Maps of the Month

November 2017:
2017 North Bay Wildfire Affected Areas

Posted by Michael Ziyambi | MTC GIS

On Sunday October 8th, the first of four devastating wildfires ignited in Napa County near tiny Tubbs Lane just north of Calistoga fueled by parched grass and tinder-dry trees. That same evening, dry, hurricane speed winds swept through the area with gusts over 80 miles per hour at times. Within the first five hours of ignition, the Tubbs fire would be just one of four blazes burning across the Northern Bay area.In total, the North Bay wildfires claimed the lives of 43 people, burning over 161,000 acres across Sonoma and Napa Counties. Some 8,200 structures were either damaged or completely destroyed in the North Bay, the majority of which were located within Sonoma County. Santa Rosa lost about 3,110 structuresto the Tubbs Fire. The map in Attachment 2 charts the damage from these fires in graphic form.


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October 2017:
Bay Area 30 Year Earthquake Risk Projection

Posted by Michael Ziyambi | MTC GIS

On Monday, October 9th, 2017 the Bay Area experienced a 4.1 earthquake about 12 miles east of San Jose near the Calaveras Fault. The small jolt-and-roll caused no damage but served as a not-so-gentle reminder that the Bay Area rests upon a number of restless faults. According to the United States Geological Survey (USGS), there is a 72% likelihood that the Bay Area will experience a magnitude 6.7 or greater earthquake over the next 30 years. For context, the 1989 Loma Prieta earthquake, with a magnitude of 6.9, was responsible for 63 deaths and severely damaged elevated transportation infrastructure in San Francisco and Oakland. The fault with the greatest likelihood of earthquake is the Hayward-Rodgers Creek Fault, which runs roughly from East San Jose in Santa Clara County to Santa Rosa in Sonoma County. When considering earthquakes, it is important to remember that the magnitude scale is logarithmic, meaning a 7.0 earthquake would release 33 times more energy than that of the 6.0 South Napa Earthquake which occurred in 2014.


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September 2017:
Population Growth Variation Across U.S.Counties

Posted by Michael Ziyambi | MTC GIS

Since the turn of the century, the U.S. has undergone a redistribution of population from rural and rust belt counties to urban counties; particularly those along the Northern and Southern Atlantic Seaboard, the Pacific Coast and parts of the Southwest region. 41 percent or 1,295 counties had population declines from 2000 –2016, with 15 counties experiencing declines of more than 25,000 people or 2.4 percent of the total population.Over the same period, total population for the nation grew by 42 million, 8 percent of which has migrated from declining rural and rust belt counties to growing urban counties along the East and West Coast, and in the Southwest, resulting in a 23 percent increase in population occurring in 60 percent of U.S. counties.The data indicates that the majority of this growth is occurring in just 12 percent of counties, including the Bay Area which has experienced a 2.4 percent increase in population.


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July 2017:
US Counties Where Adults Discuss Global Warming at Least Occasionally

Posted by Michael Ziyambi | MTC GIS

This month’s map is based on a recently published New York Times article, which found that on average, only 33% of Americans discuss global warming at least occasionally with friends and family and up to 31% never do. As shown in the map, there are distinct regional patterns. Americans in the Western US, many of whom have been affected by drought and wildfire, are more likely to iscuss climate change.


Similarly, Americans residing in the New England states talk more about climate as well as those living in coastal South Carolina – states battered by many hurricanes over the years. Aside from Southeast Florida however, the rest of the Atlantic Coast is less likely to engage in climate discussions despite recent increases in global warming related flooding. As the article highlights, global warming is precisely the kind of threat humans are awful at dealing with due to its slow- moving and abstract nature.


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June 2017:
When Bay Area Cities Will Reach Plan Bay Area 2040 Housing Targets

Posted by Michael Ziyambi | MTC GIS

On May 1, the California Department of Finance (DOF) released population estimates updated through the end of year 2016, which include detailed data on housing production for the San Francisco Bay Area. While a single year is just one data point and may not necessarily be indicative of long-term trends, this dataset is still useful to understand how the robust regional economy is affecting housing production trends in recent months. The June 2017 map of the month highlights how 2016 housing production compares to the annualized housing forecast from the Draft Plan Bay Area 2040 by identifying how many years it will take cities, at the current rate, to reach the year 2040 forecast.While some cities are on pace or even ahead of schedule to meet the forecast, numerous jurisdictions are way behind – many by centuries


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May 2017:
Northern California Mega Region Employment Density

Posted by Michael Ziyambi | MTC GIS

This month’s Map of the Month highlights employment centers within the 21 - County Northern California Mega Region. Employment Centers were defined within built-up areas where there was an employment density of over 5,000 jobs per square mile. Jobs within the Mega Region were broken down by North American Industry Classification System (NAICS) sectors, and were grouped into 7 related classifications.


The Mega Region had over 5.7 million jobs in 2016. The largest shares of jobs are in the Information, Professional Services, Management & Administration sectors (over 1.2 million). The top five employment centers within the Mega Region are in Santa Clara (988,657), Alameda (762,089), San Francisco (642,093), Sacramento (617,947), and Contra Costa Counties (408,669).


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April 2017:
Bay Area Sweet Spots

Posted by Michael Ziyambi | MTC GIS

A recent New York Times article examines one of the biggest drivers in a families’ decision to leave cities: school. Using data from a number of sources, the New York Times developed charts that look at school performance and median home sale price per square foot for 5 metro areas including New York/New Jersey, San Francisco, Boston, Chicago, and Minneapolis. For mostof the cities studied, home prices rise with the quality of the school district but a number of districts break this pattern.


The map shown highlights 5 Bay Area schools that perform above average, and have below average housing costs.The New York Times used median price per square foot to measure housing costs and the median number of grades ahead (or behind) for school district quality. In the Bay Area, the price per square foot was around $500 while students were .28 grades ahead of their grade placement. The 5 Bay Area schools that were selected had the lowest home prices and the best performing schools in the region. Source: New York Times .


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March 2017:
Traffic Incident Management Along Bay Area Freeways

Posted by Michael Ziyambi | MTC GIS

In December 2016, Waze and MTC entered into a data sharing agreement that provides the Freeway Service Patrol (FSP) program with real-time information that will help FSP tow drivers quickly detect incidents. Waze, in turn, will receive the FSP’s highway incident information – including crashes and stalls – to share with its users. Together, both will have more data and be better able to provide timely assistance to Bay Area drivers. The map in Attachment 4 represents a snapshot of the incidents reported by FSP and Waze users during peak congestion hours in the nine-county Bay Area. Traffic incidents and assists from the FSP program are shown in blue, while reported incidents from Waze are shown in yellow. Incidents have been aggregated along major corridors throughout the region in an attempt to compare the number of incidents reported by Waze users with the total number of assists handled by the FSP program. Alameda County (32%) has the largest share of the total number of incidents during the peak congestion hours, followed by Santa Clara (26%), Contra Costa (15%) and San Mateo (12%).


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February 2017:
Most Congested Urban Areas in the US

Posted by Michael Ziyambi | MTC GIS

This month’s map highlights a recent study released by Inrix Inc. that examines traffic congestion in over 1,000 cities – 240 in the U.S. – across 38 countries. The study reveals that the U.S. is ranked as the most congested developed country in the world, with drivers spending an average of 42 hours a year in traffic during peak hours. According to this study, the direct and indirect costs of congestion to all U.S. drivers amounts to nearly $300 billion in 2016, an average of $1,400 per driver.


U.S. cities dominated the top 10 most congested cities globally, with Los Angeles (first), New York (third), San Francisco (fourth), Atlanta (eighth) and Miami (10th) each dealing with an economic drain on the city upwards of $2.5 billion caused by traffic congestion. Los Angeles commuters spent an average of 104 hours last year in traffic jams during peak congestion hours more than any other city in the world. This contributed to congestion costing drivers in Los Angeles $2,408 each and the city as a whole $9.6 billion from direct and indirect costs. Direct costs relate to the value of fuel and time wasted, and indirect costs refer to freight and business fees from company vehicles idling in traffic, which are passed on to households through higher prices.

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January 2017:
How Large are Incomes in Each US County Compared to the Value of the Homes?

Posted by Michael Ziyambi | MTC GIS

This map shows median home values plotted against median household income in an effort to illustrate where the least affordable housing in the U.S. is located. The Bay Area, not surprisingly, has some of the least affordable housing in the country – both in absolute terms, and in terms relative to income. In San Francisco proper, the median home value is $800,000 with a median income of $81,000, giving a price-to-income ratio of nearly 10 to 1. In Marin County, the median home value is $815,000 with a median income of $93,000. This ratio is 8.8 times the median income of the county. In Silicon Valley, housing is still pricey, but many people are able to make up for it with higher incomes: San Mateo County has a ratio of 8.3, and Santa Clara County has a ratio of 7.3.

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