Safest Cities for Women

Safest Cities for Women

by Amy Orr

The modern world is full of dangers, especially for women.

One out of every six American women will be the victim of rape or attempted rape at some point in her lifetime, according to RAINN, and someone is sexually assaulted in America every two minutes.

We see the stories on the news, we hear the statistics and — whether we are aware of it or not — we all make choices to minimize our risk and protect our personal safety. Don't walk home; get a taxi. Don't leave your drink unattended at a bar. Travel in pairs. Know your workplace's harassment policies.

From violent crime to harassment to curtailed rights, women have much to consider, and not all places are created equal. We have compiled 31 different factors to consider the safest cities in the U.S. for women. Below you will find our results, as well as the methodology used to determine them.

The Safest Cities for Women

In our analysis of the safest cities for women, we studied not only crime statistics but also availability and access to appropriate healthcare, cancer rates, education levels, workplace policies, female representation at the mayoral level as well as in the police force, conviction rates of offenders and income levels. A full list of the factors considered can be found below. For now, here are our top five safest cities and where they ranked in four major categories.

1. Thousand Oaks, CA

  • Crime: 11
  • Public Policy & Representation: 26
  • Healthcare: 10
  • Education & Wealth: 7

2. Stamford, CT

  • Crime: 7
  • Public Policy & Representation: 93
  • Healthcare: 32
  • Education & Wealth: 2

3. Cambridge, MA

  • Crime: 37
  • Public Policy & Representation: 26
  • Healthcare: 36
  • Education & Wealth: 5

4. Fort Collins, CO

  • Crime: 34
  • Public Policy & Representation: 126
  • Healthcare: 15
  • Education & Wealth: 13

5. Amherst Town, NY

  • Crime: 8
  • Public Policy & Representation: 101
  • Healthcare: 47
  • Education & Wealth: 48

The Rest of the Best

The five above were tops, but 256 other cities were under consideration.

It is interesting to note that all of the cities ranked in the top ten for safety have populations of less than 300,000 people. Three of the top ten cities are in California, whilst three of the bottom ten cities are in Texas. The safest large city in the study was New York, coming in at number 11; whilst there was no one category in which New York particularly excelled, there was also no category in which it did poorly, and its strong performance across the board secured its high place in the rankings.

Where did your city rank?

Overall
City
Crime
Public Policy & Representation
Healthcare
Education & Wealth
1Thousand Oaks, CA1126107
2Stamford, CT793322
3Cambridge, MA3726365
4Fort Collins, CO341261513
5Amherst Town, NY81014748
6Concord, CA1191512
7Naperville, IL11018630
8Simi Valley, CA132623191
8Yonkers, NY491014721
10Irvine, CA42610168
11New York, NY541014748
12Mckinney, TX231267641
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CA","30","1","101","105"],["20","Green Bay, WI","63","226","9","74"],["21","Fremont, CA","43","26","101","64"],["22","Chula Vista, CA","29","1","101","114"],["23","Sterling Heights, MI","20","226","36","113"],["24","Roseville, CA","56","1","101","66"],["25","Bellevue, WA","69","126","67","25"],["26","San Jose, CA","102","26","26","111"],["27","Thornton, CO","88","14","93","23"],["28","Honolulu, HI","111","101","52","8"],["29","San Diego, CA","70","26","97","54"],["30","Bridgeport, CT","121","93","32","46"],["31","Daly City, CA","41","26","101","114"],["32","Glendale, CA","15","26","101","170"],["33","Rancho Cucamonga, CA","46","26","101","114"],["34","Santa Rosa, CA","109","26","73","45"],["35","Virginia Beach, VA","55","226","75","17"],["36","San Mateo, CA","48","26","101","114"],["37","Alexandria, VA","15","93","166","6"],["38","Elk Grove, CA","51","26","101","114"],["38","Elgin, IL","14","101","86","168"],["40","Grand Rapids, MI","135","93","32","43"],["41","Kenosha, 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CA","91","26","101","114"],["64","Moreno Valley, CA","94","26","101","114"],["65","El Cajon, CA","95","26","101","114"],["66","Gilbert, AZ","10","126","183","59"],["67","Santa Ana, CA","99","26","101","114"],["68","Fontana, CA","74","1","101","184"],["69","Colorado Springs, CO","191","126","23","24"],["70","Sioux Falls, SD","121","178","58","61"],["71","Pasadena, CA","116","26","101","88"],["72","Madison, WI","66","226","4","249"],["73","Erie, PA","118","178","23","141"],["74","Cape Coral, FL","12","26","215","71"],["75","Syracuse, NY","176","11","13","163"],["76","Downey, CA","82","26","101","168"],["77","Oceanside, CA","84","26","101","171"],["78","Baltimore, MD","250","13","21","8"],["79","Washington D.C., DC","233","121","7","1"],["80","Sacramento, CA","140","26","50","166"],["81","Fullerton, CA","71","1","101","222"],["82","Murfreesboro, TN","151","178","66","19"],["83","Scottsdale, AZ","38","126","183","54"],["84","Norwalk, CA","60","26","101","242"],["85","Allentown, PA","101","178","80","103"],["86","Providence, RI","136","101","45","161"],["87","Peoria, IL","170","101","46","92"],["88","Fort Wayne, IN","104","178","65","135"],["89","Ventura, CA","132","26","101","114"],["90","Chicago, IL","163","101","86","35"],["91","Columbus, OH","169","126","22","135"],["92","Escondido, CA","86","26","101","212"],["93","Cary, NC","2","178","235","11"],["94","Chandler, AZ","47","126","183","61"],["95","Frisco, TX","6","126","240","32"],["96","Toledo, OH","192","14","26","164"],["97","Los Angeles, CA","139","26","101","114"],["98","Aurora, CO","158","126","93","36"],["99","Anaheim, CA","92","26","101","221"],["100","Costa Mesa, CA","147","26","101","114"],["101","Springfield, MA","192","178","36","56"],["102","Lancaster, CA","155","26","101","103"],["103","Long Beach, CA","153","26","101","114"],["104","Columbia, MO","120","226","32","188"],["105","Louisville, KY","156","178","59","96"],["106","Plano, TX","24","126","240","34"],["107","Charleston, SC","66","178","172","53"],["108","Hartford, CT","216","93","10","134"],["108","Kent, WA","165","126","67","100"],["110","Chesapeake, VA","77","226","166","15"],["111","Lexington, KY","160","178","59","96"],["112","Lakeland, FL","166","178","76","51"],["113","Buffalo, NY","235","101","6","108"],["114","Carrollton, TX","22","126","240","52"],["114","Richardson, TX","30","126","240","36"],["116","Riverside, CA","105","26","101","235"],["117","Hayward, CA","150","1","101","165"],["118","Akron, OH","205","126","20","138"],["119","Palmdale, CA","107","26","101","239"],["120","Rialto, CA","113","1","101","244"],["121","Jacksonville, FL","179","178","82","33"],["122","Port St. Lucie, FL","5","178","204","138"],["123","Coral Springs, FL","21","178","222","74"],["124","Lakewood, CO","195","126","93","20"],["125","Vancouver, WA","159","126","67","145"],["126","Jersey City, NJ","97","101","198","25"],["127","Pearland, TX","45","126","240","30"],["128","Philadelphia, PA","198","178","80","14"],["129","Hampton, VA","80","226","166","48"],["130","Inglewood, CA","117","26","101","238"],["131","Newport, VA","99","226","166","16"],["132","Ontario, CA","114","26","101","250"],["133","Warren, MI","144","226","36","192"],["134","Des Moines, IA","183","126","1","252"],["135","Pembroke Pines, FL","38","178","222","74"],["136","Lincoln, NE","130","226","26","251"],["137","Richmond, VA","131","226","79","148"],["138","Miramar, FL","65","178","222","29"],["139","Henderson, NV","57","226","195","69"],["140","Lansing, MI","164","226","18","211"],["141","Lewisville, TX","60","126","240","42"],["142","Overland Park, KS","78","226","164","94"],["143","Cincinnati, OH","247","126","26","99"],["144","Gainesville, FL","136","178","92","155"],["145","College Station, TX","50","14","240","149"],["146","Spokane, WA","235","126","63","61"],["147","Springfield, IL","203","101","26","216"],["148","Round Rock, TX","17","126","240","149"],["149","Santa Maria, CA","128","1","218","56"],["150","Clarksville, TN","107","26","155","208"],["151","Manchester, NH","180","126","54","201"],["152","Greensboro, NC","162","26","163","87"],["153","Antioch, CA","217","26","51","203"],["154","Topeka, KS","187","226","82","71"],["155","Dayton, OH","248","14","19","222"],["156","Salem, OR","126","26","151","199"],["157","Visalia, CA","149","26","100","256"],["158","Billings, MT","167","226","52","188"],["159","Seattle, WA","202","126","56","178"],["160","Vallejo, CA","232","26","59","180"],["161","San Francisco, CA","225","26","101","114"],["162","Provo, UT","40","178","174","237"],["163","Berkeley, CA","182","26","101","209"],["164","Everett, WA","208","126","67","161"],["165","Tallahassee, FL","211","178","54","147"],["165","Milwaukee, WI","242","226","10","141"],["167","Athens-Clarke County, GA","143","178","164","64"],["168","Norfolk, VA","103","226","166","110"],["169","Paterson, NJ","123","101","198","92"],["170","Victorville, CA","184","1","101","233"],["171","Cleveland, OH","253","126","13","188"],["172","Durham, NC","188","178","153","4"],["173","Huntsville, AL","186","226","86","111"],["174","Palm Bay, FL","86","178","222","74"],["175","San Bernardino, CA","238","26","101","114"],["176","Irving, TX","62","14","240","196"],["177","Tampa, FL","115","178","222","21"],["178","Montgomery, AL","126","226","84","247"],["179","Pomona, CA","173","26","101","254"],["180","Pueblo, CO","254","126","26","180"],["181","Knoxville, TN","246","26","63","197"],["182","Sandy Springs, GA","52","178","261","88"],["183","Independence, MO","174","93","160","105"],["184","Wichita Falls, TX","124","126","207","102"],["185","Broken Arrow, OK","42","226","219","184"],["186","Eugene, OR","154","26","152","239"],["187","High Point, NC","138","178","219","38"],["188","Kansas City, KS","244","226","85","66"],["189","El Paso, TX","83","126","240","149"],["190","Boise, ID","81","226","191","195"],["191","Rockford, IL","226","101","72","231"],["192","Brownsville , TX","93","126","240","149"],["193","Newark, NJ","146","101","198","146"],["194","Grand Prairie, TX","96","126","240","149"],["195","Nashville, TN","200","26","155","179"],["196","Elizabeth, NJ","142","101","198","160"],["197","Tucson, AZ","229","126","62","248"],["198","Albuquerque, NM","256","126","74","176"],["198","Tacoma, WA","252","126","67","198"],["200","Hialeah, FL","79","178","222","202"],["201","Oxnard, CA","112","26","212","258"],["202","Anchorage, AK","228","178","177","3"],["203","Wilmington, NC","189","178","154","137"],["204","Denton, TX","68","126","240","243"],["205","West Palm Beach, FL","204","26","222","74"],["206","Clearwater, FL","171","178","222","40"],["207","Reno, NV","141","93","195","212"],["208","Jackson, MS","215","178","98","205"],["209","Columbus, GA","209","26","166","186"],["210","Phoenix, AZ","175","126","183","158"],["211","Miami Gardens, FL","161","178","222","74"],["212","New Orleans, 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FL","213","178","222","74"],["233","Modesto, CA","231","26","180","225"],["234","South Bend, IN","218","178","178","159"],["235","Salinas, CA","177","26","240","226"],["236","Mobile, AL","156","226","205","209"],["237","Pasadena, TX","148","126","240","228"],["238","Orlando, FL","245","178","217","47"],["239","West Valley, UT","199","178","174","229"],["240","San Antonio, TX","227","14","206","219"],["241","St. Petersburg, FL","230","178","222","74"],["242","Odessa, TX","214","126","239","108"],["243","Salt Lake City, UT","255","26","174","229"],["244","Fort Lauderdale, FL","234","178","222","74"],["245","Kansas City, MO","251","226","160","143"],["246","Stockton, CA","239","26","182","257"],["247","Laredo, TX","129","126","260","261"],["248","Dallas, TX","222","126","212","174"],["249","Springfield, MO","259","226","99","260"],["250","St. Louis, MO","261","226","160","143"],["251","Abilene, TX","196","126","208","259"],["252","Oklahoma City, OK","241","226","193","133"],["252","Memphis, TN","260","178","155","203"],["254","Lubbock, TX","219","126","193","246"],["255","Beaumont, TX","221","14","236","235"],["256","Wichita, KS","249","226","150","219"],["257","Amarillo, TX","210","126","209","255"],["258","Shreveport, LA","224","121","236","232"],["259","Tulsa, OK","258","226","191","187"],["260","Houston, TX","240","126","238","217"],["261","Miami, FL","212","178","259","207"]],"footnote":"","hasMarginBottom":true,"isExpandable":true,"isSortable":false,"maxWidth":"1215","minWidth":"100%","showSearch":false,"sortColumnIndex":0,"sortDirection":"asc"}

The Top (and Bottom) Five Cities for...

Next, let's look at how the cities in our study ranked in important categories — crime and healthcare — as well as how cities with 500,000 or more residents stacked up.

Crime

Crime is the most direct factor affecting personal safety. Women constitute slightly more than half of violent crime victims but represent a minority of offenders. The U.N. estimated that one in three women will experience physical or sexual violence at some point in their lives. Below, we have broken down the safest and least safe cities by their ValuePenguin crime score. This crime score was reached by analyzing crime statistics as well as conviction rates and police strength (factors 1-13 listed in the methodology section below).

Rank
City
Crime Score
1Naperville, IL141
2Cary, NC217
3Woodbridge, NJ233
4Irvine, CA245
5Port St. Lucie, FL250
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Rank
City
Crime Score
261St. Louis, MO1,939
260Memphis, TN1,836
259Springfield, MO1,829
258Tulsa, OK1,822
257Detroit, MI1,805
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Healthcare

Health and healthcare is also an important factor when considering safety. Poor access to healthcare can significantly impact well-being and longevity, whilst environmental factors can be as significant or even more significant than lifestyle for some diseases. Whilst the most important indicator for an individual woman's access to healthcare may be personal wealth, the availability of healthcare to the general female population impacts the welfare of this demographic significantly. Below, we have broken out the safest and least safe cities by their ValuePenguin health score, arrived at by analyzing cancer rates, access to women's clinics and the female uninsured rate (factors 21-25 in the methodology section below).

Rank
City
Health Score
1Des Moines, IA28
2Cedar Rapids, IA30
3Boulder, CO45
4Madison, WI51
5Concord, CA57
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Rank
City
Health Score
261Sandy Springs, GA315
260Laredo, TX299
259Miami, FL286
258Savannah-Chatham, GA285
257Pasadena, TX282
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Big Cities

An interesting result from this analysis comes from looking at a city's overall score versus its population. The majority of the cities ranked as the safest have populations less than 200,000. It can be deduced that larger cities are, in general, less safe. Below we have broken out the larger of the cities in the U.S. — those with a population of more than 500,000 — to see which among these is the safest and least safe.

Rank
City
1New York, NY
2San Jose, CA
3San Diego, CA
4Baltimore, MD
5Washington, DC
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Rank
City
26Houston, TX
25Oklahoma City, OK
24Memphis, TN
23Dallas, TX
22San Antonio, TX
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Experts' Take

To vary our coverage of the best cities for new graduates, we put the data aside and sought out an expert for answers to five questions of concern.

Janet Heller is a professor and founder of Primavera in Michigan.

Janet Heller headshot

1. What factors should be considered when determining the safety of a city for women? Good lighting of streets, a good mass transportation system, strong housing policies and a good police system sensitive to harassment of women are all important safety factors. Adequate street lighting makes it harder for people with bad intentions to hide. A good mass transportation system enables women to travel with groups -- rather than being alone -- to and from work and other activities. Intelligent housing policies that require adequate doors, locks and windows can make it very difficult for an intruder to break into an apartment or a home. A good police system will patrol high-crime areas sufficiently and respond immediately to requests for help. A good police department will also have many women officers, who tend to be more sympathetic to and aware of safety issues for women. I also believe that a community sensitive to GLBTQ issues will create a safer environment for women. Everyone deserves to feel safe, regardless of gender, gender identity, and sexual orientation.

2. What are the most commonly overlooked issues that affect the safety of an area for women? Some people believe that any woman who travels around at night is asking for trouble. However, many jobs require some night hours: for example, hospital night shifts, evening classes at colleges, department stores open at night, et cetera. If we want to offer equal opportunity to women in the workforce, we need to make it safe for them to move around at night. Also, many people go out to eat, exercise, hear a speaker, attend a place of worship or see movies at night. Women may be alone or with family members, friends or coworkers. No one deserves to be a victiim for trying to have a full life that includes night activities.

3. How can public policy affect women's safety? I feel strongly that all girls and women should learn self-defense in elementary schools, high schools, colleges and workplaces. When I was attacked from behind in Chicago one night, I froze because I had never received training about how to counter an assailant. As a result, I lost my purse, suffered a blow to the head, and got pushed down hard, which permanently damaged a disk in my back. I was in pain for months due to this attack.

After consulting with friends, I took a practical self-defense class designed for women. I learned where people are most vulnerable to jabs and kicks: eyes, nose, ears, throat, fingers, knees and insteps. I also learned how to block blows aimed at me and to break away from different kinds of holds. The class members discussed verbal strategies for calming would-be robbers and attackers. We practiced our skills until we could react rapidly and effectively. This course gave me more confidence and knowledge of self-defense.

4. How are perceptions changing regarding women's safety? I think that many people now realize that all members of our society must be able to feel safe, work in safe environments, and travel safely at any time of the day or night. Also, the old assumption that all women have a father or brother or husband to support and protect them is crumbling. We now have more women living alone, more women choosing women partners, and more women heading single-parent families. Public policies must reflect the current reality, not a fantasy world.

5. What are the trends in women's safety issues, and what do you think the major issues will be 10 years from now? We now have many classes in self-defense offered in most cities. These include judo, kung fu, aikido, karate, tae kwan do, kobudo, jiu jitsu, et cetera. More and more women are learning the martial arts. In 2026, I anticipate that most schools will offer classes in self-defense as part of the physical education curriculum.

More workplaces have strong sexual harassment policies and are enforcing them. I anticipate that all workplaces will have such policies in 2026 and will fire employees who engage in inappropriate verbal, physical or psychological harassment of coworkers.

More houses of worship now have sexual harassment policies. However, current policies focus on the conduct of church, synagogue, or mosque employees, not all congregants. I predict that in 2026, houses of worship will insist that anyone who enters must respect all other individuals and refrain from harassment of any type. I think that those who persistently abuse women and other worshippers will be immediately expelled.

Methodology

In order to determine the safest cities for women, we looked at 31 factors with data taken from 11 reputable sources. These factors were grouped into four categories: crime; healthcare; public policy and representation; and education and wealth. The latter two categories — public policy and representation and education and wealth — were considered secondary factors rather than primary factors in affecting the safety of a city for women.

Education and wealth indicate a woman’s mobility and ability to avoid — or remove herself — from potentially dangerous situations, and public policy and representation indicate how well women’s rights are protected. Those cities with strong female-friendly workplace policies allow women to better care for themselves, their health and their safety. As second-order effects, these categories were given smaller weights in the final rankings than crime and healthcare.

Some non-gender specific crime statistics were included in the analysis; these statistics indicate crimes that affect both men and women, but have been included both because they affect women living in the city and because women are more likely to be victimized during non-gender specific crimes, and statistics of these crimes for only female victims is unavailable.

All factors were ranked from the safest (one) to the least safe. Null values were set to zero. Some factors only had state-wide data rather than city-by-city data; in these instances, we included the data for the relevant state. All data was normalized by population.

There were several factors we would have liked to include in this analysis that we simply could not wrangle. For example, street harassment is an important issue that affects the majority of women at some point in their lives, and it’s one that can seriously impact how safe a woman feels, but it is inherently an unreported crime. An excellent study on this issue was performed by Stop Street Harassment. Public transit safety was another such issue for which we could not gather data.

Every woman is going to have different considerations when deciding whether a city is safe or not; by incorporating 31 different factors, we have attempted to capture the most important issues. Safety can be viewed as a secondary factor to wealth, and as such this analysis has attempted to capture women’s social mobility and choice in an area as well as more straightforward factors such as crime.

This analysis varies from a previous ValuePenguin study performed in 2014 on the safest cities for women by addressing health as a fundamental component of safety, as well as including secondary factors that may affect safety, rather than focussing purely on crime statistics.

Below, we break down the statistics we used and their point of origin. Those items marked with an asterisk used state data rather than city data. Each of the sources to the right, so that you can click into the datasets as we originally found them.

Crime (13)

  1. Rape Rate (per 100,000 Women) / FBI
  2. Robbery Rate (per 100,000 Population) / FBI
  3. Assault Rate (per 100,000 Population) / FBI
  4. Burglary Rate (per 100,000 Population) / FBI
  5. Larceny Rate (per 100,000 Population) / FBI
  6. Motor Vehicle Theft Rate (per 100,000 Population) / FBI
  7. Arson Rate (per 100,000 Population) / FBI
  8. Stalking (% of Population, Lifetime Prevalence)* / CDC
  9. Non-Rape Sexual Violence (% of Population, Lifetime Prevalence)* / CDC
  10. Homicide Rate (Male on Female, per 100,000 women)* / Violence Policy Center
  11. Conviction Rate* / U.S. Justice Department
  12. Sex Offenders (% of Population)* / Parents for Megan's Law
  13. Number of Police Officers (per 100,000 Population) / U.S. Bureau of Justice Statistics
  14. Women as a % of Police Force / U.S. Bureau of Justice Statistics

Public Policy and Representation (8) - Half-weighted in final rankings

  1. Safety Resources* / U.S. Department of Women's Health
  2. Paid Family Leave* / National Partnership for Women and Families
  3. Pregnancy Accommodations at Work* / National Partnership for Women and Families
  4. Right to Pump* / National Partnership for Women and Families
  5. Pregnancy Leave* / National Partnership for Women and Families
  6. Paid Sick Leave* / National Partnership for Women and Families
  7. State Workers Pregnancy Accommodations* / National Partnership for Women and Families
  8. Number of Female Leaders* / Center for American Women and Politics

Healthcare (5)

  1. Reproductive Clinics (per 100,000 Population)* / Planned Parenthood
  2. Breast Cancer Rates (per 100,000 Women)* / CDC
  3. Ovary Cancer Rates (per 100,000 Women)* / CDC
  4. Cervix Cancer Rates (per 100,000 Women)* / CDC
  5. Uninsured Rate (% of Women) / U.S. Census Bureau

Education and Wealth (4) - Half-weighted in final rankings

  1. % Women with High School Diploma / U.S. Census Bureau
  2. % Women with College Education / U.S. Census Bureau
  3. % Women With Post Graduate Education / U.S. Census Bureau
  4. Median Income for Women / U.S. Census Bureau

Justin is a Sr. Research Analyst at ValuePenguin, focusing on small business lending. He was a corporate strategy associate at IBM.