Community Garden
Yamazaki Memorial 961 S Mariposa Ave 90006 Community Garden
Project Youth 12467 Osborne St 91331 Community Garden
Venice Community 643 Milfred Ave 90291 Community Garden


Note: this document contains 62 addresses