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The Measure CARE Model works so communities can create solutions based on their unique needs and goals. We Can Now used the Measure CARE model to measure their food distribution efforts in Central Texas. Together we can build a more equitable world one community at a time.

This page provides at-a-glance summaries and maps of the work the City has delivered with Measure KK funding. It also compiles the detailed accountability reporting on Measure KK fund expenditures, including reports made to the Public Oversight Committee and to City Council. Lastly, it provides an overview of projects that received Measure KK funding in all three of the categories specified in the ballot measure, which are:

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The table below shows select Facilities projects managed by Oakland Public Works that received Measure KK funding, as reported to City Council by the Finance Department in February of 2022. Community-initiated projects are marked with an asterisk (*). These are highlighted examples from a list of more than 80 projects receiving funding from the measure.

All members of the public are welcome to attend one or both of these hearings. If a member of the public is unable to attend a hearing, questions and comments may be directed to measurev@mcagov.org by December 29th.

In 2012, the San Mateo County Board of Supervisors placed the original (Measure A) half-cent sales tax on the November ballot as a means of raising local funds for local needs. The decision to place a local tax measure on the ballot followed several years of budget cuts due to the recession and because of decreased or unpredictable funding from the state and federal governments.

The tax measure passed with 65.4 percent (169,661 votes) "yes" vs. 34.6 percent (89,788 votes) "no." It authorized the collection, starting on April 1, 2013, of a half-cent sales tax on taxable items through March 31, 2023.

Our environmental scans look at quality measures for the Quality Payment Program. Each report finds gaps where targeted measure development would help us meet our quality priorities. Our latest report supports development of MIPS Value Pathways (MVPs), a future reporting option for clinicians, by focusing on population health measures and gaps.

Our contractor convenes Technical Expert Panels (TEPs) to help find recommendations to improve quality measures for clinicians. These panels of measurement experts, clinicians, patient/caregiver representatives, and other stakeholders review our gap analyses and give us input on the MDP Annual Reports.

In mathematics, the concept of a measure is a generalization and formalization of geometrical measures (length, area, volume) and other common notions, such as magnitude, mass, and probability of events. These seemingly distinct concepts have many similarities and can often be treated together in a single mathematical context. Measures are foundational in probability theory, integration theory, and can be generalized to assume negative values, as with electrical charge. Far-reaching generalizations (such as spectral measures and projection-valued measures) of measure are widely used in quantum physics and physics in general.

The intuition behind this concept dates back to ancient Greece, when Archimedes tried to calculate the area of a circle. But it was not until the late 19th and early 20th centuries that measure theory became a branch of mathematics. The foundations of modern measure theory were laid in the works of mile Borel, Henri Lebesgue, Nikolai Luzin, Johann Radon, Constantin Carathodory, and Maurice Frchet, among others.

For measure spaces that are also topological spaces various compatibility conditions can be placed for the measure and the topology. Most measures met in practice in analysis (and in many cases also in probability theory) are Radon measures. Radon measures have an alternative definition in terms of linear functionals on the locally convex topological vector space of continuous functions with compact support. This approach is taken by Bourbaki (2004) and a number of other sources. For more details, see the article on Radon measures.

In physics an example of a measure is spatial distribution of mass (see for example, gravity potential), or another non-negative extensive property, conserved (see conservation law for a list of these) or not. Negative values lead to signed measures, see "generalizations" below.

A measurable set X {\displaystyle X} is called a null set if ( X ) = 0. {\displaystyle \mu (X)=0.} A subset of a null set is called a negligible set. A negligible set need not be measurable, but every measurable negligible set is automatically a null set. A measure is called complete if every negligible set is measurable.

A measure can be extended to a complete one by considering the -algebra of subsets Y {\displaystyle Y} which differ by a negligible set from a measurable set X , {\displaystyle X,} that is, such that the symmetric difference of X {\displaystyle X} and Y {\displaystyle Y} is contained in a null set. One defines ( Y ) {\displaystyle \mu (Y)} to equal ( X ) . {\displaystyle \mu (X).}

For example, the real numbers with the standard Lebesgue measure are -finite but not finite. Consider the closed intervals [ k , k + 1 ] {\displaystyle [k,k+1]} for all integers k ; {\displaystyle k;} there are countably many such intervals, each has measure 1, and their union is the entire real line. Alternatively, consider the real numbers with the counting measure, which assigns to each finite set of reals the number of points in the set. This measure space is not -finite, because every set with finite measure contains only finitely many points, and it would take uncountably many such sets to cover the entire real line. The -finite measure spaces have some very convenient properties; -finiteness can be compared in this respect to the Lindelf property of topological spaces.[original research?] They can be also thought of as a vague generalization of the idea that a measure space may have 'uncountable measure'.

Semifinite measures generalize sigma-finite measures, in such a way that some big theorems of measure theory that hold for sigma-finite but not arbitrary measures can be extended with little modification to hold for semifinite measures. (To-do: add examples of such theorems; cf. the talk page.)

The zero measure is sigma-finite and thus semifinite. In addition, the zero measure is clearly less than or equal to . {\displaystyle \mu .} It can be shown there is a greatest measure with these two properties:

We say the semifinite part of {\displaystyle \mu } to mean the semifinite measure sf {\displaystyle \mu _{\text{sf}}} defined in the above theorem. We give some nice, explicit formulas, which some authors may take as definition, for the semifinite part:

For certain purposes, it is useful to have a "measure" whose values are not restricted to the non-negative reals or infinity. For instance, a countably additive set function with values in the (signed) real numbers is called a signed measure, while such a function with values in the complex numbers is called a complex measure. Observe, however, that complex measure is necessarily of finite variation, hence complex measures include finite signed measures but not, for example, the Lebesgue measure.

Measures that take values in Banach spaces have been studied extensively.[19] A measure that takes values in the set of self-adjoint projections on a Hilbert space is called a projection-valued measure; these are used in functional analysis for the spectral theorem. When it is necessary to distinguish the usual measures which take non-negative values from generalizations, the term positive measure is used. Positive measures are closed under conical combination but not general linear combination, while signed measures are the linear closure of positive measures.

A charge is a generalization in both directions: it is a finitely additive, signed measure.[20] (Cf. ba space for information about bounded charges, where we say a charge is bounded to mean its range its a bounded subset of R.)

We see so much emotional discussion about software process,design practices and the like. Many of these arguments are impossibleto resolve because the software industry lacks the ability to measuresome of the basic elements of the effectiveness of softwaredevelopment. In particular we have no way of reasonably measuringproductivity.

Productivity, of course, is something you determine by looking atthe input of an activity and its output. So to measure softwareproductivity you have to measure the output of software development -the reason we can't measure productivity is because we can't measureoutput.

This doesn't mean people don't try. One of my biggest irritationsare studies of productivity based on lines of code. For a startthere's all the stuff about differences between languages, differentcounting styles, and differences due to formatting conventions. Buteven if you use a consistent counting standard on programs in the samelanguage, all auto-formatted to a single style - lines of code stilldoesn't measure output properly.

Now this doesn't mean that LOC is a completely useless measure,it's pretty good at suggesting the size of a system. I can be prettyconfident that a 100 KLOC system is bigger than a 10KLOC system. Butif I've written the 100KLOC system in a year, and Joe writes the samesystem in 10KLOC during the same time, that doesn't make me moreproductive. Indeed I would conclude that our productivities are aboutthe same but my system is much more poorly designed.

But all of this ignores the point that even useful functionalityisn't the true measure. As I get better I produce 30 useful FP offunctionality, and Joe only does 15. But someone figures out thatJoe's 15 leads to $10 million extra profit for our customer and mywork only leads to $5 million. I would again argue that Joe's trueproductivity is higher because he has delivered more businessvalue - and I assert that any true measure of software developmentproductivity must be based on delivered business value. 006ab0faaa