= Solution
Each row of the <contingency table> is a <conditional distribution> of the safety category among respondents with that experience status. Thus the relevant comparison uses row <percentages>, rather than interpreting the larger number of respondents with some bad experiences as itself an effect on safety.
The <sample proportions> reporting feeling very safe are $39.59\%$ without bad experiences and $32.78\%$ with some bad experiences: the latter is lower by about $6.81$ <percentage points>. Feeling fairly safe rises from $28.49\%$ to $34.63\%$, a difference of about $6.14$ <percentage points>. Feeling unsafe changes only from $31.92\%$ to $32.59\%$, about $0.67$ <percentage points>. The main descriptive shift is therefore from \b[very safe toward fairly safe], rather than a large increase in the unsafe category.
<Pearson's chi-squared test> tests the <null hypothesis> that experience status and safety category are <independent>. If that were true, the expected cell count would be its row total times column total divided by $n$. The <Pearson chi-squared statistic> sums squared observed-minus-expected count differences divided by their expected counts. Here
$$
\boxed{X^2=10.84662,\qquad \mathrm{df}=(2-1)(3-1)=2,\qquad p=0.004412523.}
$$
The expected counts are comfortably large, with minimum approximately $234.7$, so the ordinary large-sample <chi-squared distribution> approximation is appropriate under the sampling assumptions. There is evidence against <independence> even at the $1\%$ <significance level>. The reported likelihood-ratio result gives the same conclusion.
Magnitude is a separate question. <Cramér's V> gives
$$
V=\sqrt{\frac{10.84662}{1807}}\simeq0.07748,
$$
which describes a weak <statistical association>. It does not <mean> that $7.7\%$ of safety is explained. Nor does the <p-value> measure the size of that association.
For ordinal direction, order the experience categories as none, some, and the safety categories from very safe toward unsafe. <Goodman and Kruskal's gamma> compares <concordant pairs> with <discordant pairs> after excluding tied pairs:
$$
\gamma=\frac{282244-242582}{282244+242582}\simeq0.07557.
$$
Its small positive sign then corresponds to a weak tendency toward less safety among respondents with bad experiences. The sign would reverse if exactly one category ordering were reversed. Because this <contingency table> has many ties, the response-direction <Somers' D>, which retains response ties among pairs with different predictor values, is smaller:
$$
D_{Y\mid X}=\frac{39662}{524826+261384}\simeq0.05045.
$$
These coefficients describe ordered association; the omnibus <Pearson's chi-squared test> tests any departure from <independence> and does not supply a separate <p-value> for gamma.
The column-dependent <Goodman and Kruskal's lambda> makes the weak predictive improvement tangible. Predicting the overall most frequent safety category makes $1165$ errors in this table. Using a separate most frequent category within each experience row makes $1145$, giving $\lambda=20/1165\simeq0.0172$, only about a $1.7\%$ reduction in those classification errors.
\b[The sample supports a statistically detectable but weak association, chiefly a reduced <sample proportion> feeling very safe.] This <observational study> does not establish that bad experiences cause the change: reverse direction, reporting differences or <confounding> could also contribute. The numerical <p-value> also presumes the sampling and complete-case assumptions appropriate to the observations used.
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