the key the data to the supervisors. it is our understanding that the data gets shared with the hsa as the contract holder, and they make changes as they see fit with the data we have provided. this did care component is the component -- the standard of care component is the component overseen, by the department of public health. the data in the standard of care reports themselves provide a system overview and system recommendations, so it would fall within hsa. supervisor campos: 90. supervisor mar: -- thank you. supervisor mar: thank you for the quarterly report, but it is becoming clear there is no accountability in how the system works. if i were sitting here, i would be fuming about this. i'm starting to understand why they called it the runaround. i guess i just have a basic question, more from a human level -- how far have become towards a more people-centered process that supervisors amiano, campos, and others have been advocating for along with myself >> that is a challenging question to answer. you have to look

but pay extra to have the group send their data? >> if you look at the totality of what you use, it is not welcomed -- [unintelligible] there is a lot of things it is used for. that is the reason. >> you know and it is interesting. i have never focused before. this drill pipe pressure i have been focusing on came from transocean. your mud loggerhead the zeros and ones that make up or is it an analog system? >> several of these sensors are transocean. the question we have had is which ones and we have never received the map that says which particular sensors on these charts were being measured from transocean sensors. it is something we have not had. the sensors -- it is standpipe pressure from transocean. one of the things when we talked with nov regarding animating some of this, they said our system and this is one of the big differences monitors and records things. this is every five seconds. there is a 20 times difference in the sample rate. that is one of the reasons why it is different data in that regard. we still do not unders

the data are useful. i'm sure you know, sir, the submarine missions we had run in the cold war and 1990s, they provide valuable observations of how thick is that ice so we can calibrate or basically tune our sat title. s. i'd -- satellites. the department of defense is a big user of the civil structure that, in part, is appropriated from your committee. we work very closely with noaa. i have a great relationship with the doctor, and one of the things on the practical adaptation side we are jointly looking at between the navy, noaa and the air force, also have department of energy and nasa involved, is how do we look at a next generation of weather, ocean, ice coupled prediction model so that by roughly 2020, in about ten years from now, we can predict that system as a couple -- as a whole and really going spanning between weather time frames, say hours to days, out to, say, roughly about two or three decades. because as we're planning for our infrastructure or let's say if you're the port of new york and

and the data the driller was looking at was the t.o. data. and maybe the t.o. data was a lot clearer than it. maybe it was not as clear. maybe there was digital information that was easier to pick up. we would like to know that to the commission, please, but we don't. i would expect, i guess, that the t.o. screen, just common sense, would be better than sperry sun because it is their rig and investment,and their money but that is sheer guesswork. but that is a critical thing that would be good to know. now, we know there was a kick then. we know hydrocarbons were coming through the cement job that had not been remediated. could have been if people decided to but wasn't. we know there was a leak. remember, i told you earlier that we would discuss where the leak occurred. did it come up this annulus or up the chute? let's go to the next one. this is a casing hanger seal. we have talked to drill quip. they have some of the best engineers i have ever met and they say there is no damage to the casing hanger seals at all. they are pristine. if the leak came up the an

none of the data is stored on the meter or by the mta. it is handled by the meter company and processed as quickly as possible. there's very little risk of using credit cards at the meters. >> [unintelligible] concerned about the possibility of [inaudible] [traffic] >> one of the great things about the meters is that it makes it so easy to pay. people should get a lot fewer parking tickets and area. that can leave people with a bitter taste in your mouth. that is one part. the demand response of pricing , the mta is legally obligated to charge lowest pricing available to create a target. it will be easy for customers to come and find a space quickly. my name is jay primus. i am the program manager for sf park. thank y>> thank you very much. why don't you do a demonstration for them? >> meter has some time on it. you concede the flashing -- you can see the flashing led. now it is cancelled. it is going through the verification process. it starts with a minimum of 30 minutes. you can add or subtract time. [traffic] when you are ready, you c

yet it is to stabilize it, use the data we are collecting -- the idea is to stabilize it and use the data we're collecting. any changes will occur every four weeks in terms of rate changes. >> the mos expensive hourly parking right now is $3.50. you anticipate some parking up to $6. when would that happen? >> the pilot study is going to be over the next two years. it will be some time before we get to those numbers. the most important thing is that we will have the data to make better decisions in terms of the actual utilization. that is the most important part of a pilot. what is the actual utilization? how long do people need to be in a parking spot? in some cases, we expanded the limitations. we may move to four our parking in some areas. this is a major milestone. it is one of many steps towards a new parking policy in the city. it will take about two years to do that. >> are there censors around here? >> there is one right there. there is one right behind you over there, young lady. the sensors will provide information when someone is parking and pulling out of the parking spot.

even the crime statistics and data sets are really useful. another one that i found on the web site, the -- on the web site -- on the website was teh sf trees application. a lot of useful applications, especially the one about not getting private tickets. a useful views of data so that it helps people. thank you very much for the help on this. are there any questions, colleagues? supervisor maxwell. >> [no audio] >> i think it can be in that often, departments get requested the same information or data sets, so instead of individually responding, they can be proactive and put this on datasf so that everyone has access to the information. i can give you an example. the people who make maps in the city often get requests for maps of the neighborhood in the city.

the key point. one of those is looking at data just beyond the assignment data and looking at the census data. when looking at capacity for attendance area and in drawing the attendance area, we are not able to draw areas that will accommodate all of the students that live there. we know that there will be attendance areas that can't accommodate all of the applicants. we want to see if they are applying to the school and other information about the attendance area. a number of commissioners suggested some of the things that we collect data on including start times, after school, and having a deeper understanding of what kind of surveys would look like. we can share them with the board and make sure that you are aware of what we have been collecting. we look to include information about pre-k. and also, a number of commissioners suggested that we think about having certain data available for the first annual report so we can determine if there is anything that needs to be changed for the second year and looking at attendance areas and other factors to see if adjustments should be made. commissio

it is not the department heads, not the city's data, it is the public's data. we have an obligation, i would say, to push it out to the public to make it available and useful to the public. when you do that, it can help the department and city, but most importantly, it can help the public. it can be very empowering. a lot of what we do, whether it is abating graffiti, picking up trash, filling pot holes, improves someone's quality of life. when a neighborhood group is getting together to see how they can improve the conditions in their neighborhood, it is hard for them to do so if they do not know what the crime looks like in the neighborhood, where they have the legal dumping problems. community groups can then have real data to base their solutions on. it is very empowering in that respect. it can help us avoid a lot of cumbersome public record requests, rather than waiting for people to come to us to pull data out, we push it to them. they do not have to come and ask us for it, which for us to track it down. it is available for them to use. and finally, the m

>> the data is -- first, i do not encrypt it, because i did not save it. i believe it has to have a feature to save it because of the ability to track down criminal activity. it will be included using 128- bit encryption. there is no way for someone to access the data -- there is no way to access it. the only way to access it is through the on-line service, which you have to log in. you have to have a password. >> thank you. >> are you using this? >> i only have one of these now common so i am developing it. -- one of these, so i am developing it. my fear is that customers would complain we would have people checking of the door. we would have people say, are you saving that data? we said we were not, and they said that was ok. they want to see what data is on there. because it is a consumer device, i can put a lot of data on theire. that way they can make sure someone has not taken someone else's id and replaced the id with someone else. i've think it is important to try to foil the people who are trying to get around this. >> that is my next question. the

you see up going into people's behavior. >> i don't know what the data looks like in the united statesbut paul bowles from transport canada, he and i a data last year in terms of the baby boomers. and we looked at the female and male crash rates. and historically in terms of the older driver population that male crash rates are higher than female crash rates. and what we found in or transfer data that the baby boomer females are looking more like the male baby boomers, both in terms of amount driven, but also in terms of crashes. based on those projections, we can expect our cohort, the baby boomer cohort, the females to look more like the males. in the second consideration is while there is a stigma of the baby boomer population that is going to be healthier than their parents. there also are going to be segments of the baby boomer population that are in poorer health. if you look right now and -- i'm sure the statistics in the united states are similar to the canada, diabetes is up epidemic proportions, cardiovascular disease fat epidemic proportions. right on the united states, one

the talk lines at 5 m, but i didn't get them until 7, glen. it's an astonishing set of data. it shows that 32% of the voters identified as the christian conservative movement. 32% of the electorats identified with the christian movement and 25% consistented of white evangelical christians. both are the largest numbers every recorded in a modern midterm election meaning since that kind of data was kept with the rise of the so-called religious right in the 1970s. that, by the way, is an increase of 7 points over the 2006 baseline which was 25%. they voted 78 to 28 republican. by the way, broke more heavily republican than this same constituency did in 1994 when i conducted a similar survey when i was with the christian coalition. another interesting thing that i think glen will go into in greater detail, but i think it's one of the great untold stories of the 2010 elections which is that not only are the tea party and the evangelical movement not at odds and not only are they not distipght and separate movements, but as the data makes clear and there's other data to support th

the program. we matched our data with the credit bureau data. it is a roll over regression, tons of controls but it is the action -- interaction of countrywide. this measure is whether there was [inaudible] these are quite comfortable. this is the interest rate over time in these two groups. the blue line is countrywide and the other is the control group. they have a comparable credit score. this is the graph that shows the role of the rate of countrywide relative to the control group are the countrywide is the blue line and the red line is the control group. they are increasing the defaults for countrywide after the program was announced. part of this increase could be the acceleration of the individual group of countrywide are worse. the settlements were going in the 11 most popular states prior to the announcement of the program. part of this increase could be the effect of the settlement if you are worried whether we are identifying the effect of the program, for the two groups of borrowers who are the most interested, there would not be any

we, the constituents, want empirical data. in the southeast sector, in the district we know this ois one of the worst areas for liquid factions that are all connected. hopefully, the department of health and whoever runs it in the future, is to give us the constituents of san francisco this data. so we can address our children and elders, our vulnerable population. the department of health has not been doing this recently. not in the last 5 years. thank you. >> any other speakers? >> good morning. i am douglas and i have lived in san francisco 58 years. i would like to support this item. it would benefit whoever will effect. since we are talking about environmental health assessment. i would liek tke to thank "the examiner" to talk about the shortest of restuarant inspectors. food is such an important element in the life of san francisco. i thought it was unbelievable we were short on them. >> any other members of the public wishing to speak? public comment is closed. this is a grant for $106,526, and 80% is going to travel exp

this is the sperry-sun data. and the information as to what was happening with the cement job failed at the bottom of the well, create a kick is here. let's zero in a little bit. this is the information. and what we do here is to move it sideways, because every time you bp displays this information, they take it, sideways, and expanded. that's because it's easier to see that way, and that's okay. so here's drill pipe pressure. here's a pressure. here's the sperry-sun information. this is a little complicated. we don't know that anybody in the world saw this specific information that night. it was available in the driller's shack. it was available in the halliburton mud loggers shack which i showed you. it was available back short side in houston, but we don't know that anybody was looking at it. because the driller remembered, had this information available, but would have been looking at the t.o. screen, not this. so it's a little, it's difficult for us to surmise what this information shows when we don't know an

of data into the future. they wanted to talk about everything from discipline, where they lived, any and all data we collected about students, teachers, and principals and staff. there was also concerned about liability for the intentional or negligent release of personal data. we are confident that with this team of experts to think through monitoring student assignment, we have the capacity to monitor closely and be very transparent about the outcome and share that information publicly. we are also confident that we can develop the software and also the key features that they talked about. they talked about simplicity -- we should not consider rank. where students listed the choice on application forms should not impact -- everyone would get tentatively assigned based on whether they lived in the attendance area. they could get tentatively assigned to more than one school based on those tiebreakers and we would go into a cycle of swapping. why not let somebody swap with somebody else and get a higher rank ch

the data -- maybe the data was a lot clearer than this. maybe there was digital information that was easier to pick up. i would expect that the screen would be better because it is there rig, but i don't know that. it is a critical thing that would be good to know. one of the issues -- we know there was a kick then and hydrocarbons were coming through that had not been abbreviated we know there was a leak. remember i told you earlier we would discuss where the leak occurred. did it come up? let's go to the next one. this is a hangar seal. they made this and these guys have some of the best engineers i have ever seen. they say there is no damage to the casing hangar seals at all. if they came up there would be some damage to the seal. remember i told you you would be blown away by the power of this gas under pressure coming up through the riser? this is the way the inside of the casing looks. this stuff is so strong on the inside they have been totally blown away by the force of gas coming up. amazing the force of this. but the inside is to

the bank balance sheet. so i data set contains more than 700,000 mortgages, originated between january '04 and figure it '08 by a top national mortgage bank. and this medication is the distribution of these loans which are distributed roughly proportional to population distribution. we see a slight overrepresentation in southern california where the lender is, or was based. [laughter] >> okay. so to provide you with a lot of context, this chart compares the sample data from our particular mortgage lender to data on loans in the market overall during the same sample. our sample is pretty similar to the market overall for key economic indicators. we have higher proportion to low doc loans because this particular event was a low doc shop. so here we illustrate just conceptually what we demonstrate in the paper. so suppose there's just some state variable that affects the probability of loan sale but does not affect delinquency. so the absence of moral hazard we should find that those with both high and low probabilities

you see up going into people's behavior. >> i don't know what the data looks like in the united statesbut paul bowles from transport canada, he and i a data last year in terms of the baby boomers. and we looked at the female and male crash rates. and historically in terms of the older driver population that male crash rates are higher than female crash rates. and what we found in or transfer data that the baby boomer females are looking more like the male baby boomers, both in terms of amount driven, but also in terms of crashes. based on those projections, we can expect our cohort, the baby boomer cohort, the females to look more like the males. in the second consideration is while there is a stigma of the baby boomer population that is going to be healthier than their parents. there also are going to be segments of the baby boomer population that are in poorer health. if you look right now and -- i'm sure the statistics in the united states are similar to the canada, diabetes is up epidemic proportions, cardiovascular disease fat epidemic proportions. right on the united states, one

you see up going into people's behavior. >> i don't know what the data looks like in the united statest paul bowles from transport canada, he and i a data last year in terms of the baby boomers. and we looked at the female and male crash rates. and historically in terms of the older driver population that male crash rates are higher than female crash rates. and what we found in or transfer data that the baby boomer females are looking more like the male baby boomers, both in terms of amount driven, but also in terms of crashes. based on those projections, we can expect our cohort, the baby boomer cohort, the females to look more like the males. in the second consideration is while there is a stigma of the baby boomer population that is going to be healthier than their parents. there also are going to be segments of the baby boomer population that are in poorer health. if you look right now and -- i'm sure the statistics in the united states are similar to the canada, diabetes is up epidemic proportions, cardiovascular disease fat epidemic proportions. right on the united states, one in

the data cmes rom the rmed corporion to around the year 2000, to2001. you draw astraight line here, the aight red line is basically the lood ri, assume this record is statna th the clime is nochangg. ac stially, ifo ┌"at thisecord,it'nt a coofwhy d will l bien so watasbde? t nk wollot. a otfthhki anda lt of things hve dne in modeling and planning but n terms of on ground action, not much is done. i know city of safrancisco is re-eluating storm water syemredesigning and tentially rebuilding and their begning to look at sa level rise ad what it mans r inflow al ructes and rter ipn tsaat at ms d whes al de orotemai o been but its te ighttbe en. e are r tin don beyond he mit b us i el authiit would eeat tave lltif ren ound s yoc dl with hexplicit e of cmatehge het not rervoir rules ve bedjted aea atctually e done? wh chs a e en? success sories ulbeery luabletao manageme indfft ac what ha not been done? unfortunately, my list s longer here. a few ld xamples in 2002 the california flood magent ta force hel earing d a number of speakers ame nd talked aut clima

all of the schools are looking at the data. and we had a notion of progress, where we had lots of participation. we will have more as the year goes on. whether or not the scores are a true measure of achievement. we have been very clear that it is a measure of student achievement. we are looking at student achievement. we're looking at what those indicators are, setting goals, and the structure and not only professional development but what happens to meet those goals, as well, so it is a big question, and we really do appreciate it. vice president mendoza: thank you. commissioner wynns. commissioner wynns: my questions are budget questions. what i want, and i do not expect to know it this evening, we're going to get that when we get the evaluation, so you will get that in three or four months. if it is a lot of work to say to do it now -- but what i would like to know is to see all of the money that we are spending. i need to know about the small school differential. these kinds of dollars that come mainly from restricted funds

we process the entire data set and made all the corrections in the data set and allow them to determine exactly where all the co2 is going. we do this by determining changes to upset at preindustrial using a combination working together. we also had colleagues on the same clues collect dean for the isotopic signature of the co2. the changes they isotopic signature increase with the carbonate genic backside. that penetration of anthropogenic co2 goes back down to the most part the other 1500 water column. so most the anthropogenic steal the upper part of the water column, we'll muster organisms live. and we know that extremely well. now in this decade, and in 2000, we've been repeating those cruises. so we can see the direct changes to the uptake of carbon dioxide from the atmosphere from the 1990s to the present. and on those cruises, we see the same rate of change of ph that we do as the times three sites at hudson bats. we now know from the large extended surveys across her oceans, that we are seeing exact rate of ph in co2 increases in the water column. this is not extremely well. th

this includes people who are on the employed when we look at the data, so we figured -- as you know, there are large numbers of workers in construction that do not make prevailing wage. they do all types of work, and for the most part, a lot of these workers are paid relatively low wages. >> these are usually non-union workers? >> that is correct. >> did you make any distinction between non-union and union workers, or are they all lumped together? >> for this particular data, they are all lumped together. we did not have a good reference point or a source to make a distinction between how many could be union and how many could be non-union. >> the union folks -- did you approach and they did not have the information? >> the study did not include a survey of the building. now we are going to shift to a subset of san francisco resident workers, and these are active apprentices. now there are five trains that are most common among the carpenters. reserve five trains that are most common among carpenters. the apprentices are almost evenly divided among the major ethnic groups in san franc

you know, to access the data. some refer to that has as authorized use. it's the right idea. attribute-based access control. the challenge coming in that the policies to describe do you have access -- the right to access this information. some of the policies and reinsurance policies, some might be a u.s. persons rule, some might be, you know, a variety of other rule that is are coming out of different domains and agencies that are described in different ways that would make it difficult to automate the evaluation of those rules to make a judgment, you know, in real time. let's go to the issue of looking across the different policies. there's a degree of harmonization. those policies can be automated in a consistent way across the ise. technology, it works, it's established. doing it at scale and across the different domains, that becomes the hard issue. we actually did a pilot with nist over the last year. i think we described it in our annual report. the hard part wasn't the technology. that's all -- you know, th

you go to the data, you find the opposite. most recently, there's been an attempt to measure these fluxes from the surface. you have to understand, the flux might be reasonably constant through the atmosphere, but it's process is different. at the top of the atmosphere is radiation. at the surface it's mostly evaporation. and there is a problem that has been noted for some years. models predict very little change in evaporation, as you warm, compared to observations. and this can be directly translated into sensitivity. the models behavior is consistent with one and a half, afford a half degrees of the doubling of co2. the data suggest it is closer to half the lowest limit. so there too, i mean, one has the problem that the observations would specifically turn to feedbacks rather than specific negativism's. show the opposite. this isn't surprising. one speaks of clouds as a kind of peripheral uncertainty. but they involve changes in the radiative balance that are more than a factor of 20 larger than when you get from a doublin