Scenarios
Scenario correlations
Pearson, Spearman and least-squares fits at several minimum-tweet thresholds (computed with scipy).
page 1 of 1 CSV
| scenarioTEXT | unitTEXT | x_metricTEXT | y_metricTEXT | weight_metricTEXT | min_tweetsINTEGER | nINTEGER | pearson_rREAL | pearson_pREAL | spearman_rhoREAL | spearman_pREAL | slopeREAL | interceptREAL | r2REAL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| income | sa2 | median_aud | avg_income | tweets_income | 1 | 167 | 0.13830946 | 0.074667012 | 0.10174951 | 0.19073232 | 0.000028786373 | 4.9772465 | 0.019129507 |
| income | sa2 | median_aud | avg_all | tweets_all | 1 | 279 | 0.10517417 | 0.079475937 | 0.099232652 | 0.098095874 | 0.000014755496 | 5.1011617 | 0.011061606 |
| crime | lga | total | avg_crime | tweets_crime | 1 | 49 | 0.31191126 | 0.029126876 | 0.26883125 | 0.061791927 | 0.00015238944 | 3.2495853 | 0.097288636 |
| crime | lga | total | avg_all | tweets_all | 1 | 71 | 0.25773849 | 0.0300068 | 0.2582495 | 0.029670985 | 0.000025130221 | 5.6652317 | 0.066429129 |
| income | sa2 | median_aud | avg_income | tweets_income | 5 | 85 | 0.1672627 | 0.12600338 | 0.17638293 | 0.10635829 | 0.000024943312 | 4.9393495 | 0.027976811 |
| income | sa2 | median_aud | avg_all | tweets_all | 5 | 229 | 0.20959722 | 0.0014230649 | 0.18812505 | 0.0042790624 | 0.00001985493 | 4.8546276 | 0.043930993 |
| crime | lga | total | avg_crime | tweets_crime | 5 | 28 | -0.0072640054 | 0.97073582 | 0.049315098 | 0.80320216 | -0.0000023678381 | 3.6737023 | 0.000052765774 |
| crime | lga | total | avg_all | tweets_all | 5 | 70 | 0.25006126 | 0.03681717 | 0.24412562 | 0.041684933 | 0.000024015851 | 5.6621859 | 0.062530634 |
| income | sa2 | median_aud | avg_income | tweets_income | 10 | 69 | 0.081040694 | 0.50799267 | 0.086986308 | 0.47726335 | 0.0000099596974 | 5.5468321 | 0.0065675942 |
| income | sa2 | median_aud | avg_all | tweets_all | 10 | 201 | 0.22476397 | 0.0013373106 | 0.22393242 | 0.0013950607 | 0.000017127047 | 4.9558997 | 0.050518843 |
| crime | lga | total | avg_crime | tweets_crime | 10 | 17 | 0.1404833 | 0.590724 | -0.034313725 | 0.89598081 | 0.000034411737 | 3.7927542 | 0.019735558 |
| crime | lga | total | avg_all | tweets_all | 10 | 68 | 0.26238278 | 0.030650838 | 0.22403329 | 0.066269601 | 0.000023483266 | 5.6417634 | 0.068844724 |
| income | sa2 | median_aud | avg_income | tweets_income | 30 | 33 | -0.001508618 | 0.99335196 | 0.0026737968 | 0.98821763 | -1.1116529e-7 | 5.912672 | 0.0000022759282 |
| income | sa2 | median_aud | avg_all | tweets_all | 30 | 147 | 0.19686409 | 0.01685249 | 0.19746389 | 0.016514666 | 0.000012526174 | 5.1405732 | 0.03875547 |
| crime | lga | total | avg_all | tweets_all | 30 | 65 | 0.19264165 | 0.12418783 | 0.16053322 | 0.20144312 | 0.000016794784 | 5.6484881 | 0.037110805 |
| crime | sal | log10_tweets | avg_raw | tweet_count | 1 | 102 | -0.048054294 | 0.63149733 | -0.0050164472 | 0.96009088 | -0.15345588 | 3.9118519 | 0.0023092152 |
| crime | sal | log10_tweets | avg_raw | tweet_count | 5 | 37 | 0.057564178 | 0.73505271 | 0.099232441 | 0.55899472 | 0.10886796 | 3.6664978 | 0.0033136345 |
| crime | sal | log10_tweets | avg_raw | tweet_count | 10 | 23 | -0.090926692 | 0.67989412 | -0.1962765 | 0.36940362 | -0.12232905 | 4.1307964 | 0.0082676634 |
| crime | sal | log10_tweets | avg_raw | tweet_count | 30 | 3 | -0.81334567 | 0.39528764 | -0.5 | 0.66666667 | -0.41634503 | 5.1086483 | 0.66153118 |