ulewnaconthast.cf/map31.php Next, a score of 1. This division is done regardless of whether the slots are fully or partially covered, as shown in Equation 3 , in which n represents the number of slots covered. Aoristic e x t is a novel method presented in this work. It works by creating a time span using the same approach as the aoristic method, with the exception that it takes into account whether time slots are fully or only partially covered when calculating the aoristic score.
It first calculates the total coverage of the slots that the offense spans either partly or completely according to Equation 4 , where c o v e r a g e i is the coverage of slot i expressed as a value between zero and one, and n is the total number of slots. As an example, consider a burglary reported to have occurred somewhere between It can be concluded that the offense covers four different hours, although only two of them is fully covered.
Both start and stop methods will assign full scores 1. The average method will assign a full score to the second slot since the average time between start and stop is The random method will assign a full score to any of the four slots based on a uniform distribution.
The aoristic method calculates its score by dividing the point available for the offense with the number of time slots covered. Then, the score for each slot in the example is the fraction it covers. Consequently, the aoristic e x t method takes into account more detailed temporal information when establishing the scores compared to the traditional aoristic method. In this section, the data collection, representation and cleaning processes that this study relies on are described. The temporal analysis methods are evaluated on official criminal records of residential burglaries provided by the Swedish police.
Both attempted and completed burglaries were included. In the remainder of this work, the term burglaries refers to both completed and attempted burglaries. The dataset consists of all burglaries registered in Sweden during five years, from 1 January to 31 December A total of , burglaries are included in the dataset. As shown in Table 1 , roughly 3.
These records were removed due to either: Inconsistencies in the records were most likely due to human error introduced when filing out the criminal reports. No patterns in how the erroneous records were dispersed in the dataset were found, i. Therefore, the erroneous records are assumed to be uniformly distributed throughout the dataset. Although, such errors are slightly more common in — compared to the earlier years. After inconsistent data records were removed, a total of , burglaries remained, and they make up the dataset used in this study; see Table 1.
The process of cleaning crime records was carried out in the statistical software suite R. Each burglary in the dataset is represented using four attributes. The attributes, listed in Table 2 , are similar to the temporal concepts explained in Section 3. Similarly, the times t i m e s t a r t and t i m e e n d denote the first and last time on a certain date, respectively.
As an exact offense date and time usually are not available for burglaries, the temporal aspect needs to be represented as a time span instead of a specific moment. As such, the temporal data were grouped as two points in time representing the boundaries of the time span. The difference between the start and end time in seconds was also calculated for each burglary and stored in the database. Table 3 shows the distribution of these time durations within the dataset. An exact offense time is available for On the other hand, 6.
Th crimes with an exact offense time represent the ground truth regarding how burglaries are distributed throughout time. There are 10, burglaries with a known exact offense time in the dataset, which represent For these crimes, the exact points in time when the offenders committed them are known. However, the exact offense time is not known for the rest of the burglaries. The reasons that some burglaries have an exact offense time is due to burglar alarms with time logs In this study, the evaluation is thus based on the assumption that A similar approach of using a subset of the offenses for evaluation purposes has been used in previous studies, e.
The motivation behind this assumption is that the exact offense time is determined by external events, such as witnesses and burglary alarm time logs available for different types of burglaries. In addition, before making this assumption, it was discussed with domain experts in both the criminal intelligence in Stockholm, as well as the national Swedish serial crime group, specifically targeting volume crimes, such as burglaries. The present study includes eight experiments that test how accurately the six analysis methods approximate the temporal distribution of burglaries with a known exact offense time.
This is done with regard to four different temporal resolutions relative to a short perspective hours of the day , medium perspective days in the week and long perspective months per year and day in the year. In the first four experiments, a one-factor within-subjects design is used when the temporal resolutions are investigated using all available burglary data. The factor is the temporal methods described in Section 3. These also constitute the independent variable.
The dependent variables are four statistical measures to be presented in Section 5. Furthermore, since smaller sample sizes are to be expected in most cases, how a reduction of residential burglary cases affects the accuracy is also investigated in four additional experiments with a two-factor within-subjects design.
In all eight experiments, the temporal methods are evaluated by measuring how well they approximate the ground truth represented as: The similarity or divergence between the approximation and actual distribution is measured per year using the evaluation metrics described in Section 5. Then, the mean and standard deviation are calculated over the five years for each method. The results are used to suggest which temporal analysis methods are more suitable for determining the temporal distribution of burglaries with regards to the four temporal resolutions.
Extracting burglaries with a precisely-known offense time from the dataset and using them for evaluation could potentially be a threat to internal validity. If the extracted subset is not representative of the population of Swedish residential burglaries, there is a risk of biases being introduced. However, this approach has been discussed in previous high-quality research [ 11 ]. In addition, domain-experts in the Swedish law enforcement agreed that the assumption that the extracted subset was representative was valid, The reason that some burglaries have known offense times is due to external events, e.
Since the data in the present study come from Swedish residential burglaries it is unlikely that the temporal distributions for the four temporal resolutions being investigated could be generalized to other countries. As described in Section 5 , the actual distributions are produced based on the burglaries in the dataset for each of the five years individually.
For each year and representation, the approximate distribution is compared to the actual distribution and the difference measured. For each evaluation measure, the difference between the actual and approximate distribution is averaged over the five years. The performance of the methods is compared against the ground truth, i. Since the data used in the experiment are binned, other well-used nonparametric tests, such as Kolmogorov—Smirnov, are not applicable [ 21 ]. The Euclidean distance is used to measure the distance between two distributions, in this case between the actual distributions and the approximations produced by the methods.
The Euclidean distance is defined in Equation 6 , where x i and y i indicate the i -th data point in the respective distribution. The test is used to investigate how well the approximated distribution correlates with the actual distribution. The Kullback—Leibler divergence is a non-symmetric measure of the information loss when one probability distribution is used for approximating another [ 23 ].
It is also known as the information gain, or relative entropy, of one distribution to another.
This is measured in the number of extra bits that are required when approximating the first distribution based on the other, i. To evaluate the difference between the methods, the Kruskal-Wallis test is used to investigate whether a significant difference exists between different methods. Kruskal-Wallis is a non-parametric rank-based statistical test for investigating whether two or more samples are from the same distribution [ 22 ]. Kruskal-Wallis is used instead of the parametric one-way ANOVA test, as the data were not found to be normally distributed [ 22 ].
If a difference exists according to the Kruskal-Wallis test, the Dunn post hoc test is used to investigate between which pairs of methods differences exist. To correct for multiple comparisons, Benjamini-Hochberg adjustment is used [ 24 ]. The Benjamini-Hochberg adjustment is used instead of Bonferroni adjustment, as it provides a less restrictive tradeoff with regards to statistical power, although it allows false positives.
Additionally, the Benjamini-Hochberg adjustment controls the false discovery rate, whereas the Bonferroni adjustment controls the family-wise error rate. The Nemenyi test is used as a post hoc test to investigate how the methods differ from each other. The crime data were stored in a MySQL relational database.
All crime evaluation methods that are evaluated in this work were implemented as scripts in the statistical language R. The scripts make use of the RMySQL, Lubridate and Ggplot2 packages for their internal workings, and they will be available upon request to the corresponding author. Statistical evaluations have used corresponding packages in R. The results are presented and analyzed based on the experimental setup described in Section 5.
Statistically-significant differences between the methods are also presented. Any statistical difference between the methods, using the statistical tests presented in Section 5. Below are the results for the evaluation of the temporal analysis methods using the full dataset and the four different configurations.
The mean and standard deviation of the four evaluation measures were calculated for each year from — These can be seen in Table 4. This, however, could be expected since the statistical power of the tests was high due to the large amount of burglaries included in the analysis. Based on the four evaluation measures presented in Table 4 , both the aoristic and aoristic e x t methods performed similarly, although there was a modest advantage for the aoristic method, while the random method performed slightly worse.
The results in Table 5 show that both the aoristic and aoristic e x t methods performed significantly better than the average, stop and start methods. The random method performed significantly better than the start and stop methods. Based on these results, it was suggested that the most suitable method for the analysis of hour data was the aoristic method, while the start method was least suitable for the task. Since the aoristic method was found to be the most suitable method, it was used for plotting the aoristic hour of the day distribution using all burglary data available for the five years.
The distribution in Figure 2 is bimodal, or possibly trimodal, with peaks around afternoon and in the night around the early hours. It was clear that the burglary frequency increases during the hours of the day when people are out of their residences as part of their daily routine or during the night when they are at sleep.
Table 6 includes the mean and standard deviation of the four evaluation measures that were calculated for each year from — Aoristic and aoristic e x t performed similarly on the Euclidean and Kullback—Leibler measures. Compared to the two aoristic methods, the random method performed slightly worse. The results in Table 7 show that the aoristic method performed significantly better than the average, stop and start methods, while the aoristic e x t method performed significantly better than the two latter. The random method performed significantly better than the start method.
Based on these results, it was suggested that the most suitable method for the analysis of weekday data was the aoristic method, while the start method was least suitable. Since the aoristic method was found to be the most suitable method, it was used for plotting the aoristic weekday distribution using all burglary data available for the five years. As shown in Figure 3 , there are peaks during Fridays and Saturdays and a considerable drop on Sundays. The peaks can be explained by that people usually go on recreational activities during the weekends, leaving their homes unguarded.
The drop on Sundays is harder to explain, but it might be due to burglars also using Sundays for recreation. Law enforcement officers have speculated that Sundays are when stolen goods most often are fenced. In Figure 3 , the baseline, i. It shows a similar distribution, but with a less distinct peak during Fridays and Saturdays. Table 8 includes the mean and standard deviation of the four evaluation measures that were calculated for each year from — This is because there is less monthly variation of burglaries compared to variation over half-hours or weekdays.
Both the aoristic methods performed similarly and showed the best performance with regards to the evaluation measures, except for the Euclidean distance, which instead was in favor for the start, random and average methods. Random and average also performed similar to each other and were next best out of the six methods, while start and stop performed worst, taking all measures into account. Based on these results, it was suggested that the most suitable method for the analysis of month data was the aoristic method, while the stop method was least suitable for the task.
Since the aoristic method is suggested to be the most suitable method based on the results, and for consistency between the subsections, it was used for plotting the aoristic month distribution for the full five-year burglary data. The distribution in Figure 4 shows peaks during the autumn and winter months that usually is explained by darker days, due to daylight saving time, allowing burglars to move around unnoticed to a larger extent.
There is also a slight peak in July, which is the biggest vacation month in Sweden. Table 9 includes the mean and standard deviation of the four evaluation measures that were calculated for each year from — in the day in year temporal resolution. Both aoristic methods show promising results, with a slight advantage for the aoristic method. The differences between all six methods are smaller than in the previous configurations.
Since the aoristic method showed the most promising results, it was used for plotting the aoristic day of the year distribution based on the full five-year burglary data. The most distinct peak in the distribution shown in Figure 5 is on Christmas Eve, which is celebrated on the 24 th of December in Sweden. This contradicts the results by Cohn and Rotton that burglaries are under-represented during Christmas in a U. The lowest burglary frequency, if disregarding the leap day 29 February, was recorded on the last of December.
Below are the results for the evaluation of the temporal analysis methods using the reduced dataset samples of the full dataset and the four different configurations. As long as there are more than roughly 10, burglaries, the random method performs close to the aoristic methods. Then, as the numbers of burglaries are reduced, the random method shows decreasing performance due to the law of large numbers.
When analyzing crimes in the hundreds, the aoristic methods are clearly most suitable for the task at hand. If analyzing around 40— crimes, the difference in performance is substantial between the aoristic methods, on the one hand, and the random, stop and average methods, on the other hand, were the latter three show very similar performance. The start method is the worst alternative throughout the whole range. Since statistical differences were found, a Nemenyi post hoc test was used for pair-wise comparisons between methods.
As shown in Table 10 , both aoristic methods were significantly better than the average, stop and start methods, while random was significantly better than start. The average ranks from the Friedman test are also shown in Table 10 , and the aoristic method receives the best ranks, followed by aoristic e x t.
Overall, the aoristic method was the best candidate for estimating weekday crime frequencies throughout the range sizes. A Nemenyi post hoc test revealed that the aoristic method was significantly better than average, stop and start, while aoristic e x t dominated the stop and start methods; see Table The average ranks from the Friedman test suggest that the aoristic method is the most suitable method for analyzing weekday crime frequencies. As shown in Figure 8 , the six temporal analysis methods performance fluctuate throughout the whole range of numbers of crimes when estimating the monthly crime distribution.
It is hard to extract any method as better or worse than any other method only by looking at Figure 8. Unfortunately, the Nemenyi post hoc test could not identify any statistically-significant differences between the six temporal analysis methods. The average ranks of the temporal analysis methods over the datasets are presented in Table 12 showing that the aoristic method has lowest rank, which indicates that it is the most suitable candidate method for estimating monthly temporal crime distributions.
Figure 8 shows the performance for the six methods when estimating the crime frequency per day in a year as the number of crimes included in the analysis declines.
Five of the methods had a similar declining trend as the previous configurations investigated that stabilize around crimes. However, the average method shows inferior performance in estimating the crime frequency in the day of year configuration. Due to its extreme behavior, the correctness of the average method implementation was re-tested, but no errors were found.
A Nemenyi post hoc test identified that aoristic e x t dominated the average, start and stop methods, while the aoristic method outperformed the average and start methods; see Table The random method also performed significantly better than the average method. The average ranks from the Friedman test suggest that the aoristic e x t method was the best candidate for estimating the crime frequency in the day of year resolution.
Today, law enforcement uses an ad hoc approach to estimating the temporal distribution of residential burglaries. This approach could influence resource planning negatively, as it is difficult to predict how crimes distributions will change over time. Often, experience is prevalent in the decision making process. In this paper, multiple approaches for estimating an approximate temporal distribution of residential burglaries have been investigated.
A method that is able to approximate the temporal distribution of crimes is a key component, together with spatial data, when for instance, law enforcement agencies schedule patrols and other resources more efficiently to better be in sync when crimes actually occur. For example, having a set of hourly approximations that corresponds well to the actual temporal distributions of residential burglaries in different areas of a municipality allows patrols to be present in the correct area when there is a higher likelihood of a residential burglary taking place, i.
By approximating the temporal distribution of residential burglaries over days in the week allows law enforcement administration to better schedule law enforcement officers for different tasks. The temporal distribution regarding weekdays in Table 3 suggests that there is a spike in residential burglaries on Fridays and Saturdays, to then decrease on Sundays. This suggests that it is more efficient to have law enforcement officers focus on residential burglaries on Fridays and Saturdays, than on Sundays.
Further, Table 2 suggests that most residential burglaries take place during working hours. Having an analysis on different geographical areas and specific days would allow law enforcement to further distinguish patterns in the data. Looking at the actual distributions, the data suggest that As time increases, the amount of burglaries that span long segments is decreasing; In the data, 6. That is quite a large number of burglaries that is unreported for a long time. It can be noted, however, that most crimes occur within a six-hour time span. As such, being able to detect changes in trends requires the ability to determine the distribution on an hourly resolution.
If the estimation of the crime distribution only occurs on a longer time span, the details will be lost. This puts further emphasis on the need to accurately estimate the crime distribution on an hourly scale [ 13 ]. The results and the statistical analysis suggest that the aoristic and aoristic e x t methods are preferred over the other methods for estimating the temporal distribution of residential burglaries, as they perform significantly better than the stop, average and start method in multiple configurations.
The aoristic e x t method is quite complex compared to the aoristic method, and it does not suggest improved results when compared to the aoristic method. We hypothesize that this is due to limited detail in the temporal data to support the aoristic e x t method, e. That way, the temporal resolution in the data is decreased. However, when the temporal resolution is instead unique days in the year, such subtle differences have a negligible effect.
In that particular case, the aoristic e x t method also comes out as the most suitable candidate, but interestingly, this does not apply when estimating the day in the week or month in the year. Those deviations could depend on the rather low granularity in those cases, i. In the end, both aoristic methods produce usable results; the trade-off is one between accuracy and time complexity, i.
If the main focus is on time complexity, then a random method that, rather than a uniform distribution, instead uses the distribution of crimes with known offense time would probably render usable results as long as sufficiently many crimes are included in the analysis. The scenarios with a reduced number of crimes included in this study are important, as in practice, smaller sample sizes are more likely.
This is a concern for, e. Smaller timescales will be more successful in providing short-term forecasts. Raskolnikov's psychology is placed at the center, and carefully interwoven with the ideas behind his transgression; every other feature of the novel illuminates the agonizing dilemma in which Raskolnikov is caught.
Raskolnikov Rodion is the protagonist , and the novel focuses primarily on his perspective. A year-old man and former student, now destitute, Raskolnikov is described in the novel as "exceptionally handsome, above the average in height, slim, well built, with beautiful dark eyes and dark brown hair. On the one hand, he is cold, apathetic, and antisocial; on the other, he can be surprisingly warm and compassionate.
He commits murder as well as acts of impulsive charity. His chaotic interaction with the external world and his nihilistic worldview might be seen as causes of his social alienation or consequences of it. Despite its title, the novel does not so much deal with the crime and its formal punishment, as with Raskolnikov's internal struggle the book shows that his punishment results more from his conscience than from the law.
It is only in the epilogue that he realizes his formal punishment, having decided to confess and end his alienation from society. Sofya Semyonovna Marmeladova , variously called Sonya and Sonechka, is the daughter of a drunkard named Semyon Zakharovich Marmeladov, whom Raskolnikov meets in a tavern at the beginning of the novel. She is often characterized as self-sacrificial, shy, and even innocent despite the fact that she is compelled into prostitution to help her family.
She also, as Raskolnikov discerns, shares the same feelings of shame and alienation as he does and becomes the first person to whom Raskolnikov confesses his crime, and she supports him even though she was friends with one of the victims Lizaveta. Throughout the novel, Sonya is an important source of moral strength and rehabilitation for Raskolnikov.
She is forced to prostitute herself to provide for her family, leading some critics to make comparisons with Mary Magdalene. She initially plans to marry the wealthy, yet smug and self-possessed, Luzhin, to free the family from financial destitution. She has a habit of pacing across the room while thinking. She is followed to Saint Petersburg by the disturbed Svidrigailov, who seeks to win her back through blackmail.
She rejects both men in favour of Raskolnikov's loyal friend, Razumikhin. Following Raskolnikov's sentence, she falls ill mentally and physically and eventually dies. She hints in her dying stages that she is slightly more aware of her son's fate, which was hidden from her by Dunya and Razumikhin.
Dmitry Prokofyich Vrazumikhin , often referred to as Razumikhin , is Raskolnikov's loyal friend and also a former law student. In terms of Razumikhin's contribution to Dostoevsky's anti-radical thematics, he is intended to represent something of a reconciliation of the pervasive thematic conflict between faith and reason.
The fact that the name Razumikhin means "reason" shows Dostoevsky's desire to employ this faculty as a foundational basis for his Christian faith in God. Unlike Sonya, however, Porfiry does this through psychological games. Despite the lack of evidence, he becomes certain Raskolnikov is the murderer following several conversations with him, but gives him the chance to confess voluntarily. He attempts to confuse and provoke the unstable Raskolnikov in an attempt to coerce him to confess. He overhears Raskolnikov's confessions to Sonya and uses this knowledge to torment both Dunya and Raskolnikov, but does not inform the police.
When Dunya tells him she could never love him after attempting to shoot him he lets her go. He tells Sonya that he has made financial arrangements for the Marmeladov children to enter an orphanage after both their parents die , and gives her three thousand rubles, enabling her to follow Raskolnikov to Siberia. Crime and Punishment has a distinct beginning, middle and end. The novel is divided into six parts, with an epilogue. The notion of "intrinsic duality" in Crime and Punishment has been commented upon, with the suggestion that there is a degree of symmetry to the book.
The first half of the novel shows the progressive death of the first ruling principle of his character; the last half, the progressive birth of the new ruling principle. The point of change comes in the very middle of the novel. This compositional balance is achieved by means of the symmetrical distribution of certain key episodes throughout the novel's six parts.
The recurrence of these episodes in the two halves of the novel, as David Bethea has argued, is organized according to a mirror-like principle, whereby the "left" half of the novel reflects the "right" half. The seventh part of the novel, the Epilogue, has attracted much attention and controversy. Some of Dostoevsky's critics have criticized the novel's final pages as superfluous, anti-climactic, unworthy of the rest of the work,  while others have rushed to the defense of the Epilogue, offering various ingenious schemes which conclusively prove its inevitability and necessity.
Steven Cassedy argues that Crime and Punishment "is formally two distinct but closely related, things, namely a particular type of tragedy in the classical Greek mold and a Christian resurrection tale". At the same time, this tragedy contains a Christian component, and the logical demands of this element are met only by the resurrection promised in the Epilogue". Crime and Punishment is written from a third-person omniscient perspective. It is focalized primarily from the point of view of Raskolnikov; however, it does at times switch to the perspective of Svidrigailov, Razumikhin, Peter Petrovich, or Dunya.
This narrative technique, which fuses the narrator very closely with the consciousness and point of view of the central characters of the plot, was original for its period. Franks notes that his identification, through Dostoevsky's use of the time shifts of memory and his manipulation of temporal sequence, begins to approach the later experiments of Henry James , Joseph Conrad , Virginia Woolf , and James Joyce.
A late nineteenth-century reader was, however, accustomed to more orderly and linear types of expository narration. Dostoevsky uses different speech mannerisms and sentences of different length for different characters. Those who use artificial language—Luzhin, for example—are identified as unattractive people. Marmeladov's disintegrating mind is reflected in her language, too. In the original Russian text, the names of the major characters have something of a double meaning , but in translation the subtlety of the Russian language is predominately lost due to major differences in the language structure and culture.
The physical image of crime as a crossing over a barrier or a boundary is lost in translation. So is the religious implication of transgression, which in English refers to a sin rather than a crime. Raskolnikov's dreams have a symbolic meaning, which suggests a psychological view. The dream of the mare being whipped has been suggested as the fullest single expression of the whole novel,  symbolizing gratification and punishment, contemptible motives and contemptible society, depicting the nihilistic destruction of an unfit mare, the gratification therein, and Rodion's disgust and horror, as an example of his conflicted character.
Raskolnikov's disgust and horror is central to the theme of his conflicted character, his guilty conscience, his contempt for society, his rationality of himself as an extraordinary man above greater society, holding authority to kill, and his concept of justified murder.
His reaction is pivotal, provoking his first taking of life toward the rationalization of himself as above greater society. The dream is later mentioned when Raskolnikov talks to Marmeladov. Marmeladov's daughter, morally chaste and devout Sonya, must earn a living as a prostitute for their impoverished family, the result of his alcoholism.
The dream is also a warning, foreshadowing an impending murder and holds several comparisons to his murder of the pawnbroker. The dream occurs after Rodion crosses a bridge leading out of the oppressive heat and dust of Petersburg and into the fresh greenness of the islands.
This symbolizes a corresponding mental crossing, suggesting that Raskolnikov is returning to a state of clarity when he has the dream. In it, he returns to the innocence of his childhood and watches as a group of peasants beat an old mare to death. Therefore, in order for Raskolnikov to find redemption, he must ultimately renounce his theory. In the final pages, Raskolnikov, who at this point is in the prison infirmary, has a feverish dream about a plague of nihilism , that enters Russia and Europe from the east and which spreads senseless dissent Raskolnikov's name alludes to "raskol", dissent and fanatic dedication to "new ideas": Though we don't learn anything about the content of these ideas they clearly disrupt society forever and are seen as exclusively critical assaults on ordinary thinking: Chernyshevsky's What Is to Be Done?
Janko Lavrin , who took part in the revolutions of the World War I era, knew Vladimir Lenin and Leon Trotsky and many others, and later would spend years writing about Dostoevsky's novels and other Russian classics, called this final dream "prophetic in its symbolism". Sonya gives Rodya a cross when he goes to turn himself in and symbolizes the burden Raskolnikov must bear.
Sonya and Lizaveta had exchanged crosses, so originally the cross was Lizaveta's—whom Rodya didn't intend to kill, making it an important symbol of redemption. Sonya's face reminds him of Lizaveta's face, another example of his guilty conscience and symbolizes a shared grief. Self-sacrifice, along with poverty, is a larger theme of the novel.
The desperation of poverty creates a situation where the only way to survive is through self-sacrifice, which Raskolnikov consistently rejects, as part of his philosophical reasoning. For example, he rejects Razumikhin's offer of employment and the idea of his sister's arranged marriage. Raskolnikov originally rejects Sonya's offer to accompany him to the confession but, in a feverish state of mind, sees her following him through the market, and finds power in that idealism. Dostoevsky continues the use of this symbol from his earlier work Notes from Underground where the narrator rants about determinism and logic.
On an exceptionally hot evening early in July a young man came out of the garret in which he lodged in S. Place and walked slowly, as though in hesitation, towards K. The above opening sentence of the novel has a symbolic function: Russian critic Vadim K. Kozhinov argues that the reference to the "exceptionally hot evening" establishes not only the suffocating atmosphere of Saint Petersburg in midsummer but also "the infernal ambience of the crime itself". Evnin regards Crime and Punishment as the first great Russian novel "in which the climactic moments of the action are played out in dirty taverns, on the street, in the sordid back rooms of the poor".
Dostoevsky's Petersburg is the city of unrelieved poverty; "magnificence has no place in it, because magnificence is external, formal abstract, cold". Dostoevsky connects the city's problems to Raskolnikov's thoughts and subsequent actions. Donald Fanger asserts that "the real city It is crowded, stifling, and parched. For example, the great storm in Shakespeare's King Lear reflects the state of the titular character's mind, much like the chaos, disorder and noise of St.
Petersburg reflects the state of Raskolnikov's mind. The color yellow is used throughout the novel to signify suffering and mental illness. Of note, the Russian term for lunatic asylum, "zholti dom", is literally translated as "yellow house". Yellow is also mentioned as the color of Luzchin's ring.
The scripts make use of the RMySQL, Lubridate and Ggplot2 packages for their internal workings, and they will be available upon request to the corresponding author. The attributes, listed in Table 2 , are similar to the temporal concepts explained in Section 3. He attempts to confuse and provoke the unstable Raskolnikov in an attempt to coerce him to confess. However, as the goal of this study was to identify the algorithm most efficient at approximating the temporal distribution, this is not deemed problematic. Martin Boldt performed the experiments. The novel soon attracted the criticism of the liberal and radical critics. The same is true for the aoristic method, as well.
Dostoevsky's letter to Katkov reveals his immediate inspiration, to which he remained faithful even after his original plan evolved into a much more ambitious creation: He thus attacked a peculiar Russian blend of French utopian socialism and Benthamite utilitarianism, which had led to what revolutionaries, such as Nikolai Chernyshevsky , called " rational egoism ". The radicals refused, however, to recognize themselves in the novel's pages Dimitri Pisarev ridiculed the notion that Raskolnikov's ideas could be identified with those of the radicals of his time , since Dostoevsky pursued nihilistic ideas to their most extreme consequences.
The aim of these ideas was altruistic and humanitarian, but these aims were to be achieved by relying on reason and suppressing entirely the spontaneous outflow of Christian pity and compassion. Chernyshevsky's utilitarian ethic proposed that thought and will in Man were subject to the laws of physical science.
Raskolnikov exemplifies all the potentially disastrous hazards contained in such an ideal. Frank notes that "the moral-psychological traits of his character incorporate this antinomy between instinctive kindness, sympathy, and pity on the one hand and, on the other, a proud and idealistic egoism that has become perverted into a contemptuous disdain for the submissive herd". Dostoevsky wants to show that this utilitarian type of reasoning and its conclusions had become widespread and commonplace; they were by no means the solitary invention of Raskolnikov's tormented and disordered mind.
He even becomes fascinated with the majestic image of a Napoleonic personality who, in the interests of a higher social good, believes that he possesses a moral right to kill. Indeed, his "Napoleon-like" plan drags him to a well-calculated murder, the ultimate conclusion of his self-deception with utilitarianism. In his depiction of the Petersburg background, Dostoevsky accentuates the squalor and human wretchedness that pass before Raskolnikov's eyes.
He also uses Raskolnikov's encounter with Marmeladov to present both the heartlessness of Raskolnikov's convictions and the alternative set of values to be set against them. The product of this "freedom", Raskolnikov, is in perpetual revolt against society, himself, and God. Although the remaining parts of the novel had still to be written, an anonymous reviewer wrote that "the novel promises to be one of the most important works of the author of The House of the Dead ".
In his memoirs, the conservative belletrist Nikolay Strakhov recalled that in Russia Crime and Punishment was the literary sensation of The novel soon attracted the criticism of the liberal and radical critics. Yeliseyev sprang to the defense of the Russian student corporations, and wondered, "Has there ever been a case of a student committing murder for the sake of robbery? He measured the novel's excellence by the accuracy and understanding with which Dostoevsky portrayed the contemporary social reality, and focused on what he regarded as inconsistencies in the novel's plot.
Strakhov rejected Pisarev's contention that the theme of environmental determinism was essential to the novel, and pointed out that Dostoevsky's attitude towards his hero was sympathetic: The Garnett translation was the dominant translation for more than 80 years after its publication in When Crime and Punishment came up in their extended interview, Alfred Hitchcock told French director Francois Truffaut that he would never consider filming it.
Hitchcock explained that he could make a great film out of a good book, and even or especially a mediocre book, but never a great book, because the film would always suffer by comparison. Edit this page Read in another language Crime and Punishment. For other uses, see Crime and Punishment disambiguation. I didn't like it myself. A new form, a new plan excited me, and I started all over again.
I wrote [this chapter] with genuine inspiration, but perhaps it is no good; but for them the question is not its literary worth, they are worried about its morality. Here I was in the right—nothing was against morality, and even quite the contrary, but they saw otherwise and, what's more, saw traces of nihilism I took it back, and this revision of a large chapter cost me at least three new chapters of work, judging by the effort and the weariness; but I corrected it and gave it back.
Other characters of the novel are: Shy and retiring, Praskovya Pavlovna does not figure prominently in the course of events. Raskolnikov had been engaged to her daughter, a sickly girl who had died, and Praskovya Pavlovna had granted him extensive credit on the basis of this engagement and a promissory note for roubles.
She had then handed this note to a court councillor named Chebarov, who had claimed the note, causing Raskolnikov to be summoned to the police station the day after his crime. Her bequest of 3, rubles to Dunya allows Dunya to reject Luzhin as a suitor. She drives Sonya into prostitution in a fit of rage, but later regrets it, and beats her children mercilessly, but works ferociously to improve their standard of living.