Methods *Suitability I: The first method we used was creating a suitability analysis with an opportunities and a constraints layer. The constraints layer included combining the prevalence of (1) high injury corridors, (2) stationary pollution and a (3) brownfield sites. The opportunities layer included combining the areas with (1) high housing unit density, (2) SFMTA bike routes, and (3) health facilities. *Network Analysis: We used the network analysis from Lab 10 to determine the service area for farmer’s markets, with intervals of 1, 2, 3, 4, and 5 minutes to determine travel time. We exported areas within a 3 minute travel time from farmer’s markets. Nina Gustis, Sarah Strochak, and Tara Singh Aim We have based our analysis on having a location with these criteria: *Close proximity to services and infrastructure (e.g police stations, healthcare, and bike lanes) *Avoiding building near high injury corridors, stationary pollution, brownfields. *Adequate sunlight exposure for solar panels, green roofs and natural lighting. *Accessible to commercial spaces (e.g farmers market) Introduction Our client TMG Partners, a housing development firm in the bay area, has asked us to determine the location in San Francisco that is best suited to build Net Zero Energy housing units. These housing units produce as much energy as the total amount of energy used by the building on an annual basis, which is roughly equal to the amount of renewable energy created on site. This principle is viewed as a means to reduce carbon emissions and dependence on fossil fuels. Zero net energy housing developments require the use of solar panels, green roofs, natural ventilation, among other assets. In order to fulfill these requirements the housing developments must be placed in areas that are pollution free, highly exposed to sunlight, and in desirable parts of the city. Locating Net Zero Energy Housing in San Francisco, CA Methods Cont. *Aspect Map: The second method we used was creating a surface aspect map to convey south facing slopes in San Francisco. An area with south facing slope is optimal for our building, as we would like to install solar panels, which produce maximum energy at a south facing aspect. We downloaded the 1/3 arc NED data and used the spatial analyst tool to create 20m contour lines. With the contour lines we used the 3D analyst tools to generate a TIN and altered the symbology to display face slope with a graduated color ramp. We then dissolved the TIN into polygons and joined the aspect map with the Aspect Code chart provided in lab 9 to display the direction of each polygon. The polygons corresponding to south facing aspect are the best location for a Net Zero Energy building. Results *Suitability II: We combined the suitability analysis map and the network analysis of areas within three minutes from a farmers market to find a place that would be optimal for building Net Zero Energy housing units. We converted the network analysis areas of three minutes or less to a shape file and added it to our original opportunity map. We then combined our new opportunity map with our original constraints map to create a final suitability map. With the new suitability map, we selected by attribute where the suitability weight was greater than or equal to three. We then exported these select areas and laid them on a base map to convey the optimal locations to build Net Zero Energy housing based on these constraints. Conclusion After conducting a suitability and network analysis and comparing these results with an analysis of surface aspect in San Francisco, we were able to find locations that are both desirable to live in and provide the best chances of achieving Zero-Net Energy in the long run. We have identified several addresses that we believe are fit for this type of facility and have chosen an area on Clayton St. and Page St.. This area was deemed suitable for our Net Zero Energy housing due to its proximity to an array of services, such as health facilities, commercial areas, transportation hubs and farmers markets. Layers Opportunities Census (hse_units) areas with greater than or equal to 198.000001 housing units per census block SFMTA Bike Route Network 100’ buffer from existing routes Health Facilities 1000’ buffer from existing facilities Layers Constraints High Injury Corridors 500’ buffer around high injury corridors Stationary Pollution 800’ buffer around areas of stationary pollution Brownfields 500’ buffer around brownfields Flow Chart Areas highlighted in red are optimum locations with suitability greater than or equal to three. Acknowledgements datasf.org National Map Viewer LD Arch C188 Lab 3 and Lab 10 Data, John Radke NAD1983 State Plane California III Projection: Lambert Conformal Conic Units: Feet
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Methods *Suitability I: The first method we used was creating a suitability analysis with an opportunities and a constraints layer. The constraints layer included combining the prevalence of (1) high injury corridors, (2) stationary pollution and a (3) brownfield sites.
The opportunities layer included combining the areas with (1) high housing unit density, (2) SFMTA bike routes, and (3) health facilities.
*Network Analysis: We used the network analysis from Lab 10 to determine the service area for farmer’s markets, with intervals of 1, 2, 3, 4, and 5 minutes to determine travel time. We exported areas within a 3 minute travel time from farmer’s markets.
Nina Gustis, Sarah Strochak, and Tara Singh
Aim We have based our analysis on having a location with these criteria:
*Close proximity to services and infrastructure (e.g police stations, healthcare, and bike lanes)
*Avoiding building near high injury corridors, stationary pollution, brownfields.
*Adequate sunlight exposure for solar panels, green roofs and natural lighting.
*Accessible to commercial spaces (e.g farmers market)
Introduction Our client TMG Partners, a housing development firm in the bay area, has asked us to determine the location in San Francisco that is best suited to build Net Zero Energy housing units.
These housing units produce as much energy as the total amount of energy used by the building on an annual basis, which is roughly equal to the amount of renewable energy created on site. This principle is viewed as a means to reduce carbon emissions and dependence on fossil fuels.
Zero net energy housing developments require the use of solar panels, green roofs, natural ventilation, among other assets. In order to fulfill these requirements the housing developments must be placed in areas that are pollution free, highly exposed to sunlight, and in desirable parts of the city.
Locating Net Zero Energy Housing in San Francisco, CA
Methods Cont. *Aspect Map: The second method we used was creating a surface aspect map to convey south facing slopes in San Francisco. An area with south facing slope is optimal for our building, as we would like to install solar panels, which produce maximum energy at a south facing aspect. We downloaded the 1/3 arc NED data and used the spatial analyst tool to create 20m contour lines. With the contour lines we used the 3D analyst tools to generate a TIN and altered the symbology to display face slope with a graduated color ramp. We then dissolved the TIN into polygons and joined the aspect map with the Aspect Code chart provided in lab 9 to display the direction of each polygon. The polygons corresponding to south facing aspect are the best location for a Net Zero Energy building.
Results*Suitability II: We combined the suitability analysis map and the network analysis of areas within three minutes from a farmers market to find a place that would be optimal for building Net Zero Energy housing units. We converted the network analysis areas of three minutes or less to a shape file and added it to our original opportunity map. We then combined our new opportunity map with our original constraints map to create a final suitability map. With the new suitability map, we selected by attribute where the suitability weight was greater than or equal to three. We then exported these select areas and laid them on a base map to convey the optimal locations to build Net Zero Energy housing based on these constraints.
ConclusionAfter conducting a suitability and network analysis and comparing these results with an analysis of surface aspect in San Francisco, we were able to find locations that are both desirable to live in and provide the best chances of achieving Zero-Net Energy in the long run. We have identified several addresses that we believe are fit for this type of facility and have chosen an area on Clayton St. and Page St.. This area was deemed suitable for our Net Zero Energy housing due to its proximity to an array of services, such as health facilities, commercial areas, transportation hubs and farmers markets.
Layers Opportunities
Census (hse_units) areas with greater than or equal to 198.000001 housing units per census block
SFMTA Bike Route Network 100’ buffer from existing routes
Health Facilities 1000’ buffer from existing facilities
Layers Constraints
High Injury Corridors 500’ buffer around high injury corridors
Stationary Pollution 800’ buffer around areas of stationary pollution
Brownfields 500’ buffer around brownfields
Flow Chart
Areas highlighted in red are optimum
locations with suitability greater than
or equal to three.
Acknowledgementsdatasf.org
National Map Viewer
LD Arch C188 Lab 3 and Lab 10 Data, John Radke
NAD1983 State Plane California III Projection: Lambert Conformal Conic Units: Feet
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