Notebooks
In this section, you can find a few initial raw technical notebooks related to the site. (They are presented as-is; please excuse stray typos and jargon.) These notebooks attack problems on the ground level (without using any existing specialized modules) to see how things really work before we settle down on any specific path. For the tech-savvy, these samples may provide initial insights for their own experiments and exploration. For those who are technology students, working on your own through these challenges is a fun learning journey in itself – from algorithms to statistical analysis, you will encounter the full spectrum of complexities required to engineer a solution.
- Astro-AI
- Astro-Hypothesis-Test
- Chart-Reader-1-Intro
- Chart-Reader-2-Evaluation
- Chart-Reader-3-Tasks
- Chart-Reader-4-Agent
- Magic-Mirror
- Nickname-ID
( Production level solutions are included in our member-onboarding crash-course. )
Astrology: any empirical evidence?
Astrologers look for patterns: for a specific astrological aspect (a meaningful angle between two celestial entities), we can look back into history to see what happened when that aspect was active. If we see a pattern – there it is.
It is nearly impossible to pull together a rigorous statistical hypothesis test for a specific astrological influence, as these influences never occur in isolation. Another important distinction is synchronicity vs. causality. Astrology does not claim to predict specific events, but rather the magnitude and "flavor" of turbulence vs. harmony, which usually correlate with the types of events we aim to predict. Synchronicity is correlational, not causal, by definition. Let's take a look.
In this notebook, we take two public datasets and cross-reference them with the ephemerides. We will focus on the slow-moving planets (their natal influence is shared by generations and their transits last longer) and the major aspects (which possess stronger effects and wider orbs).
import os import re import numpy as np import pandas as pd from scipy.stats import probplot, ttest_ind from datetime import datetime, timezone, timedelta from IPython.display import display, HTML from matplotlib import pyplot as plt from dotenv import load_dotenv load_dotenv('.nbenv')Gemma will consult us on the current Internet-Astrology state of mind.
gem = 'gemma3:12b' import os from ollama import Client from IPython.display import display, Markdown client = Client(host=os.environ['OLLAMA_HOST']) def ai_response(prompt, model, show_output=True): response = client.chat(model=model, messages=[{ 'role':'user', 'content':prompt }]) if show_output: display(Markdown(response.message.content)) return response.message.contentPublic Datasets
What kind of data could be useful?
The planets beyond the Jupiter are "malefic" in a traditional astrological sense, and also "slow moving" – with longer orbits (far from the Sun). Just what we need here: in relation to social dynamics "malefic" and "slow" are easier to track from the statistical perspective. For this exercise we take:
pd.read_csv('data/archive/accident-stats-CA.csv.gz').columns Index(['Severity', 'Start_Time', 'Description', 'City', 'County', 'Temperature(F)', 'Humidity(%)', 'Pressure(in)', 'Visibility(mi)', 'Wind_Speed(mph)', 'Precipitation(in)', 'Weather_Condition', 'Sunrise_Sunset', 'Civil_Twilight'], dtype='object')- LA Crime Dataset (LAPD Records)
Ephemerides
For the planetary positions calculation let's use pyswisseph wrapper of Swiss-Efemerides.
To quantify astrological influence we can use the difference between the actual angle and the aspect value normalized by the value of the aspect orb (radius of the influence): when the angle is within the aspect orb -- this yields the number in [0, 1] interval; the influence is strongest =1 when the angle is equal to the aspect value and falls to 0 when the angle is orb-away. And, we can use positive numbers for the beneficial and negative for the difficult influences.
Let's calculate the timeline covering the date-range of our datasets. For this simple exercise we just need the daily angular positions on the Zodiac-Wheel for our target objects. The zero universal time is good enough – the day-time in US where our datasets are from. Eclipses could be interesting, they are known to affect societal dynamics, but won't give us sufficient data within the range of our target datasets. SUN makes a degree per day, and MOON makes about 14. Let's calculate planetary positions at 00:00 UTC and mark eclipses.
from app.astro import get_year_data, get_eclipses
ephemerides = []
for year in range(2016, 2026):
positions, timeline = get_year_data(year)
positions = pd.DataFrame.from_dict(positions)
positions['date'] = timeline
positions = positions.set_index('date')
ephemerides.append(positions)
ephemerides = pd.concat(ephemerides)
ephemerides.to_csv('data/archive/astro-positions.csv')
eclipses = get_eclipses(ephemerides, angles)
eclipses = pd.DataFrame(list(eclipses.values()), index=list(eclipses.keys()), columns=['target','time'])
eclipses.to_csv('data/archive/astro-eclipses.csv')
| SUN | MOON | MERCURY | VENUS | MARS | JUPITER | SATURN | URANUS | NEPTUNE | PLUTO | RAHU | eclipse | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| date | ||||||||||||
| 2016-01-01 | 279.98 | 176.70 | 299.28 | 242.06 | 208.56 | 173.15 | 251.14 | 16.57 | 337.55 | 285.05 | 175.61 | NaN |
| 2016-01-02 | 281.00 | 188.53 | 299.94 | 243.27 | 209.11 | 173.18 | 251.25 | 16.58 | 337.57 | 285.08 | 175.56 | NaN |
| 2016-01-03 | 282.02 | 200.33 | 300.46 | 244.49 | 209.67 | 173.19 | 251.36 | 16.58 | 337.59 | 285.12 | 175.50 | NaN |
| 2016-01-04 | 283.04 | 212.18 | 300.82 | 245.70 | 210.22 | 173.21 | 251.47 | 16.59 | 337.62 | 285.15 | 175.45 | NaN |
| 2016-01-05 | 284.06 | 224.19 | 301.02 | 246.92 | 210.77 | 173.22 | 251.57 | 16.60 | 337.64 | 285.19 | 175.40 | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2025-12-27 | 275.47 | 355.51 | 261.10 | 272.91 | 278.86 | 112.00 | 355.90 | 58.09 | 359.45 | 302.57 | 342.44 | NaN |
| 2025-12-28 | 276.49 | 9.04 | 262.60 | 274.17 | 279.63 | 111.87 | 355.95 | 58.06 | 359.46 | 302.60 | 342.38 | NaN |
| 2025-12-29 | 277.51 | 22.94 | 264.10 | 275.43 | 280.39 | 111.74 | 356.00 | 58.03 | 359.47 | 302.63 | 342.33 | NaN |
| 2025-12-30 | 278.53 | 37.21 | 265.61 | 276.69 | 281.16 | 111.62 | 356.05 | 58.01 | 359.48 | 302.66 | 342.28 | NaN |
| 2025-12-31 | 279.55 | 51.83 | 267.13 | 277.95 | 281.92 | 111.49 | 356.11 | 57.98 | 359.49 | 302.69 | 342.22 | NaN |
3653 rows × 12 columns
angles = pd.DataFrame(index=ephemerides.index) for i in range(len(OBJECTS)): for j in range(i + 1, len(OBJECTS)): a, b = OBJECTS[i], OBJECTS[j] angles[f'{a}-{b}'] = (ephemerides[a] - ephemerides[b]).abs() angles[f'{a}-{b}'] = angles[f'{a}-{b}'].apply(lambda x:min(x, 360 - x)) aspects = pd.DataFrame(-np.ones((len(angles), len(angles.columns))), columns=angles.columns, index=angles.index) strength = pd.DataFrame(np.zeros((len(angles), len(angles.columns))), columns=angles.columns, index=angles.index) for aspect, orb in ASPECTS.items(): value = (aspect - angles).abs()/orb mask = value > 1. # no influence aspects = aspects.where(mask, other=aspect) d = -1 if aspect in (90, 150, 180) else 1 # tension vs. flow strength += ((1 - value).where(value < 1).fillna(0) ** 2) * d # signed squre of distance from the aspect strength = strength.where(aspects != -1, other=0) aspects = aspects.where(aspects != -1, other=None)Next, let's give all of those a sentiment according to their astrological definition: tense aspects (squares and oppositions) are negative while harmonious (trines and sextiles) are positive. The conjunction is the most powerful but "undefined" in this simplistic way. In individual context a conjunction is mostly beneficial (high potential), even with the malefic entities, it could be very positive in the presence of awareness and focused conscious effort. In the context of the crowd malefic conjunctions are almost certain negative.
malefic = set(('MARS','SATURN','URANUS','NEPTUNE','PLUTO')) malefic = [x for x in strength.columns if len(set(x.split('-')).intersection(malefic)) > 0] for x in malefic: strength[x] = strength[x].where(aspects[x]!=0, other=-strength[x].where(aspects[x]==0)) aspects.to_csv('data/archive/astro-aspects.csv') strength.to_csv('data/archive/astro-strength.csv')Now we can define our astro-background: a simplified numeric measure for overall combined influence of a day.
Using the same weight for all influences may seem as oversimplification,
but justified for this introductory exercise,
as a cluster of aspects is usually more potent than a single aspect.
It might be good enough for our simple goal here, mainly a categorical filter with values
NEGATIVE, NEUTRAL, POSITIVE
characterizing the level of astrological influence for a specific day.
# plot some examples
pairs = ('SATURN-PLUTO','URANUS-PLUTO','SATURN-NEPTUNE')
fig, ax = plt.subplots(3, 1, figsize=(9, 9))
x = pd.to_datetime(strength.index)
for i, pair in enumerate(pairs):
ax[0].plot(x, strength[pair].values, label=pair)
ax[0].set_title('Aspect strength timeline sample (color: planets)')
ax[0].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False)
legend = set()
color = {0:'magenta', 180:'crimson', 120:'lime', 90:'purple', 60:'green', 150:'steelblue'}
for pair in pairs:
for aspect in ASPECTS:
seg = strength.loc[aspects[pair]==aspect, pair]
if len(seg) == 0:
continue
ax[1].plot(pd.to_datetime(seg.index), seg.values, color=color[aspect],
label=None if aspect in legend else aspect)
legend.add(aspect)
ax[1].set_ylim([-1.1, 1.1])
ax[1].set_title('Aspect strength timeline sample (color: aspect)')
ax[1].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False)
margin = 0.5 # radius of low-influence zone
N = background < -margin
P = background > 0
Z = (background >= -margin) & (background <= 0)
ax[2].scatter(pd.to_datetime(background[P].index), background[P].values, s=3, color='#494', label='positive')
ax[2].scatter(pd.to_datetime(background[N].index), background[N].values, s=3, color='#944', label='negative')
ax[2].scatter(pd.to_datetime(background[Z].index), background[Z].values, s=3, color='#888', label='neutral')
ax[2].axhline(y=0, linestyle=':', linewidth=0.5, color='black')
ax[2].set_title('Astro-Background timeline')
ax[2].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False)
fig.set_facecolor('none')
for i in range(len(ax)):
ax[i].set_facecolor('none')
ax[i].tick_params(axis='both', which='major', labelsize=8)
plt.tight_layout()
US Traffic Accident Dataset
Unfortunately, no overall traffic volume data is provided: with more traffic we would expect more accidents on average. For our specific purpose the rate of accidents holds more information than the accidents volume. To construct our main metric we can go for the daily distribution of the accident-severity instead.
for x in os.listdir('data/archive'): if x.startswith('accident-stats-'): print(x) accident-stats-CA.csv.gz accident-stats-MN.csv.gz accident-stats-TX.csv.gz accident-stats-NY.csv.gz accident-stats-FL.csv.gz accident-stats-NC.csv.gz accident-stats-VA.csv.gz accident-stats-PA.csv.gz accident-stats-OR.csv.gz accident-stats-SC.csv.gz accidents = pd.read_csv('data/archive/accident-stats-CA.csv.gz') cities = accidents.groupby('City').size().sort_values(ascending=False).head(4).index.tolist() cities ['Los Angeles', 'Sacramento', 'San Diego', 'San Jose'] accidents = accidents.loc[accidents['City'].isin(cities)].reset_index(drop=True) #for x in accidents.columns: # print(f'------------------------------------------\n\t{x}\n------------------------------------------') # print(accidents.groupby(x).size().sort_values(ascending=False))Here comes a note on one key difference between astrology and science. The weather condition is known to affect the accidents rate and severity (predictive feature). Based on astrological influence, in contrast, we would expect a certain level of difficulty, the areas where difficulties may arise are more or less wide spectrum (synchronicity-based inference). The features like weather relate to influence manifestation, not the influence magnitude.
In a traditional statistical sense we would compare two populations (like bad-weather-accidents vs. fair-weather-accidents) and see if they are different: let's go for a population under malefic influence vs. population under beneficial influence for the maximum contrast.
#weather = (accidents['Weather_Condition']=='Fair')&(accidents['Precipitation(in)']==0) #light = (accidents['Civil_Twilight']=='Day') #accidents = accidents.loc[ weather & light ].reset_index() accidents['date'] = accidents['Start_Time'].apply(lambda x:x.split()[0]) accidents['month'] = accidents['date'].apply(lambda x:'-'.join(x.split('-')[:-1])) accidents['week'] = accidents['date'].apply(lambda x:datetime(*[int(d) for d in x.split('-')]).strftime('%Y-%W')) timeline = accidents.groupby('date').size().index.to_list() fig, ax = plt.subplots(2, 1, figsize=(9, 8)) for i, (d, n) in enumerate(zip(('week','month'),(7, 30))): for city in cities: stats = accidents[accidents['City']==city].groupby(d).size() m, s = stats.mean(), stats.std() stats = (stats - m)/s x = pd.to_datetime(timeline[::n][:len(stats)]) ax[i].plot(x, stats.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label=city) ax[i].axhline(y=0, linestyle=':', linewidth=0.5, color='gray') ax[i].set_title(f'Accidents {d}ly volume (standardized)') ax[i].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False) ax[i].set_facecolor('none') ax[i].tick_params(axis='both', which='major', labelsize=8) fig.set_facecolor('none') plt.show()
There seems to be some quite dramatic procedural changes in the fall of 2019. (?)
That was preceding a very interesting astrological point at early 2020: several astrologers predicted a potential global pandemic (SATURN-PLUTO conjunction) while many others were "distracted" with political dynamics in the US (the same SATURN-PLUTO issue). Both turned right.
fig, ax = plt.subplots(2, 1, figsize=(9, 8)) for i, (d, n) in enumerate(zip(('week','month'),(7, 30))): for city in cities: stats = accidents[accidents['City']==city].groupby(d)['Severity'].mean() m, s = stats.mean(), stats.std() stats = (stats - m)/s x = pd.to_datetime(timeline[::n][:len(stats)]) ax[i].plot(x, stats.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label=city) ax[i].axhline(y=0, linestyle=':', linewidth=0.5, color='gray') ax[i].set_title(f'Accidents {d}ly avg. severity (standardized)') ax[i].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False) ax[i].set_facecolor('none') ax[i].tick_params(axis='both', which='major', labelsize=8) fig.set_facecolor('none') plt.show()
We cannot use t-test with this timeseries in a strict sense, but negative and positive days seem evenly distributed all along the timeline: we might still be able to compare the distributions in a fair way, at least visually. Let's go for it and make a standard test routine:
- separate negative and positive populations
- remove outliers
- check if remaining size is sufficient
- run 2-samples t-test using full populations to ensure similar timeline coverage
- make some visuals, as a t-test is rather weak here...
Let's also check if correlation shows up (spearman or kendall).
We calculate 3 columns:
- test: our main metric
- other: related metric to compare
- background: target influence (test) correlation with the astro-background shows which is dominant
Then, we display overall correlation stats along with the top influences to help our reading of the composite one.
def run(test, other, label, margin=1., borders=[.25, .75], order=lambda corr:corr['test'].abs()): test /= test.std() other /= other.std() astro = strength.loc[test.index] astbg = background.loc[test.index] print(f'--------------------------------------------\n\t{label}: {len(test)}\n' '--------------------------------------------') print(f"astro-background: test {test.corr(astbg,'spearman'):.2%} / " f"other {other.corr(astbg,'spearman'):.2%}") test_hypothesys(test, astbg, margin, borders) def get_corr(s1, s2): if s1.sum() == 0 or s2.sum() == 0: # no aspects return return s1.corr(s2, method='spearman') corr = [(get_corr(test, astro[x]), get_corr(other, astro[x]), get_corr(astro[x], astbg), x) for x in astro.columns] columns = ['test','other','background','influence'] corr = pd.DataFrame(corr, columns=columns).set_index('influence').dropna() corr['ord'] = order(corr) print('\nCorr. stats:') display(HTML(corr.describe().loc[['std','min','mean','max']]\ .style.background_gradient('coolwarm', axis=None).to_html())) print('\nTop influence (std units):') corr = corr.sort_values('ord', ascending=False)/corr.std() # show all beyond std display(HTML(corr[(corr['test'] > 1)|(corr['test'] < -1)|(corr['ord'] > 1)]\ .style.background_gradient('coolwarm', axis=None).to_html())) print('\n') margin = .75 borders = [0.2, 0.8] order = lambda corr:corr['test'].abs() for city in cities[::-1]: stats = accidents[accidents['City']==city].groupby('date').size() # construct qualitative metric test = accidents[accidents['City']==city].groupby('date')['Severity'].mean() #test += np.random.rand(len(test))/2 # add o.5 random uniform to remove "steps" run(test, stats, city, margin, borders, order) stats = accidents.groupby('date').size() test = accidents.groupby('date')['Severity'].mean() #test += np.random.rand(len(test))/2 # add o.5 random uniform to remove "steps" run(test, stats, 'all', margin, borders, order) -------------------------------------------- San Jose: 2474 -------------------------------------------- astro-background: test 5.53% / other -0.36% Sample size: negative 719 positive 463
p-value: 0.000000 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.125404 | 0.045348 | 0.093173 | 0.107941 |
| min | -0.395460 | -0.117360 | -0.132185 | 0.000372 |
| mean | -0.011692 | 0.002776 | 0.096089 | 0.064195 |
| max | 0.534349 | 0.087367 | 0.260659 | 0.534349 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| SATURN-URANUS | 4.261020 | -2.587978 | 1.603101 | 4.950365 |
| URANUS-PLUTO | -3.153491 | 0.943021 | -0.450571 | 3.663660 |
| NEPTUNE-PLUTO | -2.183746 | -1.329295 | -0.863435 | 2.537031 |
| JUPITER-SATURN | 1.814108 | -0.296054 | 2.473683 | 2.107593 |
| URANUS-NEPTUNE | -1.671488 | 0.857008 | -0.170995 | 1.941899 |
| JUPITER-URANUS | -1.415447 | 0.236383 | 1.164291 | 1.644437 |
| MARS-URANUS | -0.958138 | 1.751658 | 1.226962 | 1.113145 |
p-value: 0.000014 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.137306 | 0.084935 | 0.092607 | 0.120457 |
| min | -0.439146 | -0.359447 | -0.129374 | 0.000172 |
| mean | -0.017394 | 0.012471 | 0.096648 | 0.067447 |
| max | 0.619017 | 0.213887 | 0.259588 | 0.619017 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| SATURN-URANUS | 4.508305 | -4.231998 | 1.574372 | 5.138899 |
| URANUS-PLUTO | -3.198303 | 2.518236 | -0.358172 | 3.645662 |
| NEPTUNE-PLUTO | -2.021382 | 1.377990 | -0.824879 | 2.304120 |
| URANUS-NEPTUNE | -1.776068 | 2.000014 | -0.162840 | 2.024494 |
| JUPITER-URANUS | -1.514236 | 0.560770 | 1.221932 | 1.726038 |
| JUPITER-SATURN | 1.360931 | -0.009374 | 2.476173 | 1.551289 |
p-value: 0.006884 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.130528 | 0.082259 | 0.092620 | 0.111123 |
| min | -0.400514 | -0.374670 | -0.133170 | 0.000453 |
| mean | -0.011517 | 0.004086 | 0.096227 | 0.068684 |
| max | 0.561425 | 0.216524 | 0.258729 | 0.561425 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| SATURN-URANUS | 4.301197 | -4.554784 | 1.601923 | 5.052275 |
| URANUS-PLUTO | -3.068427 | 2.632242 | -0.383958 | 3.604238 |
| NEPTUNE-PLUTO | -2.327250 | 0.924322 | -0.805732 | 2.733636 |
| JUPITER-SATURN | 1.609905 | -1.778498 | 2.513984 | 1.891027 |
| URANUS-NEPTUNE | -1.609023 | 1.567116 | -0.172932 | 1.889991 |
| JUPITER-URANUS | -1.381394 | 0.504857 | 1.195202 | 1.622614 |
| JUPITER-PLUTO | 0.870325 | -0.125514 | 1.536941 | 1.022302 |
p-value: 0.000000 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.138533 | 0.088150 | 0.092963 | 0.120430 |
| min | -0.452475 | -0.396345 | -0.131012 | 0.000084 |
| mean | -0.016708 | 0.005441 | 0.095984 | 0.069735 |
| max | 0.611137 | 0.250994 | 0.260775 | 0.611137 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| SATURN-URANUS | 4.411478 | -4.496277 | 1.588843 | 5.074613 |
| URANUS-PLUTO | -3.266180 | 2.847370 | -0.431122 | 3.757153 |
| NEPTUNE-PLUTO | -2.006380 | 1.294645 | -0.842595 | 2.307980 |
| URANUS-NEPTUNE | -1.889447 | 1.519339 | -0.168317 | 2.173470 |
| JUPITER-SATURN | 1.331963 | -1.487525 | 2.504569 | 1.532185 |
| JUPITER-URANUS | -1.294198 | 0.855261 | 1.160249 | 1.488742 |
p-value: 0.000000 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.144229 | 0.088043 | 0.092948 | 0.125169 |
| min | -0.459390 | -0.403621 | -0.130501 | 0.002808 |
| mean | -0.015891 | 0.006696 | 0.095935 | 0.072617 |
| max | 0.642030 | 0.233731 | 0.260094 | 0.642030 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| SATURN-URANUS | 4.451466 | -4.584381 | 1.588809 | 5.129317 |
| URANUS-PLUTO | -3.185147 | 2.654747 | -0.428545 | 3.670168 |
| NEPTUNE-PLUTO | -2.054401 | 1.076570 | -0.844066 | 2.367237 |
| URANUS-NEPTUNE | -1.844104 | 1.631600 | -0.169460 | 2.124917 |
| JUPITER-SATURN | 1.518224 | -1.288112 | 2.500660 | 1.749413 |
| JUPITER-URANUS | -1.323608 | 0.636373 | 1.160944 | 1.525161 |
Finally, let's ask our AI consultant Gemma...
res = ai_response("""In astrology, which planetary aspects would signify potential accidents? Consider effect on community statistics, not the natal chart context. Give a list ordered by strength of the influence. Exampe: 1. Planet A - Planet B <short explanation> 2. Planet C - Planet D <short explanation> 3. ... """, gem)Okay, let's explore planetary aspects, viewed through the lens of potential accident-proneness within community statistics (not individual natal charts). This means looking at their broad astrological influence and how they might correlate with increased incidents – not definitively causing them, but highlighting periods of increased vulnerability. It's crucial to understand that this is a speculative and interpretive area of astrology, and statistically proving a direct causal link is extremely difficult. I'll prioritize aspects most commonly associated with heightened risk, ordered by perceived strength of influence within a community context. Important Disclaimer: This is not a substitute for safe practices or professional advice. These aspects simply highlight times when heightened caution is advisable.
Understanding the Considerations
- Slow-Moving Planets: Planets like Saturn, Jupiter, Uranus, Neptune, and Pluto are key here. Their aspects are long-lasting and affect a wide population.
- Cardinal Signs: Aspects involving Cardinal signs (Aries, Cancer, Libra, Capricorn) often indicate periods of activation, instability, and potential for sudden change, which can increase accidents.
- Tense Aspects: Conjunctions, Squares, and Oppositions are generally considered "tense" and represent conflict, friction, or challenging energies. These are more likely to be associated with increased incidents than harmonious aspects.
- Houses and Signs: The houses and signs involved in the aspect will modulate the type of accidents likely to occur (e.g., aspects to the 6th house – work, health – may indicate workplace accidents).
Planetary Aspects and Potential for Accidents (Ordered by Strength – Subjective & Contextual)
Here's a list, considering the above, with explanations geared towards community impact. Note that the actual manifestation depends on many factors.
Saturn - Uranus (Especially Conjunctions and Squares): Highest Influence
- Explanation: This is arguably the most potent combination for larger-scale disruptions and sudden shifts, often with consequences. Saturn represents structure, limitations, and responsibility; Uranus is about sudden change, rebellion, and unexpected events. The combination is explosive. Conjunctions can bring severe, prolonged issues. Squares create ongoing tension, leading to volatile situations. This pairing often signals technological failures, structural collapses, unexpected disasters, or changes in established systems that lead to unforeseen consequences. Think earthquakes, major industrial accidents, significant social upheaval, transportation issues. The prolonged nature of Saturn means these issues can simmer for years.
- Community Impact: Major infrastructure failures, significant policy changes leading to accidents, widespread disruptions.
Pluto - Uranus (Especially Conjunctions & Squares): Very High Influence
- Explanation: Pluto represents transformation, power, and destruction/reconstruction, while Uranus embodies sudden change and disruption. Their aspects indicate periods of intense upheaval, often with a feeling of being out of control. This combination can bring about seismic shifts in society, new technologies with unforeseen risks, and events that drastically alter the status quo.
- Community Impact: Technological disasters (power grid failures, cyberattacks), widespread social unrest, radical changes leading to unforeseen consequences, potentially violent events.
Saturn - Neptune (Especially Squares & Oppositions): High Influence
- Explanation: Saturn (structure, limitations, reality) opposing or squaring Neptune (illusion, confusion, boundaries). This pairing can blur the lines between reality and illusion, leading to misjudgment, poor planning, and a lack of foresight. It highlights potential for deception, negligence, or a failure to properly assess risks. Accidents can be due to poor infrastructure maintenance, lack of safety protocols, or a general sense of complacency.
- Community Impact: Increased incidents related to water (floods, drownings), maritime accidents, chemical spills, healthcare errors, accidents stemming from misinformation or deceptive practices.
Jupiter - Saturn (Especially Conjunctions & Squares): Moderate-High Influence
- Explanation: Jupiter (expansion, optimism) squaring or conjoining Saturn (restriction, limitation) often creates a sense of overreach combined with a harsh reality check. This can lead to risky endeavors, underestimation of dangers, or structural problems arising from rapid expansion. Jupiter's optimism can blind people to Saturn's warnings.
- Community Impact: Accidents related to overexpansion (building collapses due to faulty construction), financial crises that lead to economic hardship and increased risks, problems arising from poorly planned projects.
Mars – Uranus (Especially Conjunctions & Squares): Moderate Influence
- Explanation: Mars (action, aggression) clashing with Uranus (sudden change) creates a volatile combination. It suggests impulsive actions, reckless behavior, and accidents resulting from unexpected events. This aspect often manifests as a lack of caution and a tendency to act without thinking.
- Community Impact: Traffic accidents (due to aggressive driving or unexpected road conditions), accidents involving machinery, sudden bursts of violence, potential for riots.
Mars – Saturn (Especially Squares & Oppositions): Moderate Influence
- Explanation: Mars (action, energy) being blocked or challenged by Saturn (restriction, discipline). This can lead to frustration, pent-up anger, and accidents stemming from carelessness or reckless actions due to suppressed energy. It highlights a potential for frustrated attempts at action leading to unintended consequences.
- Community Impact: Workplace accidents resulting from frustration and impatience, potential for accidents involving heavy machinery or equipment.
Jupiter – Neptune (Especially Squares & Oppositions): Moderate Influence
- Explanation: Jupiter (expansion) and Neptune (illusion) create a combination that can lead to overestimation of resources, poor judgment, and a lack of awareness of potential dangers. This can lead to accidents stemming from negligence, complacency, or taking unnecessary risks.
- Community Impact: Accidents related to water or maritime activities, potential for accidents due to poor planning or inadequate safety measures.
Venus – Uranus (Especially Squares & Oppositions): Lower-Moderate Influence
- Explanation: Venus (harmony, beauty) challenged by Uranus (sudden change) can bring unexpected disruptions in areas of enjoyment, relationships, and aesthetics. This can lead to accidents caused by impulsive actions or a lack of attention to detail.
- Community Impact: Accidents related to entertainment venues, transportation mishaps, social disruptions.
Important Caveats:
- Aspect Degrees: The orb (the degree range where an aspect is considered valid) matters. Wider orbs weaken the influence.
- House Placement: Where the aspect falls within the astrological chart (houses) is incredibly important for determining the type of accidents that may occur.
- Sign Characteristics: The signs involved modify the expression of the aspect.
- Multiple Aspects: The combination of multiple aspects creates a more complex picture.
- This is Interpretive: Astrological correlations are not proof of causation.
To help me refine this response, could you tell me:
- Are you interested in specific types of accidents (e.g., natural disasters, industrial accidents, traffic accidents)?
- What timeframe are you interested in analyzing?
LA Crime Dataset
With this dataset the daily aggregation can serve as our main metric, but, we should look separately into categorical groups like violent/non-violent because they are quite different in nature and/or too broad in spectrum, we need some distinct astrological profiles.
Another challenge here is a sentiment labeling: it is quite straightforward in the case of accidents; in the case of crimes it actually depends on which point of view we take, the victim or the criminal.
crime = pd.read_csv('data/archive/crime-stats-LA.csv.gz') #for x in crime.columns: # print(f'------------------------------------------\n\t{x}\n------------------------------------------') # print(crime.groupby(x).size().sort_values(ascending=False)) ### proxy for the level of intent: spontaneous vs. planned #public = ['STREET','PARKING LOT','RESTAURANT/FAST FOOD','DEPARTMENT STORE','MARKET'] #crime = crime.loc[(crime['Premis Desc'].isin(public))|\ # (crime['Premis Desc'].str.endswith('STORE'))|\ # (crime['Premis Desc'].str.startswith('MTA'))].reset_index(drop=True) #private = ['SINGLE FAMILY DWELLING','MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)', # 'SIDEWALK','GARAGE/CARPORT','DRIVEWAY','PARKING UNDERGROUND/BUILDING','CONDOMINIUM/TOWNHOUSE'] #crime = crime.loc[crime['Premis Desc'].isin(private)].reset_index(drop=True) ### proxy for the criminal "success" #crime = crime.loc[crime['Status Desc'].str.endswith('Arrest')].reset_index(drop=True) #crime = crime.loc[~crime['Status Desc'].str.endswith('Arrest')].reset_index(drop=True) crime['date'] = crime['DATE OCC'].apply(lambda x:'-'.join([x.split()[0].split('/')[i] for i in [2,0,1]])) crime['month'] = crime['date'].apply(lambda x:'-'.join(x.split('-')[:-1])) crime['week'] = crime['date'].apply(lambda x:datetime(*[int(d) for d in x.split('-')]).strftime('%Y-%W')) crime['arm'] = 1 - crime['Weapon Desc'].isna().astype(int) firearm = set(['ASSAULT','AUTOMATIC','GUN','FIREARM','RIFLE','REVOLVER','SHOTGUN','PISTOL']) crime['firearm'] = crime['Weapon Desc'].apply(lambda x:len(set(re.split(r'\W+',str(x))).intersection(firearm))) crime.loc[crime['firearm'] > 0,'arm'] = 2 blade = set(['BLADE','KNIFE','DAGGER','MACHETE','RAZOR','SWORD']) crime['blade'] = crime['Weapon Desc'].apply(lambda x:len(set(re.split(r'\W+',str(x))).intersection(blade))) crime.loc[crime['blade'] > 0,'arm'] = 3 strong = set(['ARM','KNUCKLES','BAT','BRICK','HAMMER','AXE','PIPE','SCISSORS','SCREWDRIVER','STICK']) crime['strong'] = crime['Weapon Desc'].apply(lambda x:len(set(re.split(r'\W+',str(x))).intersection(strong))) crime.loc[crime['strong'] > 0,'arm'] = 4 timeline = crime.groupby('date').size().index.to_list() accidents = accidents.loc[accidents['date'].isin(timeline)].reset_index(drop=True) accidents.groupby('City').size() # not only LA! City Los Angeles 90590 Sacramento 40605 San Diego 34440 San Jose 17132 dtype: int64 weapon = ~crime['Weapon Used Cd'].isna() fig, ax = plt.subplots(2, 1, figsize=(9, 8)) for i, (d, n) in enumerate(zip(('week','month'),(7, 30))): stats = accidents.groupby(d).size() m, s = stats.mean(), stats.std() stats = (stats - m)/s x = pd.to_datetime(timeline[::n][:len(stats)]) ax[i].plot(x, stats.values[:len(x)], color='#ccc', marker='.', linestyle=':', linewidth=0.5, label='traffic accidents') for seg, name in zip((~weapon, weapon),('non-violent','violent')): stats = crime[seg].groupby(d).size() m, s = stats.mean(), stats.std() stats = (stats - m)/s x = pd.to_datetime(timeline[::n][:len(stats)]) ax[i].plot(x, stats.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label=f'{name} crime') ax[i].axhline(y=0, linestyle=':', linewidth=0.5, color='gray') ax[i].set_title(f'Crime {d}ly count (standardized)') ax[i].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False) ax[i].set_facecolor('none') ax[i].tick_params(axis='both', which='major', labelsize=8) fig.set_facecolor('none') plt.show()
Again, there's some evident procedural change late in 2023. Let's cut it off, which also helps comparison with the accidents data in the same area.
crime = crime.loc[crime['date'] < '2023-11-01'].reset_index() timeline = crime.groupby('date').size().index.to_list() fig, ax = plt.subplots(2, 1, figsize=(9, 8)) for i, (d, n) in enumerate(zip(('week','month'),(7, 30))): for j, label in enumerate(['non-violent','violent','firearm','blade','strong']): stats = crime[crime['arm']==j].groupby(d).size() m, s = stats.mean(), stats.std() stats = (stats - m)/s x = pd.to_datetime(timeline[::n][:len(stats)]) ax[i].plot(x, stats.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label=label) ax[i].axhline(y=0, linestyle=':', linewidth=0.5, color='gray') ax[i].set_title(f'Crime {d}ly stats (standardized)') ax[i].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False) ax[i].set_facecolor('none') ax[i].tick_params(axis='both', which='major', labelsize=8) fig.set_facecolor('none') plt.show()
Let's compare our target metric timeseries for the crime (LA) and accidents (LA, SD, SJ, Sacramento)
fig, ax = plt.subplots(2, 1, figsize=(9, 8)) for i, (d, n) in enumerate(zip(('week','month'),(7, 30))): stats_a = accidents.groupby(d)['Severity'].mean() m, s = stats_a.mean(), stats_a.std() stats_a = (stats_a - m)/s x = pd.to_datetime(timeline[::n][:len(stats_a)]) ax[i].plot(x, stats_a.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label='accidents') stats_c = crime.loc[weapon].groupby(d).size()/crime.loc[~weapon].groupby(d).size().loc[stats_a.index] m, s = stats_c.mean(), stats_c.std() stats_c = (stats_c - m)/s x = pd.to_datetime(timeline[::n][:len(stats_c)]) corr = stats_a.corr(stats_c, 'spearman') ax[i].plot(x, stats_c.values[:len(x)], marker='.', linestyle=':', linewidth=0.5, label='crime') ax[i].axhline(y=0, linestyle=':', linewidth=0.5, color='gray') ax[i].set_title(f'Crime and accidents {d}ly qualitative metric (corr. {corr:.2%})') ax[i].set_facecolor('none') ax[i].tick_params(axis='both', which='major', labelsize=8) ax[i].legend(bbox_to_anchor=(1.01, 1), loc='upper left', frameon=False) fig.set_facecolor('none') plt.show()
We've got significant correlation all along and a huge spike at the pandemic time. (Of course, this all could be procedural dynamics.)
margin = .75 borders = [.2, .8] order = lambda corr:corr[['test','other']].abs().mean(axis=1) for i, label in enumerate(['violent','firearm','blade','strong'], 1): stats = crime[crime['arm']==i].groupby('date').size() test = stats/crime.loc[~weapon].groupby('date').size() # vs. non-violent run(test, stats, label, margin, borders, order) stats = crime.loc[weapon].groupby('date').size() test = stats/crime.loc[~weapon].groupby('date').size() run(test, stats, 'all', margin, borders, order) -------------------------------------------- violent: 1400 -------------------------------------------- astro-background: test -2.24% / other 13.74% Sample size: negative 446 positive 254
p-value: 0.255168 => Null-Hypothesys stands...
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.091916 | 0.104068 | 0.095482 | 0.065250 |
| min | -0.286153 | -0.104457 | -0.098738 | 0.002662 |
| mean | -0.010764 | 0.023310 | 0.102324 | 0.063007 |
| max | 0.267013 | 0.360529 | 0.297952 | 0.267030 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| NEPTUNE-PLUTO | 1.887917 | 3.464370 | 1.525810 | 4.092396 |
| URANUS-PLUTO | 1.329418 | 3.050002 | 1.475742 | 3.368587 |
| SATURN-URANUS | 1.617878 | 2.159649 | 1.437597 | 2.861746 |
| URANUS-NEPTUNE | 0.840567 | 2.739904 | 0.809089 | 2.776983 |
| SATURN-PLUTO | -2.571020 | 1.091404 | 2.086910 | 2.681207 |
| JUPITER-SATURN | -1.360772 | 2.067947 | 3.120510 | 2.607530 |
| JUPITER-PLUTO | -3.113185 | 0.376395 | 1.467165 | 2.492889 |
| JUPITER-NEPTUNE | 2.904956 | -0.390289 | -1.034100 | 2.357306 |
| VENUS-JUPITER | -1.021541 | -1.003738 | -0.718418 | 1.519941 |
| VENUS-NEPTUNE | -0.934978 | -0.817443 | 1.336297 | 1.310411 |
| MARS-SATURN | -1.551753 | 0.181238 | 2.277850 | 1.237486 |
| MERCURY-NEPTUNE | -0.942891 | -0.584663 | 0.902284 | 1.130354 |
| MERCURY-VENUS | -0.588921 | -0.806932 | -0.470383 | 1.058288 |
| MARS-PLUTO | -0.628276 | 0.770309 | 2.515057 | 1.056802 |
p-value: 0.000000 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.053595 | 0.069577 | 0.095482 | 0.032474 |
| min | -0.157802 | -0.123466 | -0.098738 | 0.001528 |
| mean | -0.020238 | -0.000848 | 0.102324 | 0.049598 |
| max | 0.072026 | 0.195040 | 0.297952 | 0.124831 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| NEPTUNE-PLUTO | 1.219426 | 2.648955 | 1.525810 | 3.844040 |
| MARS-SATURN | -2.944330 | -1.078621 | 2.277850 | 3.585184 |
| MERCURY-URANUS | -2.081173 | -1.481347 | 2.366990 | 3.304331 |
| SATURN-PLUTO | -0.197015 | 2.803240 | 2.086910 | 3.165621 |
| JUPITER-SATURN | -1.833931 | 1.432155 | 3.120510 | 3.047607 |
| JUPITER-PLUTO | -1.354517 | 1.689287 | 1.467165 | 2.927450 |
| JUPITER-NEPTUNE | 1.173215 | -1.774533 | -1.034100 | 2.869160 |
| MARS-URANUS | -1.633672 | -1.059881 | 2.756310 | 2.483543 |
| MARS-PLUTO | -2.173856 | -0.523170 | 2.515057 | 2.354341 |
| VENUS-NEPTUNE | 1.343896 | 1.161557 | 1.336297 | 2.353341 |
| VENUS-SATURN | -0.942205 | -1.301508 | 2.035355 | 2.171788 |
| SUN-URANUS | 1.286910 | 0.960831 | 1.683335 | 2.091283 |
| URANUS-NEPTUNE | -1.253195 | 0.953299 | 0.809089 | 2.055392 |
| VENUS-PLUTO | -1.060431 | -1.088448 | 1.369664 | 2.041103 |
| MOON-NEPTUNE | -1.046487 | -1.086299 | 0.790653 | 2.027294 |
| URANUS-PLUTO | -0.601890 | 1.301808 | 1.475742 | 1.891280 |
| SUN-VENUS | -0.912580 | -0.957316 | -0.121932 | 1.778617 |
| MERCURY-VENUS | -1.004760 | -0.856692 | -0.470383 | 1.746890 |
| SATURN-NEPTUNE | -1.049373 | -0.798063 | 0.000849 | 1.720897 |
| MARS-JUPITER | -1.624495 | -0.250145 | 1.728181 | 1.608520 |
| SATURN-URANUS | 0.455677 | 1.005644 | 1.437597 | 1.453350 |
| VENUS-MARS | -0.519725 | -0.928461 | 0.981960 | 1.423518 |
| SUN-NEPTUNE | -0.876926 | -0.607090 | 0.579339 | 1.374007 |
| SUN-PLUTO | -0.747374 | -0.702519 | 1.903694 | 1.369330 |
| MERCURY-SATURN | -0.793643 | -0.665007 | 1.758989 | 1.367325 |
| VENUS-JUPITER | -0.677600 | -0.625418 | -0.718418 | 1.229156 |
| MOON-SATURN | 0.754449 | 0.550512 | 1.808118 | 1.212327 |
| SUN-SATURN | 0.891256 | 0.437277 | 0.764959 | 1.203916 |
| MOON-URANUS | 0.664045 | 0.472207 | 1.703364 | 1.053838 |
| MARS-NEPTUNE | 1.034410 | 0.041698 | -0.104336 | 0.898273 |
p-value: 0.004660 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.055353 | 0.061335 | 0.095482 | 0.034806 |
| min | -0.179948 | -0.085809 | -0.098738 | 0.004338 |
| mean | -0.011010 | 0.009777 | 0.102324 | 0.039607 |
| max | 0.161971 | 0.249344 | 0.297952 | 0.188613 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| NEPTUNE-PLUTO | 2.310310 | 4.065256 | 1.525810 | 5.418949 |
| JUPITER-SATURN | -1.330889 | 2.089808 | 3.120510 | 2.899592 |
| SATURN-PLUTO | -1.407665 | 1.940263 | 2.086910 | 2.828878 |
| JUPITER-PLUTO | -3.250903 | 0.059991 | 1.467165 | 2.637851 |
| JUPITER-NEPTUNE | 2.926135 | -0.143018 | -1.034100 | 2.452762 |
| URANUS-PLUTO | 0.380597 | 2.374678 | 1.475742 | 2.394955 |
| VENUS-JUPITER | -1.448182 | -1.399007 | -0.718418 | 2.384198 |
| SUN-JUPITER | -1.235976 | -1.219958 | -0.750149 | 2.057701 |
| URANUS-NEPTUNE | -0.229529 | 1.927792 | 0.809089 | 1.881082 |
| SATURN-NEPTUNE | -0.835583 | -0.771267 | 0.000849 | 1.343984 |
| MOON-MARS | 0.967097 | 0.643687 | 1.391273 | 1.336148 |
| SUN-PLUTO | -0.722668 | -0.799145 | 1.903694 | 1.278761 |
| MARS-JUPITER | 0.162269 | 1.296952 | 1.728181 | 1.271769 |
| MARS-SATURN | -1.501048 | 0.055856 | 2.277850 | 1.242790 |
| VENUS-MARS | 1.114115 | 0.377484 | 0.981960 | 1.218500 |
| MARS-PLUTO | -1.334610 | 0.079868 | 2.515057 | 1.131602 |
| MERCURY-MARS | -0.959512 | -0.392639 | 0.437891 | 1.108920 |
| MERCURY-NEPTUNE | -0.763709 | -0.512753 | 0.902284 | 1.059056 |
| VENUS-NEPTUNE | -0.659314 | -0.561820 | 1.336297 | 1.019278 |
p-value: 0.000314 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.087645 | 0.097989 | 0.095482 | 0.055915 |
| min | -0.286086 | -0.112001 | -0.098738 | 0.002228 |
| mean | -0.015235 | 0.021343 | 0.102324 | 0.061863 |
| max | 0.306206 | 0.344720 | 0.297952 | 0.228434 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| NEPTUNE-PLUTO | 1.279590 | 3.517934 | 1.525810 | 4.085396 |
| JUPITER-SATURN | -1.097860 | 2.937535 | 3.120510 | 3.434401 |
| SATURN-PLUTO | -3.264158 | 0.986871 | 2.086910 | 3.422956 |
| JUPITER-PLUTO | -2.904805 | 0.987366 | 1.467165 | 3.141753 |
| JUPITER-NEPTUNE | 3.493722 | -0.274882 | -1.034100 | 2.979003 |
| URANUS-NEPTUNE | -0.399707 | 2.196710 | 0.809089 | 2.238099 |
| URANUS-PLUTO | -0.307187 | 2.146390 | 1.475742 | 2.121496 |
| SATURN-URANUS | 0.636514 | 1.539944 | 1.437597 | 1.848210 |
| VENUS-JUPITER | -1.019516 | -1.142988 | -0.718418 | 1.800554 |
| SUN-VENUS | -0.903314 | -1.011597 | -0.121932 | 1.594353 |
| MARS-SATURN | -1.561059 | 0.293010 | 2.277850 | 1.480197 |
| JUPITER-URANUS | 0.913456 | 0.781014 | 0.344110 | 1.400256 |
| MARS-PLUTO | -0.798978 | 0.833138 | 2.515057 | 1.356209 |
| MARS-URANUS | 0.648612 | 0.960821 | 2.756310 | 1.350244 |
| MERCURY-NEPTUNE | -0.851814 | -0.701042 | 0.902284 | 1.281871 |
| MARS-JUPITER | -0.873780 | 0.604139 | 1.728181 | 1.214177 |
| SUN-JUPITER | -0.733148 | -0.709819 | -0.750149 | 1.196560 |
| VENUS-NEPTUNE | -0.743797 | -0.624907 | 1.336297 | 1.130504 |
| MARS-NEPTUNE | 0.292350 | -0.881522 | -0.104336 | 1.001545 |
p-value: 0.000000 => Null-Hypothesys rejected!
Corr. stats:
| test | other | background | ord | |
|---|---|---|---|---|
| std | 0.105666 | 0.094001 | 0.095482 | 0.067067 |
| min | -0.330680 | -0.106784 | -0.098738 | 0.000795 |
| mean | -0.029680 | 0.015955 | 0.102324 | 0.066007 |
| max | 0.352031 | 0.288279 | 0.297952 | 0.254461 |
| test | other | background | ord | |
|---|---|---|---|---|
| influence | ||||
| JUPITER-SATURN | -2.190259 | 2.951946 | 3.120510 | 3.794150 |
| SATURN-PLUTO | -3.129479 | 1.623897 | 2.086910 | 3.603337 |
| JUPITER-PLUTO | -2.900328 | 1.586009 | 1.467165 | 3.396266 |
| JUPITER-NEPTUNE | 3.331534 | -0.673892 | -1.034100 | 3.096743 |
| URANUS-NEPTUNE | -1.661224 | 1.747957 | 0.809089 | 2.533634 |
| URANUS-PLUTO | -1.478089 | 1.782195 | 1.475742 | 2.413359 |
| NEPTUNE-PLUTO | -0.244779 | 3.066755 | 1.525810 | 2.342020 |
| SATURN-URANUS | 0.362147 | 2.018234 | 1.437597 | 1.699672 |
| MARS-SATURN | -1.543537 | 0.349390 | 2.277850 | 1.460803 |
| MARS-PLUTO | -1.093447 | 0.832475 | 2.515057 | 1.444784 |
| MARS-JUPITER | -0.684309 | 1.161338 | 1.728181 | 1.352946 |
| MARS-NEPTUNE | 0.845211 | -0.807170 | -0.104336 | 1.231498 |
| SUN-VENUS | -0.495841 | -1.125165 | -0.121932 | 1.179127 |
| VENUS-JUPITER | -0.548741 | -1.016891 | -0.718418 | 1.144921 |
| JUPITER-URANUS | 0.599367 | 0.921338 | 0.344110 | 1.117839 |
Okay, this is a fascinating and complex question! Predicting societal trends like criminal activity based on astrological aspects is speculative and requires careful nuance. Astrology deals with potential energies, not guaranteed outcomes. It's also crucial to remember that socio-economic factors, political climates, and human choices are the primary drivers of crime. Astrological influences can, however, contribute to an atmosphere that makes certain patterns more likely.
Here's a list of astrological aspects that, when prominent in a planetary configuration affecting a timeframe (e.g., a transit or a eclipse series), might correlate with increased potential for societal unrest and criminal activity. This is ordered by what I perceive to be the potential strength of influence, although interpretations vary greatly among astrologers. This is a potential influence, not a guarantee. I'm also adding caveats for each aspect to explain why interpretations are complex. Please read the important disclaimers at the bottom of this answer.
Important Note: I am focusing on aspects affecting the collective – meaning transits of outer planets (Jupiter, Saturn, Uranus, Neptune, Pluto), eclipses, or significant conjunctions involving these planets. Inner planets (Mercury, Venus, Mars) are generally less impactful on collective societal issues, though Mars can certainly play a role in violent incidents.
Planetary Aspects & Potential Correlation with Increased Criminal Activity (Ordered by Potential Strength)
- Pluto - Saturn (Major Conjunctions, Squares, Oppositions): Most Powerful, Long-Term Influence. Pluto represents transformation, destruction, power struggles, and the underworld. Saturn represents structure, authority, restriction, and law. The combination creates immense tension.
- Potential Impact: This is the most likely aspect to correlate with significant societal upheaval, including a rise in violent crime, corruption, systemic collapse, and the emergence of new power structures that might involve criminal enterprises. The exact nature of the rise in crime will depend on the houses involved in the configuration. A conjunction brings a potent, almost unavoidable clash. Squares indicate a structural conflict needing resolution, often through struggle and unrest. Oppositions create long-term tension and require conscious effort to balance. Saturn can represent law enforcement, which is affected and potentially destabilized.
- Caveats: These aspects happen infrequently (major Pluto-Saturn conjunctions are roughly every 20 years). Their influence is drawn out and complex, affecting institutions and long-term trends, not just a sudden spike in crime. The way this energy manifests is highly dependent on the surrounding astrological context.
- Uranus - Pluto (Conjunctions, Squares, Oppositions): Very Strong, Disruptive & Transformative. Uranus is rebellion, sudden change, and disruption. Pluto is deep transformation and power.
- Potential Impact: This combination can bring sudden, unexpected shifts in societal structures, leading to increased feelings of alienation, rebellion against authority, and a breakdown of traditional norms. This can manifest as a rise in politically motivated crime, acts of protest that escalate into violence, and the emergence of new criminal organizations exploiting the instability. Sudden technological advancements could be exploited for criminal purposes.
- Caveats: Uranus acts quickly and erratically, so the impact can be unpredictable. The disruption might manifest as a sudden surge in specific types of crime, rather than a generalized increase.
- Neptune - Pluto (Conjunctions, Squares, Oppositions): Strong, Deceptive & Corrosive. Neptune represents illusion, deception, spirituality, and the collective unconscious. Pluto represents the underworld, hidden power, and transformation.
- Potential Impact: This combination can erode trust in institutions, facilitate corruption, and create an environment ripe for exploitation. Increased drug-related crime, human trafficking, and organized crime are potential outcomes. This aspect tends to foster a sense of hopelessness, allowing criminal behaviour to flourish under a veil of secrecy.
- Caveats: Neptune is a blurring influence – making it difficult to pinpoint the cause of the rise in crime. This aspect can also manifest as a rise in white-collar crime or financial manipulation, rather than visible violence.
- Saturn - Uranus (Squares, Oppositions): Moderate to Strong, Tensional and Restrictive. Saturn is restriction and authority. Uranus is rebellion and liberation.
- Potential Impact: This aspect creates tension between established rules and the desire for freedom and change. Increased civil unrest, protests against authority, and a rise in petty crime could occur. This may manifest as resistance against rules/law.
- Caveats: While disruptive, this aspect doesn's necessarily produce violent crime, but rather friction and a breakdown in respect for authority. The nature of crime depends heavily on the surrounding signs and houses.
- Mars - Pluto (Conjunctions, Squares, Oppositions): Moderate, Aggressive & Destructive. Mars represents aggression, action, and conflict. Pluto represents destruction, transformation, and power.
- Potential Impact: This combination can lead to a heightened sense of aggression and a willingness to use violence to achieve goals. Increased violent crime, gang activity, and a breakdown in law enforcement's ability to maintain order are possible. This is a potential increase in impulsive violence.
- Caveats: While this aspect may contribute to violent acts, it is often triggered by other underlying tensions (like those created by Pluto-Saturn or Uranus-Pluto). It's a catalyst for violence, not the root cause.
- Jupiter - Saturn (Squares, Oppositions): Moderate, Stagnation and Frustration. Jupiter represents expansion, optimism and law. Saturn represents restriction, limitation and authority.
- Potential Impact: A frustrating disconnect between ideals and reality can build up. This is frequently manifested as societal tensions and feelings of being unfairly limited. In extreme cases, this might push people towards acting outside the law to overcome these feelings. This is more likely to increase instances of white-collar crime, fraud and corruption.
- Caveats: This is a more subtle influence than some of the other aspects. It might manifest as a gradual decline in trust in institutions, rather than a sudden spike in crime.
Important Disclaimers:
- Correlation, Not Causation: Astrology indicates potential correlations, not guaranteed outcomes. Societal behavior is incredibly complex and driven by numerous factors. These astrological aspects are one piece of a larger puzzle.
- Free Will & Human Agency: Humans have free will, and choices made by individuals and governments have a far greater impact than astrological influences.
- Complex Interpretation: Astrology is a nuanced art. The houses, signs, and other planetary placements involved in these aspects are crucial for accurate interpretation. A skilled astrologer considers the entire chart, not just a single aspect.
- Specificity is Difficult: Predicting the type of crime that might increase is challenging. The aspects provide an energetic framework, but the specifics are heavily dependent on other astrological factors.
- Not a Predictive Tool: Astrology shouldn't be used as a substitute for evidence-based crime prevention strategies.
To provide a more tailored analysis, I would need to know:
- The specific timeframe you are interested in.
- The astrological chart being considered (e.g., a particular eclipse series, a transit of outer planets).
- The specific area/community you are analyzing.
So, is it conclusive? Noop... Is it suggestive? Kinda...
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