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Scientific modelling
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Example of scientific modelling. A schematic of chemical and transport processes related to atmospheric composition.
Scientific modelling is a scientific activity, the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge. It requires selecting and identifying relevant aspects of a situation in the real world and then using different types of models for different aims, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, and graphical models to visualize the subject. Modelling is an essential and inseparable part of many scientific disciplines, each of which have their own ideas about specific types of modelling.[1][2]
There is also an increasing attention to scientific modelling[3] in fields such as science education, philosophy of science, systems theory, and knowledge visualization. There is growing collection of methods, techniques and meta-theory about all kinds of specialized scientific modelling.
Contents
[hide]
1 Overview
2 Basics of scientific modelling
2.1 Modelling as a substitute for direct measurement and experimentation
2.2 Simulation
2.3 Structure
2.4 Systems
2.5 Generating a model
2.6 Evaluating a model
2.7 Visualization
2.8 Space mapping
3 Types of scientific modelling
4 Applications
4.1 Modelling and simulation
4.2 Model-based learning in education
5 See also
6 References
7 Further reading
8 External links
Overview[edit]
A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way. All models are in simulacra, that is, simplified reflections of reality that, despite being approximations, can be extremely useful.[4] Building and disputing models is fundamental to the scientific enterprise. Complete and true representation may be impossible, but scientific debate often concerns which is the better model for a given task, e.g., which is the more accurate climate model for seasonal forecasting.[5]
Attempts to formalize the principles of the empirical sciences use an interpretation to model reality, in the same way logicians axiomatize the principles of logic. The aim of these attempts is to construct a formal system that will not produce theoretical consequences that are contrary to what is found in reality. Predictions or other statements drawn from such a formal system mirror or map the real world only insofar as these scientific models are true.[6][7]
For the scientist, a model is also a way in which the human thought processes can be amplified.[8] For instance, models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon, or process being represented. Such computer models are in silico. Other types of scientific models are in vivo (living models, such as laboratory rats) and in vitro (in glassware, such as tissue culture).[9]
Basics of scientific modelling[edit]
Modelling as a substitute for direct measurement and experimentation[edit]
Models are typically used when it is either impossible or impractical to create experimental conditions in which scientists can directly measure outcomes. Direct measurement of outcomes under controlled conditions (see Scientific method) will always be more reliable than modelled estimates of outcomes.
Within modelling and simulation, a model is a task-driven, purposeful simplification and abstraction of a perception of reality, shaped by physical, legal, and cognitive constraints.[10] It is task-driven, because a model is captured with a certain question or task in mind. Simplifications leave all the known and observed entities and their relation out that are not important for the task. Abstraction aggregates information that is important, but not needed in the same detail as the object of interest. Both activities, simplification and abstraction, are done purposefully. However, they are done based on a perception of reality. This perception is already a model in itself, as it comes with a physical constraint. There are also constraints on what we are able to legally observe with our current tools and methods, and cognitive constraints which limit what we are able to explain with our current theories. This model comprises the concepts, their behavior, and their relations in formal form and is often referred to as a conceptual model. In order to execute the model, it needs to be implemented as a computer simulation. This requires more choices, such as numerical approximations or the use of heuristics.[11] Despite all these epistemological and computational constraints, simulation has been recognized as the third pillar of scientific methods: theory building, simulation, and experimentation.[12]
Simulation[edit]
A simulation is the implementation of a model. A steady state simulation provides information about the system at a specific instant in time (usually at equilibrium, if such a state exists). A dynamic simulation provides information over time. A simulation brings a model to life and shows how a particular object or phenomenon will behave. Such a simulation can be useful for testing, analysis, or training in those cases where real-world systems or concepts can be represented by models.[13]
Structure[edit]
Structure is a fundamental and sometimes intangible notion covering the recognition, observation, nature, and stability of patterns and relationships of entities. From a child's verbal description of a snowflake, to the detailed scientific analysis of the properties of magnetic fields, the concept of structure is an essential foundation of nearly every mode of inquiry and discovery in science, philosophy, and art.[14]
Systems[edit]
A system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole. In general, a system is a construct or collection of different elements that together can produce results not obtainable by the elements alone.[15] The concept of an 'integrated whole' can also be stated in terms of a system embodying a set of relationships which are differentiated from relationships of the set to other elements, and from relationships between an element of the set and elements not a part of the relational regime. There are two types of system models: 1) discrete in which the variables change instantaneously at separate points in time and, 2) continuous where the state variables change continuously with respect to time.[16]
Generating a model[edit]
Modelling is the process of generating a model as a conceptual representation of some phenomenon. Typically a model will deal with only some aspects of the phenomenon in question, and two models of the same phenomenon may be essentially different—that is to say, that the differences between them comprise more than just a simple renaming of components.
Such differences may be due to differing requirements of the model's end users, or to conceptual or aesthetic differences among the modellers and to contingent decisions made during the modelling process. Considerations that may influence the structure of a model might be the modeller's preference for a reduced ontology, preferences regarding statistical models versus deterministic models, discrete versus continuous time, etc. In any case, users of a model need to understand the assumptions made that are pertinent to its validity for a given use.
Building a model requires abstraction. Assumptions are used in modelling in order to specify the domain of application of the model. For example, the special theory of relativity assumes an inertial frame of reference. This assumption was contextualized and further explained by the general theory of relativity. A model makes accurate predictions when its assumptions are valid, and might well not make accurate predictions when its assumptions do not hold. Such assumptions are often the point with which older theories are succeeded by new ones (the general theory of relativity works in non-inertial reference frames as well).
The term "assumption" is actually broader than its standard use, etymologically speaking. The Oxford English Dictionary (OED) and online Wiktionary indicate its Latin source as assumere ("accept, to take to oneself, adopt, usurp"), which is a conjunction of ad- ("to, towards, at") and sumere (to take). The root survives, with shifted meanings, in the Italian sumere and Spanish sumir. In the OED, "assume" has the senses of (i) “investing oneself with (an attribute), ” (ii) “to undertake” (especially in Law), (iii) “to take to oneself in appearance only, to pretend to possess,” and (iv) “to suppose a thing to be.” Thus, "assumption" connotes other associations than the contemporary standard sense of “that which is assumed or taken for granted; a supposition, postulate,” and deserves a broader analysis in the philosophy of science.[citation needed]
Evaluating a model[edit]
See also: Models of scientific inquiry § Choice of a theory
A model is evaluated first and foremost by its consistency to empirical data; any model inconsistent with reproducible observations must be modified or rejected. One way to modify the model is by restricting the domain over which it is credited with having high validity. A case in point is Newtonian physics, which is highly useful except for the very small, the very fast, and the very massive phenomena of the universe. However, a fit to empirical data alone is not sufficient for a model to be accepted as valid. Other factors important in evaluating a model include:[citation needed]
Ability to explain past observations
Ability to predict future observations
Cost of use, especially in combination with other models
Refutability, enabling estimation of the degree of confidence in the model
Simplicity, or even aesthetic appeal
People may attempt to quantify the evaluation of a model using a utility function.
Visualization[edit]
Visualization is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of man. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering and scientific purposes.
Space mapping[edit]
Space mapping refers to a methodology that employs a "quasi-global" modeling formulation to link companion "coarse" (ideal or low-fidelity) with "fine" (practical or high-fidelity) models of different complexities. In engineering optimization, space mapping aligns (maps) a very fast coarse model with its related expensive-to-compute fine model so as to avoid direct expensive optimization of the fine model. The alignment process iteratively refines a "mapped" coarse model (surrogate model).
Types of scientific modelling[edit]
Analogical modelling
Assembly modelling
Catastrophe modelling
Choice Modelling
Climate model
Continuous modelling
Data modelling
Discrete modeling
Document modelling
Economic model
Ecosystem model
Empirical modelling
Enterprise modelling
Futures studies
Geologic modelling
Goal Modelling
Homology modelling
Hydrogeology
Hydrography
Hydrologic modelling
Idealized modelling
Informative Modelling
Macroscale modelling
Mathematical modelling
Metabolic network modelling
Microscale modelling
Modeling biological systems
Modelling in Epidemiology
Molecular modelling
Multiscale modelling
NLP modelling
Phenomenological modelling
Predictive intake modelling
Predictive modelling
Scale modeling
Simulation
Software modelling
Solid modelling
Space mapping
Statistics
Stochastic modelling (insurance)
Surrogate model
System architecture
System dynamics
Systems modelling
Water quality modelling
Applications[edit]
Modelling and simulation[edit]
One application of scientific modelling is the field of modelling and simulation, generally referred to as "M&S". M&S has a spectrum of applications which range from concept development and analysis, through experimentation, measurement and verification, to disposal analysis. Projects and programs may use hundreds of different simulations, simulators and model analysis tools.
Example of the integrated use of Modelling and Simulation in Defence life cycle management. The modelling and simulation in this image is represented in the center of the image with the three containers.[13]
The figure shows how Modelling and Simulation is used as a central part of an integrated program in a Defence capability development process.[13]
Model-based learning in education[edit]
Model–based learning in education, particularly in relation to learning science involves students creating models for scientific concepts in order to:[17]
Gain insight of the scientific idea(s)
Acquire deeper understanding of the subject through visualization of the model
Improve student engagement in the course
Different types of model based learning techniques include:[17]
Physical macrocosms
Representational systems
Syntactic models
Emergent models
Model–making in education is an iterative exercise with students refining, developing and evaluating their models over time. This shifts learning from the rigidity and monotony of traditional curriculum to an exercise of students' creativity and curiosity. This approach utilizes the constructive strategy of social collaboration and learning scaffold theory. Model based learning includes cognitive reasoning skills where existing models can be improved upon by construction of newer models using the old models as a basis.[18]
"Model–based learning entails determining target models and a learning pathway that provide realistic chances of understanding." [19] Model making can also incorporate blended learning strategies by using web based tools and simulators, thereby allowing students to:
Familiarize themselves with on-line or digital resources
Create different models with various virtual materials at little or no cost
Practice model making activity any time and any place
Refine existing models
"A well-designed simulation simplifies a real world system while heightening awareness of the complexity of the system. Students can participate in the simplified system and learn how the real system operates without spending days, weeks or years it would take to undergo this experience in the real world." [20]
The teacher's role in the overall teaching and learning process is primarily that of a facilitator and arranger of the learning experience. He or she would assign the students, a model making activity for a particular concept and provide relevant information or support for the activity. For virtual model making activities, the teacher can also provide information on the usage of the digital tool and render troubleshooting support in case of glitches while using the same. The teacher can also arrange the group discussion activity between the students and provide the platform necessary for students to share their observations and knowledge extracted from the model making activity.
Model–based learning evaluation could include the use of rubrics that assess the ingenuity and creativity of the student in the model construction and also the overall classroom participation of the student vis-a-vis the knowledge constructed through the activity.
It is important, however, to give due consideration to the following for successful model–based learning to occur:
Use of the right tool at the right time for a particular concept
Provision within the educational setup for model–making activity: e.g., computer room with internet facility or software installed to access simulator or digital tool
See also[edit]
Heuristic
Grey box completion and validation
Scientific visualization
Simulation
Statistical model
Systems engineering
Toy model
References[edit]
Jump up ^ Cartwright, Nancy. 1983. How the Laws of Physics Lie. Oxford University Press
Jump up ^ Hacking, Ian. 1983. Representing and Intervening. Introductory Topics in the Philosophy of Natural Science. Cambridge University Press
Jump up ^ Frigg and Hartmann (2009) state: "Philosophers are acknowledging the importance of models with increasing attention and are probing the assorted roles that models play in scientific practice". Source: Frigg, Roman and Hartmann, Stephan, "Models in Science", The Stanford Encyclopedia of Philosophy (Summer 2009 Edition), Edward N. Zalta (ed.), (source)
Jump up ^ Box, George E.P. & Draper, N.R. (1987). [Empirical Model-Building and Response Surfaces.] Wiley. p. 424
Jump up ^ Hagedorn, R. et al. (2005) http://www.ecmwf.int/staff/paco_doblas/abstr/tellus05_1.pdf Tellus 57A:219–33
Jump up ^ Leo Apostel (1961). "Formal study of models". In: The Concept and the Role of the Model in Mathematics and Natural and Social. Edited by Hans Freudenthal. Springer. pp. 8–9 (Source)],
Jump up ^ Ritchey, T. (2012) Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling
Jump up ^ C. West Churchman, The Systems Approach, New York: Dell publishing, 1968, p. 61
Jump up ^ Griffiths, E. C. (2010) What is a model?
Jump up ^ Tolk, A. (2015). Learning something right from models that are wrong – Epistemology of Simulation. In Yilmaz, L. (Ed.) Concepts and Methodologies in Modeling and Simulation. Springer–Verlag. pp. 87–106
Jump up ^ Oberkampf, W. L., DeLand, S. M., Rutherford, B. M., Diegert, K. V., & Alvin, K. F. (2002). Error and uncertainty in modeling and simulation. Reliability Engineering & System Safety 75(3): 333–57.
Jump up ^ Ihrig, M. (2012). A New Research Architecture For The Simulation Era. In European Council on Modelling and Simulation. pp. 715–20).
^ Jump up to: a b c Systems Engineering Fundamentals. Defense Acquisition University Press, 2003.
Jump up ^ Pullan, Wendy (2000). Structure. Cambridge: Cambridge University Press. ISBN 0-521-78258-9.
Jump up ^ Fishwick PA. (1995). Simulation Model Design and Execution: Building Digital Worlds. Upper Saddle River, NJ: Prentice Hall.
Jump up ^ Sokolowski, J.A., Banks, C.M.(2009). Principles of Modelling and Simulation. Hoboken, NJ: John Wiley and Sons.
^ Jump up to: a b Lehrer, Richard; Schauble, Leona (2006). The Cambridge Handbook of Learning Sciences. Cambridge, UK: Cambridge University Press. p. 371. ISBN 978-0-521-84554-0.
Jump up ^ Nersessian, Nancy J (2002). The Cognitive Basis of Science. Cambridge, UK: Cambridge University Press. p. 133. ISBN 0-521-01177-9.
Jump up ^ Clement, JJ; Rea-Ramirez, Mary Anne (2008). Model Based Learning and Instruction in Science (2 ed.). Springer Science & Business Media. p. 45. ISBN 978-1-4020-6493-7.
Jump up ^ Blumschein, Patrick; Hung, Woei; Jonassen, David; Strobel, Johannes (2009). Model-Based Approaches to Learning (PDF). Netherlands: Sense Publishers. ISBN 978-90-8790-711-2.
Further reading[edit]
Nowadays there are some 40 magazines about scientific modelling which offer all kinds of international forums. Since the 1960s there is a strong growing number of books and magazines about specific forms of scientific modelling. There is also a lot of discussion about scientific modelling in the philosophy-of-science literature. A selection:
Rainer Hegselmann, Ulrich Müller and Klaus Troitzsch (eds.) (1996). Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Theory and Decision Library. Dordrecht: Kluwer.
Paul Humphreys (2004). Extending Ourselves: Computational Science, Empiricism, and Scientific Method. Oxford: Oxford University Press.
Johannes Lenhard, Günter Küppers and Terry Shinn (Eds.) (2006) "Simulation: Pragmatic Constructions of Reality", Springer Berlin.
Tom Ritchey (2012). "Outline for a Morphology of Modelling Methods: Contribution to a General Theory of Modelling". In: Acta Morphologica Generalis, Vol 1. No 1. pp. 1–20.
Fritz Rohrlich (1990). "Computer Simulations in the Physical Sciences". In: Proceedings of the Philosophy of Science Association, Vol. 2, edited by Arthur Fine et al., 507-518. East Lansing: The Philosophy of Science Association.
Rainer Schnell (1990). "Computersimulation und Theoriebildung in den Sozialwissenschaften". In: Kölner Zeitschrift für Soziologie und Sozialpsychologie 1, 109-128.
Sergio Sismondo and Snait Gissis (eds.) (1999). Modeling and Simulation. Special Issue of Science in Context 12.
Eric Winsberg (2001). "Simulations, Models and Theories: Complex Physical Systems and their Representations". In: Philosophy of Science 68 (Proceedings): 442-454.
Eric Winsberg (2003). "Simulated Experiments: Methodology for a Virtual World". In: Philosophy of Science 70: 105–125.
Eric Winsberg (2010) Science in the Age of Computer Simulation Chicago: University of Chicago Press
Tomáš Helikar, Jim A Rogers (2009). "ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists". BioMed Central.
External links[edit]
Wikimedia Commons has media related to Scientific modeling.
Wikiquote has quotations related to: Scientific modelling
Models. Entry in the Internet Encyclopedia of Philosophy
Models in Science. Entry in the Stanford Encyclopedia of Philosophy
The World as a Process: Simulations in the Natural and Social Sciences, in: R. Hegselmann et al. (eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library. Dordrecht: Kluwer 1996, 77-100.
Research in simulation and modelling of various physical systems
Modelling Water Quality Information Center, U.S. Department of Agriculture
Ecotoxicology & Models
A Morphology of Modelling Methods. Acta Morphologica Generalis, Vol 1. No 1. pp. 1–20.
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California locations by crime rate
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The following is a list of California locations by crime rate.
In 2014, California reported 153,709 violent crimes (3.96 for every 1,000 people) and 947,192 property crimes (24.41 for every 1,000 people). These rates are very similar for the average county and city in California.
Contents
1 Entire state
2 Counties
3 Cities and other agencies
4 See also
5 Notes
6 References
Entire state[edit]
The following table includes the number of incidents reported and the rate per 1,000 persons for each type of offense.
Population[1]
38,802,500
Violent crime[1]
153,709 3.96
Murder and nonnegligent manslaughter[1]
1,699 0.04
Rape[1][note 1]
11,527 0.30
Robbery[1]
48,680 1.26
Aggravated assault[1]
91,803 2.37
Property crime[1]
947,192 24.41
Burglary[1]
202,670 5.22
Larceny-theft[1]
592,670 15.27
Motor vehicle theft[1]
151,852 3.91
Counties[edit]
There is no correlation between crime rate and population for counties in California. There is also no correlation between county crime rate and population density if the outlier of San Francisco is excluded. The median county in California has violent and property crime rates roughly the same as California's.
San Bernardino County also has violent and property crime rates similar to that of the state.
County
Population[2]
Population
density[2][3][note 2]
Violent crimes[4]
Violent crime rate
per 1,000 persons
Property crimes[4]
Property crime rate
per 1,000 persons
Alameda 1,559,308 2,109.8 10,356 6.6 57,620 37.0
Alpine 1,202 1.6 4 3.3 24 20.0
Amador 37,159 62.5 81 2.2 629 16.9
Butte 221,578 135.4 678 3.1 6,631 29.9
Calaveras 44,921 44.0 113 2.5 989 22.0
Colusa 21,424 18.6 40 1.9 350 16.3
Contra Costa 1,081,232 1,496.0 3,650 3.4 32,232 29.8
Del Norte 28,066 27.9 165 5.9 649 23.1
El Dorado 181,465 106.3 409 2.3 3,138 17.3
Fresno 948,844 159.2 4,547 4.8 32,535 34.3
Glenn 28,019 21.3 125 4.5 527 18.8
Humboldt 134,876 37.8 485 3.6 4,787 35.5
Imperial 177,026 42.4 583 3.3 5,536 31.3
Inyo 18,439 1.8 106 5.7 303 16.4
Kern 857,730 105.5 4,465 5.2 28,283 33.0
Kings 151,390 109.0 696 4.6 3,475 23.0
Lake 64,209 51.1 307 4.8 1,832 28.5
Lassen 33,356 7.3 140 4.2 521 15.6
Los Angeles 9,974,203 2,457.9 42,725 4.3 217,493 21.8
Madera 152,452 71.3 888 5.8 3,380 22.2
Marin 256,802 493.4 454 1.8 4,565 17.8
Mariposa 17,946 12.4 71 4.0 276 15.4
Mendocino 87,612 25.0 512 5.8 1,546 17.6
Merced 261,609 135.2 1,485 5.7 7,126 27.2
Modoc 9,335 2.4 49 5.2 166 17.8
Mono 14,193 4.7 36 2.5 184 13.0
Monterey 424,927 129.5 1,833 4.3 10,577 24.9
Napa 139,253 186.1 534 3.8 2,385 17.1
Nevada 98,606 103.0 308 3.1 1,591 16.1
Orange 3,086,331 3,903.6 6,257 2.0 54,754 17.7
Placer 361,518 256.9 596 1.6 6,643 18.4
Plumas 19,286 7.6 91 4.7 312 16.2
Riverside 2,266,899 314.6 6,260 2.8 61,434 27.1
Sacramento 1,450,277 1,503.0 7,452 5.1 40,246 27.8
San Benito 56,888 41.0 196 3.4 736 12.9
San Bernardino 2,078,586 103.6 8,281 4.0 54,778 26.4
San Diego 3,183,143 756.7 10,585 3.3 59,050 18.6
San Francisco 829,072 17,680.1 6,822 8.2 45,936 55.4
San Joaquin 701,050 503.9 5,346 7.6 25,092 35.8
San Luis Obispo 274,184 83.1 1,177 4.3 5,683 20.7
San Mateo 739,837 1,649.7 1,561 2.1 14,186 19.2
Santa Barbara 431,555 157.8 1,294 3.0 9,088 21.1
Santa Clara 1,841,569 1,427.3 4,676 2.5 43,084 23.4
Santa Cruz 267,203 600.2 1,139 4.3 8,121 30.4
Shasta 178,520 47.3 1,275 7.1 5,705 32.0
Sierra 3,019 3.2 11 3.6 29 9.6
Siskiyou 44,261 7.1 137 3.1 724 16.4
Solano 421,624 513.1 2,109 5.0 13,453 31.9
Sonoma 491,790 312.1 1,821 3.7 8,583 17.5
Stanislaus 522,794 349.8 2,826 5.4 18,559 35.5
Sutter 95,067 157.8 316 3.3 2,364 24.9
Tehama 63,284 21.5 321 5.1 1,589 25.1
Trinity 13,515 4.3 34 2.5 174 12.9
Tulare 451,108 93.5 1,903 4.2 11,398 25.3
Tuolumne 54,347 24.5 151 2.8 1,157 21.3
Ventura 835,790 453.5 1,890 2.3 16,812 20.1
Yolo 204,162 201.2 757 3.7 5,507 27.0
Yuba 73,059 115.6 296 4.1 2,135 29.2
Cities and other agencies[edit]
There's hardly any correlation between crime rates and population or population density whatsoever for cities in California.
The median city had crime rates slightly lower than that of the state, with a violent crime rate of 2.74 and a property crime rate of 21.66. The fact that the average city had crime rates similar to the state in contrast to the lower median rates indicates the presence of outliers with high crime rates. Indeed, the 66th percentile for violent crime rates was 3.69 crimes per 1,000 people, still not as high as the average crime rate among cities (the 33rd percentile was 1.81).
Irvine ranks second with regard to lowest violent crime rates among places with a population of 50,000 or more even though it has a population roughly five times that. Its violent crime rate is 0.49 per 1,000 people. Joining Irvine among the top five in that population class are San Ramon (0.31), Murrieta (0.63), Cupertino (0.66), and Yorba Linda (0.66). Change the focus to property crime and Orange County cities with a population not much higher than 50,000 dominate the top ten, with seven of the ten places ranking in the top ten being located in Orange County, including (in ascending order of violent crime rate) Aliso Viejo, Laguna Niguel, Lake Forest, Mission Viejo, and Yorba Linda.
Of the ten places with populations over 50,000 and the highest violent crime rates, only two had populations under 100,000: they are Santa Cruz (8.26) and Compton (11.49). The other places include (in descending order of violent crime rate) Oakland, Stockton, San Bernardino, Vallejo, and San Francisco.
City/Agency
County
Population[5]
Population
density[5][3][note 2]
Violent crimes[5]
Violent crime rate
per 1,000 persons
Property crimes[5]
Property crime rate
per 1,000 persons
Adelanto San Bernardino 31,213 557.3 189 6.06 790 25.31
Agoura Hills Los Angeles 20,767 2,664.8 17 0.82 234 11.27
Alameda Alameda 77,048 7,378.7 145 1.88 1,723 22.36
Albany Alameda 19,350 10,822.1 31 1.6 478 24.7
Alhambra Los Angeles 84,931 11,129.7 168 1.98 1,743 20.52
Aliso Viejo Orange 50,671 7,323.5 35 0.69 273 5.39
Alturas Modoc 2,615 1,073.9 29 11.09 89 34.03
American Canyon Napa 20,379 3,351.3 55 2.7 568 27.87
Anaheim Orange 346,956 6,942.3 1,101 3.17 8,196 23.62
Anderson Shasta 10,176 1,597.0 96 9.43 617 60.63
Angels Calaveras 3,716 1,024.3 7 1.88 48 12.92
Antioch Contra Costa 108,223 3,820.1 849 7.84 4,190 38.72
Apple Valley San Bernardino 71,329 958.2 212 2.97 1,654 23.19
Arcadia Los Angeles 57,950 5,304.3 60 1.04 1,159 20.0
Arcata Humboldt 17,748 1,952.0 78 4.39 702 39.55
Arroyo Grande San Luis Obispo 17,829 3,056.6 52 2.92 356 19.97
Artesia Los Angeles 16,788 10,356.6 49 2.92 294 17.51
Arvin Kern 20,541 4,262.5 177 8.62 483 23.51
Atascadero San Luis Obispo 29,268 1,138.3 72 2.46 448 15.31
Atherton San Mateo 7,221 1,435.9 3 0.42 75 10.39
Atwater Merced 28,961 4,736.8 182 6.28 1,024 35.36
Auburn Placer 14,042 1,962.8 44 3.13 295 21.01
Avalon Los Angeles 3,784 1,289.3 20 5.29 89 23.52
Avenal Kings 13,914 716.4 18 1.29 154 11.07
Azusa Los Angeles 48,224 4,994.2 165 3.42 1,065 22.08
Bakersfield Kern 367,406 2,473.3 1,678 4.57 14,595 39.72
Baldwin Park Los Angeles 76,933 11,602.0 219 2.85 1,342 17.44
Banning Riverside 30,699 1,329.0 128 4.17 619 20.16
Barstow San Bernardino 23,349 564.8 217 9.29 781 33.45
Beaumont Riverside 41,323 1,334.4 71 1.72 974 23.57
Bell Los Angeles 36,059 14,417.8 217 6.02 718 19.91
Bellflower Los Angeles 77,830 12,721.5 293 3.76 1,623 20.85
Bell Gardens Los Angeles 43,089 17,523.0 108 2.51 710 16.48
Belmont San Mateo 26,947 5,826.4 36 1.34 372 13.8
Belvedere Marin 2,135 4,113.7 1 0.47 36 16.86
Benicia Solano 27,766 2,147.6 26 0.94 484 17.43
Berkeley Alameda 117,753 11,251.0 431 3.66 5,102 43.33
Beverly Hills Los Angeles 34,788 6,094.6 111 3.19 1,071 30.79
Big Bear Lake San Bernardino 5,148 811.1 44 8.55 262 50.89
Biggs Butte 1,701 2,674.5 17 9.99 22 12.93
Bishop Inyo 3,860 2,070.8 27 6.99 128 33.16
Blythe Riverside 19,669 751.0 88 4.47 748 38.03
Bradbury Los Angeles 1,084 553.9 2 1.85 9 8.3
Brawley Imperial 25,695 3,344.8 69 2.69 1,020 39.7
Brea Orange 41,359 3,392.0 53 1.28 1,124 27.18
Brentwood Contra Costa 55,826 3,759.1 102 1.83 1,250 22.39
Brisbane San Mateo 4,482 1,447.7 11 2.45 133 29.67
Broadmoor San Mateo 4,343 9,629.7 10 2.3 48 11.05
Buellton Santa Barbara 4,997 3,158.7 6 1.2 71 14.21
Buena Park Orange 83,414 7,926.1 217 2.6 2,030 24.34
Burbank Los Angeles 105,041 6,057.4 150 1.43 2,426 23.1
Burlingame San Mateo 30,161 6,847.0 47 1.56 750 24.87
Calabasas Los Angeles 24,325 1,774.8 19 0.78 252 10.36
Calexico Imperial 39,572 4,584.3 96 2.43 1,630 41.19
California City Kern 13,168 64.7 79 6.0 481 36.53
Calimesa Riverside 8,238 554.9 17 2.06 259 31.44
Calistoga Napa 5,277 2,031.2 3 0.57 73 13.83
Camarillo Ventura 66,272 3,393.0 72 1.09 965 14.56
Campbell Santa Clara 40,872 7,042.0 81 1.98 1,388 33.96
Canyon Lake Riverside 10,987 2,809.3 13 1.18 128 11.65
Capitola Santa Cruz 10,134 6,361.6 30 2.96 627 61.87
Carlsbad San Diego 112,297 2,976.6 204 1.82 1,731 15.41
Carmel-by-the-Sea Monterey 3,871 3,584.3 13 3.36 104 26.87
Carpinteria Santa Barbara 13,654 5,280.0 19 1.39 197 14.43
Carson Los Angeles 92,838 4,958.5 339 3.65 2,116 22.79
Cathedral City Riverside 53,368 2,482.2 128 2.4 1,194 22.37
Ceres Stanislaus 46,903 5,028.2 145 3.09 1,584 33.77
Cerritos Los Angeles 49,867 5,717.4 86 1.72 1,525 30.58
Chico Butte 88,562 2,690.1 340 3.84 3,213 36.28
Chino San Bernardino 81,600 2,750.8 186 2.28 1,816 22.25
Chino Hills San Bernardino 76,972 1,722.5 61 0.79 1,030 13.38
Chowchilla Madera 17,079 1,533.5 89 5.21 332 19.44
Chula Vista San Diego 259,894 5,236.5 612 2.35 4,524 17.41
Citrus Heights Sacramento 85,758 6,027.0 389 4.54 2,691 31.38
Claremont Los Angeles 36,042 2,699.8 39 1.08 853 23.67
Clayton Contra Costa 11,646 3,032.8 4 0.34 111 9.53
Clearlake Lake 14,914 1,472.4 97 6.5 681 45.66
Cloverdale Sonoma 8,766 2,887.4 15 1.71 171 19.51
Clovis Fresno 100,705 4,291.3 216 2.14 3,064 30.43
Coachella Riverside 43,633 1,507.2 166 3.8 1,152 26.4
Coalinga Fresno 16,445 2,463.3 121 7.36 315 19.15
Colma San Mateo 1,503 789.0 12 7.98 271 180.31
Colton San Bernardino 53,485 3,490.3 137 2.56 1,575 29.45
Colusa Colusa 5,942 3,239.9 9 1.51 105 17.67
Commerce Los Angeles 13,033 1,993.4 77 5.91 965 74.04
Compton Los Angeles 98,224 9,810.6 1,129 11.49 2,619 26.66
Concord Contra Costa 126,744 4,149.6 465 3.67 5,196 41.0
Corcoran Kings 23,057 3,072.6 86 3.73 292 12.66
Corning Tehama 7,569 2,132.1 35 4.62 309 40.82
Corona Riverside 161,128 4,148.9 171 1.06 3,440 21.35
Coronado San Diego 23,385 2,948.6 17 0.73 545 23.31
Costa Mesa Orange 112,709 7,178.9 318 2.82 3,462 30.72
Cotati Sonoma 7,424 3,948.9 33 4.45 70 9.43
Covina Los Angeles 48,681 6,926.7 110 2.26 1,108 22.76
Crescent City Del Norte 7,090 3,611.8 63 8.89 191 26.94
Cudahy Los Angeles 24,175 20,574.5 81 3.35 332 13.73
Culver City Los Angeles 39,561 7,740.4 169 4.27 1,693 42.79
Cupertino Santa Clara 60,564 5,354.4 40 0.66 1,026 16.94
Cypress Orange 49,370 7,467.9 51 1.03 752 15.23
Daly City San Mateo 105,628 13,825.7 194 1.84 1,685 15.95
Dana Point Orange 34,244 5,269.1 71 2.07 463 13.52
Danville Contra Costa 43,685 2,416.6 17 0.39 439 10.05
Davis Yolo 66,360 6,714.6 84 1.27 1,455 21.93
Delano Kern 52,224 3,651.3 215 4.12 1,486 28.45
Del Mar San Diego 4,305 2,522.0 18 4.18 137 31.82
Del Rey Oaks Monterey 1,683 3,506.2 1 0.59 52 30.9
Desert Hot Springs Riverside 28,086 930.0 168 5.98 1,160 41.3
Diamond Bar Los Angeles 56,665 3,808.4 49 0.86 722 12.74
Dinuba Tulare 23,815 3,680.8 187 7.85 933 39.18
Dixon Solano 19,103 2,680.0 53 2.77 428 22.4
Dorris Siskiyou 899 1,280.6 5 5.56 13 14.46
Dos Palos Merced 5,081 3,766.5 51 10.04 155 30.51
Downey Los Angeles 113,595 9,155.0 281 2.47 2,897 25.5
Duarte Los Angeles 21,817 3,261.1 54 2.48 352 16.13
Dublin Alameda 53,795 3,531.5 70 1.3 798 14.83
Dunsmuir Siskiyou 1,569 978.2 5 3.19 17 10.83
East Palo Alto San Mateo 29,377 11,722.7 124 4.22 574 19.54
Eastvale Riverside 55,504 4,383.2 59 1.06 929 16.74
El Cajon San Diego 102,838 7,103.1 327 3.18 2,252 21.9
El Centro Imperial 43,532 3,927.5 167 3.84 2,095 48.13
El Cerrito Contra Costa 24,485 6,695.4 77 3.14 926 37.82
Elk Grove Sacramento 162,957 3,862.2 622 3.82 3,072 18.85
El Monte Los Angeles 116,220 12,154.4 333 2.87 2,213 19.04
El Paso de Robles (Paso Robles) San Luis Obispo 31,117 1,625.2 125 4.02 788 25.32
El Segundo Los Angeles 16,990 3,110.0 48 2.83 538 31.67
Emeryville Alameda 10,972 8,565.2 117 10.66 1,603 146.1
Encinitas San Diego 62,071 3,299.5 104 1.68 908 14.63
Escalon San Joaquin 7,281 3,164.3 10 1.37 171 23.49
Escondido San Diego 149,839 4,043.3 512 3.42 3,026 20.2
Etna Siskiyou 715 940.8 0 0.0 1 1.4
Eureka Humboldt 26,843 2,860.5 145 5.4 2,176 81.06
Exeter Tulare 10,524 4,272.8 47 4.47 305 28.98
Fairfax Marin 7,647 3,469.6 16 2.09 104 13.6
Fairfield Solano 110,300 2,950.1 519 4.71 3,873 35.11
Farmersville Tulare 10,752 4,761.7 52 4.84 165 15.35
Ferndale Humboldt 1,360 1,324.2 3 2.21 17 12.5
Fillmore Ventura 15,311 4,551.4 46 3.0 122 7.97
Firebaugh Fresno 8,238 2,343.7 75 9.1 299 36.3
Folsom Sacramento 73,329 3,341.2 79 1.08 1,305 17.8
Fontana San Bernardino 204,532 4,820.5 710 3.47 3,710 18.14
Fort Bragg Mendocino 7,245 2,635.5 27 3.73 256 35.33
Fort Jones Siskiyou 681 1,131.2 0 0.0 16 23.49
Fortuna Humboldt 11,752 2,425.6 33 2.81 496 42.21
Foster City San Mateo 32,836 8,650.2 14 0.43 366 11.15
Fountain Valley Orange 57,024 6,285.7 86 1.51 1,023 17.94
Fowler Fresno 6,032 2,372.9 49 8.12 158 26.19
Fremont Alameda 227,575 2,937.8 284 1.25 3,910 17.18
Fresno Fresno 513,187 4,532.5 2,382 4.64 21,101 41.12
Fullerton Orange 139,895 6,236.1 339 2.42 3,206 22.92
Galt Sacramento 24,671 4,115.3 70 2.84 574 23.27
Gardena Los Angeles 60,233 10,333.3 276 4.58 1,389 23.06
Garden Grove Orange 176,106 9,807.1 406 2.31 3,098 17.59
Gilroy Santa Clara 52,415 3,247.1 197 3.76 1,486 28.35
Glendale Los Angeles 197,079 6,473.5 186 0.94 3,073 15.59
Glendora Los Angeles 51,292 2,644.1 66 1.29 1,241 24.19
Goleta Santa Barbara 30,669 3,906.4 35 1.14 422 13.76
Gonzales Monterey 8,459 4,412.6 37 4.37 86 10.17
Grand Terrace San Bernardino 12,405 3,542.3 18 1.45 248 19.99
Grass Valley Nevada 12,777 2,693.9 109 8.53 676 52.91
Greenfield Monterey 16,986 7,956.0 91 5.36 319 18.78
Gridley Butte 6,558 3,168.1 88 13.42 167 25.47
Grover Beach San Luis Obispo 13,497 5,842.9 52 3.85 324 24.01
Guadalupe Santa Barbara 7,260 5,546.2 10 1.38 68 9.37
Gustine Merced 5,667 3,653.8 34 6.0 161 28.41
Hanford Kings 54,824 3,262.6 305 5.56 1,857 33.87
Hawaiian Gardens Los Angeles 14,490 15,284.8 55 3.8 183 12.63
Hawthorne Los Angeles 86,670 14,252.6 658 7.59 2,588 29.86
Hayward Alameda 153,319 3,375.6 605 3.95 4,873 31.78
Healdsburg Sonoma 11,569 2,595.7 19 1.64 238 20.57
Hemet Riverside 82,412 2,959.5 430 5.22 3,357 40.73
Hercules Contra Costa 25,026 3,903.0 27 1.08 286 11.43
Hermosa Beach Los Angeles 19,873 13,926.4 37 1.86 543 27.32
Hesperia San Bernardino 92,592 1,266.7 296 3.2 2,303 24.87
Hidden Hills Los Angeles 1,915 1,133.8 0 0.0 12 6.27
Highland San Bernardino 54,559 2,909.0 256 4.69 1,309 23.99
Hillsborough San Mateo 11,382 1,838.8 1 0.09 103 9.05
Hollister San Benito 36,967 5,047.4 136 3.68 498 13.47
Holtville Imperial 6,030 5,252.6 24 3.98 86 14.26
Hughson Stanislaus 7,089 3,587.6 11 1.55 143 20.17
Huntington Beach Orange 199,152 7,394.1 391 1.96 4,238 21.28
Huntington Park Los Angeles 59,064 19,603.1 409 6.92 1,806 30.58
Huron Fresno 6,792 4,269.0 72 10.6 145 21.35
Imperial Imperial 16,535 2,823.6 5 0.3 46 2.78
Imperial Beach San Diego 27,234 6,545.1 112 4.11 416 15.28
Indian Wells Riverside 5,211 363.8 6 1.15 120 23.03
Indio Riverside 84,540 2,757.6 466 5.51 2,210 26.14
Industry Los Angeles 205 17.4 72 351.22 1,047 5,107.32
Inglewood Los Angeles 111,997 12,350.8 783 6.99 2,740 24.46
Ione Amador 6,773 1,474.0 6 0.89 60 8.86
Irvine Orange 242,971 3,705.5 120 0.49 3,045 12.53
Irwindale Los Angeles 1,426 161.6 18 12.62 169 118.51
Isleton Sacramento 825 1,875.0 2 2.42 23 27.88
Jackson Amador 4,534 1,215.5 17 3.75 113 24.92
Jurupa Valley Riverside 98,684 2,298.7 287 2.91 2,586 26.2
Kensington Contra Costa 5,281 5,576.6 2 0.38 46 8.71
Kerman Fresno 14,538 4,496.8 38 2.61 458 31.5
King City Monterey 13,336 3,468.4 55 4.12 243 18.22
Kingsburg Fresno 11,762 4,165.0 34 2.89 318 27.04
La Cañada Flintridge Los Angeles 20,627 2,390.7 18 0.87 306 14.83
Lafayette Contra Costa 25,355 1,685.1 17 0.67 439 17.31
Laguna Beach Orange 23,371 2,627.7 64 2.74 462 19.77
Laguna Hills Orange 31,035 4,701.6 45 1.45 441 14.21
Laguna Niguel Orange 65,033 4,411.7 73 1.12 554 8.52
Laguna Woods Orange 16,480 4,926.8 6 0.36 118 7.16
La Habra Orange 61,964 8,425.9 105 1.69 1,049 16.93
La Habra Heights Los Angeles 5,451 885.0 8 1.47 49 8.99
Lake Elsinore Riverside 58,665 1,529.8 128 2.18 1,662 28.33
Lake Forest Orange 79,748 4,793.7 104 1.3 682 8.55
Lakeport Lake 4,763 1,557.6 17 3.57 335 70.33
Lakewood Los Angeles 81,382 8,640.2 202 2.48 1,737 21.34
La Mesa San Diego 59,005 6,499.1 208 3.53 1,584 26.85
La Mirada Los Angeles 49,277 6,299.8 89 1.81 635 12.89
Lancaster Los Angeles 160,190 1,698.8 891 5.56 3,661 22.85
La Palma Orange 15,977 8,960.7 12 0.75 247 15.46
La Puente Los Angeles 40,583 11,665.1 136 3.35 440 10.84
La Quinta Riverside 39,753 1,131.5 87 2.19 1,324 33.31
La Verne Los Angeles 32,067 3,803.9 46 1.43 820 25.57
Lawndale Los Angeles 33,340 16,889.6 157 4.71 398 11.94
Lemon Grove San Diego 26,332 6,788.3 125 4.75 516 19.6
Lemoore Kings 25,080 2,944.7 119 4.74 660 26.32
Lincoln Placer 45,792 2,279.0 17 0.37 463 10.11
Lindsay Tulare 13,300 5,095.8 41 3.08 243 18.27
Livermore Alameda 86,147 3,206.5 236 2.74 1,501 17.42
Livingston Merced 13,690 3,723.1 50 3.65 245 17.9
Lodi San Joaquin 63,601 4,652.3 290 4.56 2,095 32.94
Loma Linda San Bernardino 23,798 3,166.3 41 1.72 689 28.95
Lomita Los Angeles 20,677 10,820.0 77 3.72 355 17.17
Lompoc Santa Barbara 43,809 3,778.6 153 3.49 854 19.49
Long Beach Los Angeles 471,123 9,366.1 2,304 4.89 12,438 26.4
Los Alamitos Orange 11,708 2,911.0 16 1.37 204 17.42
Los Altos Santa Clara 30,256 4,673.5 7 0.23 322 10.64
Los Altos Hills Santa Clara 8,408 942.2 2 0.24 73 8.68
Los Angeles Los Angeles 3,906,772 8,335.9 19,171 4.91 83,139 21.28
Los Banos Merced 37,011 3,703.7 133 3.59 900 24.32
Los Gatos Santa Clara 30,614 2,741.7 23 0.75 616 20.12
Lynwood Los Angeles 71,812 14,837.2 401 5.58 1,424 19.83
McFarland Kern 12,346 4,627.4 65 5.26 230 18.63
Madera Madera 63,495 4,021.5 415 6.54 1,686 26.55
Malibu Los Angeles 12,912 652.5 25 1.94 312 24.16
Mammoth Lakes Mono 8,033 323.1 28 3.49 111 13.82
Manhattan Beach Los Angeles 35,872 9,111.5 37 1.03 878 24.48
Manteca San Joaquin 73,055 3,555.5 176 2.41 2,100 28.75
Marina Monterey 20,530 2,311.2 58 2.83 463 22.55
Martinez Contra Costa 37,416 2,962.2 73 1.95 974 26.03
Marysville Yuba 12,242 3,534.1 86 7.02 507 41.41
Maywood Los Angeles 27,833 23,627.3 88 3.16 250 8.98
Mendota Fresno 11,480 3,483.0 72 6.27 287 25.0
Menifee Riverside 84,843 1,825.7 107 1.26 1,501 17.69
Menlo Park San Mateo 33,321 3,408.8 52 1.56 565 16.96
Merced Merced 81,603 3,513.4 570 6.99 2,632 32.25
Mill Valley Marin 14,411 3,025.6 11 0.76 188 13.05
Milpitas Santa Clara 70,568 5,196.1 112 1.59 2,131 30.2
Mission Viejo Orange 97,124 5,510.0 71 0.73 864 8.9
Modesto Stanislaus 205,820 4,616.1 1,778 8.64 9,113 44.28
Monrovia Los Angeles 37,225 2,736.1 47 1.26 718 19.29
Montague Siskiyou 1,398 786.3 2 1.43 13 9.3
Montclair San Bernardino 38,347 6,950.7 210 5.48 1,506 39.27
Montebello Los Angeles 63,737 7,648.7 134 2.1 1,449 22.73
Monterey Monterey 28,512 3,280.3 115 4.03 1,022 35.84
Monterey Park Los Angeles 61,284 7,992.2 91 1.48 1,039 16.95
Monte Sereno Santa Clara 3,511 2,147.4 0 0.0 30 8.54
Moorpark Ventura 35,299 2,806.2 39 1.1 277 7.85
Moraga Contra Costa 16,953 1,791.1 8 0.47 150 8.85
Moreno Valley Riverside 202,911 3,957.3 584 2.88 6,410 31.59
Morgan Hill Santa Clara 41,585 3,261.8 65 1.56 612 14.72
Morro Bay San Luis Obispo 10,516 1,983.0 50 4.75 234 22.25
Mountain View Santa Clara 78,759 6,567.1 156 1.98 1,608 20.42
Mount Shasta Siskiyou 3,267 867.5 6 1.84 62 18.98
Murrieta Riverside 108,376 3,227.3 68 0.63 1,458 13.45
Napa Napa 79,572 4,456.6 249 3.13 1,315 16.53
National City San Diego 60,130 8,260.8 267 4.44 1,589 26.43
Needles San Bernardino 4,944 160.4 25 5.06 164 33.17
Nevada City Nevada 3,055 1,396.3 36 11.78 95 31.1
Newark Alameda 44,467 3,204.8 109 2.45 971 21.84
Newman Stanislaus 10,742 5,333.7 25 2.33 157 14.62
Newport Beach Orange 87,759 3,690.8 109 1.24 1,880 21.42
Norco Riverside 26,933 1,954.9 46 1.71 642 23.84
Norwalk Los Angeles 106,838 11,004.0 316 2.96 2,001 18.73
Novato Marin 54,756 1,995.3 80 1.46 770 14.06
Oakdale Stanislaus 21,651 3,581.6 43 1.99 822 37.97
Oakland Alameda 409,994 7,335.3 6,910 16.85 24,367 59.43
Oakley Contra Costa 38,851 2,447.6 45 1.16 469 12.07
Oceanside San Diego 174,102 4,220.4 652 3.74 3,984 22.88
Ojai Ventura 7,607 1,749.5 10 1.31 120 15.77
Ontario San Bernardino 168,278 3,370.0 431 2.56 4,654 27.66
Orange Orange 140,767 5,551.2 142 1.01 2,241 15.92
Orange Cove Fresno 9,704 5,415.2 10 1.03 151 15.56
Orinda Contra Costa 18,904 1,470.8 4 0.21 187 9.89
Orland Glenn 7,511 2,577.6 20 2.66 123 16.38
Oroville Butte 16,090 1,215.5 91 5.66 994 61.78
Oxnard Ventura 204,159 7,589.3 884 4.33 6,382 31.26
Pacifica San Mateo 38,925 3,074.2 91 2.34 638 16.39
Pacific Grove Monterey 15,612 5,462.6 30 1.92 301 19.28
Palmdale Los Angeles 158,210 1,493.0 840 5.31 3,219 20.35
Palm Desert Riverside 50,971 1,901.3 140 2.75 2,178 42.73
Palm Springs Riverside 46,665 496.2 295 6.32 2,722 58.33
Palo Alto Santa Clara 67,169 2,813.6 59 0.88 1,299 19.34
Palos Verdes Estates Los Angeles 13,668 2,863.0 5 0.37 121 8.85
Paradise Butte 26,306 1,436.1 52 1.98 516 19.62
Paramount Los Angeles 55,194 11,668.9 239 4.33 1,408 25.51
Parlier Fresno 14,948 6,538.9 116 7.76 234 15.65
Pasadena Los Angeles 140,373 6,107.7 394 2.81 3,469 24.71
Patterson Stanislaus 20,977 3,523.2 40 1.91 626 29.84
Perris Riverside 73,223 2,332.4 180 2.46 1,905 26.02
Petaluma Sonoma 59,803 4,158.2 200 3.34 1,074 17.96
Pico Rivera Los Angeles 63,970 7,710.9 275 4.3 1,360 21.26
Piedmont Alameda 11,170 6,570.6 8 0.72 232 20.77
Pinole Contra Costa 19,034 3,695.9 69 3.63 632 33.2
Pismo Beach San Luis Obispo 7,910 2,197.8 26 3.29 387 48.93
Pittsburg Contra Costa 67,509 3,927.7 175 2.59 2,362 34.99
Placentia Orange 52,513 7,939.7 82 1.56 732 13.94
Placerville El Dorado 10,486 1,804.2 57 5.44 273 26.03
Pleasant Hill Contra Costa 34,363 4,856.3 60 1.75 1,738 50.58
Pleasanton Alameda 75,060 3,072.8 61 0.81 1,244 16.57
Pomona Los Angeles 151,899 6,618.1 777 5.12 4,403 28.99
Porterville Tulare 55,391 3,145.8 158 2.85 1,204 21.74
Port Hueneme Ventura 22,247 4,998.2 89 4.0 347 15.6
Poway San Diego 49,786 1,274.0 70 1.41 436 8.76
Rancho Cordova Sacramento 68,636 1,969.3 305 4.44 1,644 23.95
Rancho Cucamonga San Bernardino 172,694 4,332.1 283 1.64 3,668 21.24
Rancho Mirage Riverside 17,926 732.8 29 1.62 677 37.77
Rancho Palos Verdes Los Angeles 42,645 3,167.1 24 0.56 472 11.07
Rancho Santa Margarita Orange 49,558 3,842.6 27 0.54 203 4.1
Red Bluff Tehama 14,103 1,864.7 129 9.15 911 64.6
Redding Shasta 91,426 1,532.8 610 6.67 3,924 42.92
Redlands San Bernardino 70,295 1,951.0 147 2.09 2,837 40.36
Redondo Beach Los Angeles 68,075 10,983.4 158 2.32 1,447 21.26
Redwood City San Mateo 81,870 4,208.8 194 2.37 1,728 21.11
Reedley Fresno 25,186 4,902.9 199 7.9 460 18.26
Rialto San Bernardino 102,540 4,589.4 328 3.2 2,154 21.01
Richmond Contra Costa 108,464 3,609.2 843 7.77 4,281 39.47
Ridgecrest Kern 28,830 1,388.3 118 4.09 470 16.3
Rio Dell Humboldt 3,358 1,472.2 19 5.66 57 16.97
Rio Vista Solano 7,825 1,179.2 38 4.86 116 14.82
Ripon San Joaquin 14,859 2,800.9 18 1.21 258 17.36
Riverbank Stanislaus 23,690 5,789.3 28 1.18 688 29.04
Riverside Riverside 319,453 3,935.2 1,384 4.33 9,864 30.88
Rocklin Placer 60,371 3,088.8 50 0.83 1,039 17.21
Rohnert Park Sonoma 41,535 5,931.0 160 3.85 735 17.7
Rolling Hills Los Angeles 1,896 633.9 1 0.53 18 9.49
Rolling Hills Estates Los Angeles 8,233 2,306.8 7 0.85 114 13.85
Rosemead Los Angeles 54,748 10,606.0 147 2.69 1,186 21.66
Roseville Placer 128,997 3,055.9 193 1.5 3,070 23.8
Ross Marin 2,476 1,591.3 4 1.62 21 8.48
Sacramento Sacramento 482,767 4,930.0 2,968 6.15 15,078 31.23
St. Helena Napa 5,978 1,198.7 6 1.0 56 9.37
Salinas Monterey 156,908 6,645.8 997 6.35 5,227 33.31
San Bernardino San Bernardino 214,588 3,488.8 2,128 9.92 9,239 43.05
San Bruno San Mateo 42,787 7,835.0 110 2.57 1,054 24.63
San Buenaventura (Ventura) Ventura 109,246 5,007.6 276 2.53 3,867 35.4
San Clemente Orange 65,397 3,562.7 69 1.06 687 10.51
Sand City Monterey 343 610.3 4 11.66 165 481.05
San Diego San Diego 1,368,690 4,208.9 5,214 3.81 26,812 19.59
San Dimas Los Angeles 33,953 2,258.0 69 2.03 631 18.58
San Fernando Los Angeles 24,362 10,262.0 75 3.08 406 16.67
San Francisco San Francisco 850,294 18,132.6 6,761 7.95 45,093 53.03
San Gabriel Los Angeles 40,409 9,748.9 83 2.05 555 13.73
Sanger Fresno 24,771 4,330.6 97 3.92 549 22.16
San Jacinto Riverside 46,216 1,797.1 101 2.19 1,875 40.57
San Jose Santa Clara 1,009,679 5,718.3 3,242 3.21 24,577 24.34
San Juan Capistrano Orange 36,124 2,537.7 62 1.72 383 10.6
San Leandro Alameda 88,690 6,644.9 369 4.16 3,757 42.36
San Luis Obispo San Luis Obispo 46,672 3,554.6 240 5.14 1,439 30.83
San Marcos San Diego 90,799 3,726.6 196 2.16 1,286 14.16
San Marino Los Angeles 13,371 3,549.5 5 0.37 221 16.53
San Mateo San Mateo 102,082 8,415.7 230 2.25 1,944 19.04
San Pablo Contra Costa 29,831 11,342.6 241 8.08 1,162 38.95
San Rafael Marin 59,292 3,578.9 193 3.26 1,664 28.06
San Ramon Contra Costa 75,049 4,036.8 23 0.31 748 9.97
Santa Ana Orange 336,462 12,394.5 1,260 3.74 5,784 17.19
Santa Barbara Santa Barbara 90,889 4,663.4 302 3.32 2,389 26.28
Santa Clara Santa Clara 121,114 6,579.1 162 1.34 3,268 26.98
Santa Clarita Los Angeles 206,930 3,924.3 307 1.48 2,437 11.78
Santa Cruz Santa Cruz 63,440 4,979.6 524 8.26 3,270 51.54
Santa Fe Springs Los Angeles 17,254 1,944.8 74 4.29 1,124 65.14
Santa Maria Santa Barbara 102,885 4,516.5 439 4.27 3,045 29.6
Santa Monica Los Angeles 93,151 11,069.6 338 3.63 3,026 32.48
Santa Paula Ventura 30,269 6,590.2 85 2.81 447 14.77
Santa Rosa Sonoma 172,991 4,189.5 636 3.68 3,850 22.26
Santee San Diego 56,737 3,495.8 130 2.29 794 13.99
Saratoga Santa Clara 31,122 2,493.9 19 0.61 288 9.25
Sausalito Marin 7,137 4,029.9 21 2.94 232 32.51
Scotts Valley Santa Cruz 11,795 2,554.1 17 1.44 264 22.38
Seal Beach Orange 24,727 2,188.8 26 1.05 494 19.98
Seaside Monterey 34,350 3,717.5 116 3.38 507 14.76
Sebastopol Sonoma 7,646 4,110.8 10 1.31 124 16.22
Selma Fresno 24,404 4,749.7 154 6.31 986 40.4
Shafter Kern 17,235 616.7 46 2.67 462 26.81
Sierra Madre Los Angeles 11,090 3,755.5 13 1.17 92 8.3
Signal Hill Los Angeles 11,411 5,244.0 40 3.51 500 43.82
Simi Valley Ventura 126,604 3,052.5 140 1.11 1,618 12.78
Solana Beach San Diego 13,321 3,784.4 22 1.65 188 14.11
Soledad Monterey 25,807 5,846.6 53 2.05 232 8.99
Solvang Santa Barbara 5,418 2,234.2 9 1.66 81 14.95
Sonoma Sonoma 10,950 3,990.5 34 3.11 166 15.16
Sonora Tuolumne 4,786 1,513.1 22 4.6 335 70.0
South El Monte Los Angeles 20,465 7,198.4 120 5.86 647 31.61
South Gate Los Angeles 95,981 13,264.4 506 5.27 2,687 28.0
South Lake Tahoe El Dorado 21,379 2,104.6 112 5.24 516 24.14
South Pasadena Los Angeles 26,041 7,647.9 30 1.15 475 18.24
South San Francisco San Mateo 66,793 7,277.5 156 2.34 1,274 19.07
Stallion Springs Kern 2,587 157.5 4 1.55 11 4.25
Stanton Orange 38,791 12,525.3 124 3.2 684 17.63
Stockton San Joaquin 299,519 4,857.0 3,988 13.31 13,148 43.9
Suisun City Solano 28,986 7,061.1 68 2.35 606 20.91
Sunnyvale Santa Clara 149,384 6,795.4 167 1.12 2,356 15.77
Susanville Lassen 15,048 1,894.0 107 7.11 315 20.93
Sutter Creek Amador 2,438 929.5 3 1.23 42 17.23
Taft Kern 8,895 582.7 46 5.17 276 31.03
Tehachapi Kern 12,997 1,316.2 51 3.92 309 23.77
Temecula Riverside 108,308 2,910.6 100 0.92 2,535 23.41
Temple City Los Angeles 36,272 9,054.4 45 1.24 428 11.8
Thousand Oaks Ventura 129,175 2,340.8 128 0.99 1,599 12.38
Tiburon Marin 9,232 2,076.5 1 0.11 107 11.59
Torrance Los Angeles 147,971 7,225.9 155 1.05 2,623 17.73
Tracy San Joaquin 85,078 3,770.9 129 1.52 2,262 26.59
Truckee Nevada 16,161 500.0 18 1.11 164 10.15
Tulare Tulare 61,596 3,023.1 457 7.42 2,054 33.35
Tulelake Siskiyou 988 2,409.8 1 1.01 3 3.04
Turlock Stanislaus 70,786 4,181.6 372 5.26 2,510 35.46
Tustin Orange 79,046 7,103.3 132 1.67 1,356 17.15
Twentynine Palms San Bernardino 25,936 438.5 62 2.39 301 11.61
Ukiah Mendocino 15,836 3,391.0 134 8.46 551 34.79
Union City Alameda 73,268 3,776.7 207 2.83 1,602 21.86
Upland San Bernardino 75,790 4,853.0 170 2.24 2,165 28.57
Vacaville Solano 94,701 3,336.5 268 2.83 2,587 27.32
Vallejo Solano 119,504 3,896.3 1,034 8.65 4,877 40.81
Vernon Los Angeles 115 23.1 25 217.39 312 2,713.04
Victorville San Bernardino 122,316 1,668.0 642 5.25 4,374 35.76
Villa Park Orange 5,982 2,877.3 5 0.84 84 14.04
Visalia Tulare 128,488 3,448.6 484 3.77 3,796 29.54
Vista San Diego 97,651 5,227.6 340 3.48 1,686 17.27
Walnut Los Angeles 30,282 3,367.7 29 0.96 294 9.71
Walnut Creek Contra Costa 67,555 3,418.9 74 1.1 2,439 36.1
Waterford Stanislaus 8,646 3,713.9 18 2.08 196 22.67
Watsonville Santa Cruz 52,778 7,891.4 265 5.02 1,489 28.21
Weed Siskiyou 2,894 604.2 20 6.91 84 29.03
West Covina Los Angeles 108,136 6,741.2 225 2.08 2,832 26.19
West Hollywood Los Angeles 35,507 18,816.6 267 7.52 1,325 37.32
Westlake Village Los Angeles 8,452 1,630.1 7 0.83 173 20.47
Westminster Orange 92,217 9,182.2 194 2.1 2,210 23.97
Westmorland Imperial 2,264 3,837.3 8 3.53 4 1.77
West Sacramento Yolo 50,152 2,333.1 262 5.22 1,295 25.82
Wheatland Yuba 3,521 2,160.1 4 1.14 50 14.2
Whittier Los Angeles 86,952 5,935.7 239 2.75 2,247 25.84
Wildomar Riverside 33,921 1,432.0 39 1.15 550 16.21
Williams Colusa 5,188 953.0 6 1.16 64 12.34
Willits Mendocino 4,814 1,720.5 33 6.86 50 10.39
Willows Glenn 6,089 2,138.7 22 3.61 167 27.43
Windsor Sonoma 27,350 3,763.1 86 3.14 254 9.29
Winters Yolo 6,962 2,372.1 21 3.02 155 22.26
Woodlake Tulare 7,700 3,334.8 30 3.9 122 15.84
Woodland Yolo 56,854 3,714.5 298 5.24 1,756 30.89
Yorba Linda Orange 67,702 3,505.3 45 0.66 702 10.37
Yountville Napa 2,978 1,945.1 7 2.35 40 13.43
Yreka Siskiyou 7,558 756.9 62 8.2 298 39.43
Yuba City Sutter 65,525 4,473.0 223 3.4 1,699 25.93
Yucaipa San Bernardino 52,794 1,858.9 71 1.34 978 18.52
Yucca Valley San Bernardino 21,228 530.5 75 3.53 533 25.11
See also[edit]
Crime in California
California locations by income
California locations by race
California locations by voter registration
Organized crime in California
Crime in the United States
Notes[edit]
Jump up ^ Revised definition: See Table 5 data declaration
^ Jump up to: a b Total population divided by land area in square miles.
References[edit]
^ Jump up to: a b c d e f g h i j United States Department of Justice, Federal Bureau of Investigation. Crime in the United States, 2014, Table 5. Retrieved 2016-08-13.
^ Jump up to: a b U.S. Census Bureau. American Community Survey, 2010-2014 American Community Survey 5-Year Estimates, Table B01003. American FactFinder Archived September 11, 2013, at the Wayback Machine..
^ Jump up to: a b U.S. Census Bureau. 2014 U.S. Gazetteer. 2014 U.S. Gazetteer Files.
^ Jump up to: a b Office of the Attorney General, Department of Justice, State of California. CJSC Statistics: Crimes and Clearances. Retrieved 2016-08-13.
^ Jump up to: a b c d United States Department of Justice, Federal Bureau of Investigation. Crime in the United States, 2014, Table 8 (California). Retrieved 2016-08-03.
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