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1

Doran B.J., Burgess M.B., 2012, Putting Fear of Crime on the Map. Investigating Perceptions of Crime Using Geographic Information Systems, Springer, New York–Dordrecht–Heidelberg–London, ss. 283

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Mordwa S., Katedra Gospodarki Przestrzennej i Planowania Przestrzennego U. Ł.

Acta Universitatis Lodziensis. Folia Geographica Socio-Oeconomica

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2012

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issue 12

XX

Recenzja

2

Kradzieże w przestrzeni Łodzi

100%

Mordwa S., Katedra Gospodarki Przestrzennej i Planowania Przestrzennego U. Ł.

Acta Universitatis Lodziensis. Folia Geographica Socio-Oeconomica

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2011

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issue 11

EN

For the city of Łódź, like in the other polish cities, a typical phenomenon of crime offences is a domination of the thefts (larcenies) among the total number of crimes. Larceny-theft is not only the most common crime in Łódź, but it is the offense category which is very socially burdensome. Larceny-theft entails a variety of crimes characterized by the taking away of someone else's property. Examples of larceny-theft include pick-pocketing, residential thefts and car theft. The main aim of the paper is to show the spatial diversity of property offences in Łódź in the period of 2006–2009. The following issues are presented in the article: a) geographic distribution of thefts in local pattern in Łódź – fig. 1; b) spatial autocorrelation (which conducive to determining whether there are any local level relationships between chosen crime and place exist) – fig. 2; c) spatial regression of chosen crimes (for identifying spatial effects in the dependent variable, i.e. intensity of thefts, and among the independent (demographic) variables – fig. 3 and 4; d) daily and weekly rhythms of offences – fig. 5. The crime rates (number of crimes per 1 000 residents) committed in Łódź shows not clear spatial diversity. Some of them, i.e. pick-pocketing, have observable concentration in the central core of the city, but in case of car thefts, this trend does not work (fig. 5b versus 5c). Knowing the distribution of the crime risk for each part of the city such situation can be avoided by directing the police resources especially in these hot spots (identifying by analyzing of spatial autocorrelation). In case of the regression model, in which the number of larcenies-thefts was a dependent variable, population density and women participation in the local population was a significant enough. In case of crimes some long standing regularity concerning their daily and weekly schedule can be observed. As it shows the fig. 5, days most threatened of larcenies-thefts are Mondays till Fridays. The pickpocket’s works in the morning and in the evening, but more car thefts occurs by night.

PL

Kradzieże należą do grupy przestępstw przeciwko mieniu. Ich uciążliwość w przestrzeni miasta polega na ich powszechności i dokuczliwości społecznej wynikających ze znacznej liczby dokonywanych tego typu czynów. W tym artykule kradzieże rozpatrywane będą pod kątem dystrybucji przestrzennej, autokorelacji sektorów zagrożonych tymi czynami, regresji przestrzennej oraz zmienności w czasie.

3

Struktura i typologia przestrzenna przestępczości w Polsce - przykład wykorzystania walidacji liczby skupień w metodzie k-średnich

100%

Mordwa S., Katedra Gospodarki Przestrzennej i Planowania Przestrzennego U. Ł.

Acta Universitatis Lodziensis. Folia Geographica Socio-Oeconomica

|

2012

|

issue 12

EN

This paper presents an overview of the present state of crime in Poland, and the main goals are to detect the distribution and clustering of crimes, and to identify high rate regions. It is a common practice to compare countries, regions or cities in terms of safety performance, and to rank them in terms of risk indicators such as the crime rates, which are often expressed as the number of crime per 10 000 persons. This paper examines the subregional disparities hidden behind the national statistics (www.stat.gov.pl) on some crime statistics in Poland with GIS and spatial analyses. The first step to identify possible patterns of crime rates is to map the crime phenomenon. The distribution of relative rates of crimes in the Polish subregions were presented in fig. 1. The analysis of all the committed in Poland crimes in years 2008‒2011, maintaining the same territorial level of researches (66 subregions; i. e. NTS-3 European statistical level) revealed an interesting crime geographies picture, which in turn, increased the assumption of not homogenous spatial distributions. Talking into consideration all the crimes in Poland there were 294 crimes per 10 000 inhabitants, but in large cities (Wrocław – 561; katowicki – 527; trójmiejski – 509; Poznań ‒ 488) and in north-western subregions the value was more than 50% higher than the Polish average. In the eastern subregions the number of crimes was the half of the Polish average value (krośnieński – 169; przemyski – 174; rzeszowski ‒ 179). The changes of crime rates in 2008‒2011 years were shown in fig. 2. Statistics of growth rates in north subregions there was a more than 2% decrease (koszaliński, Szczecin, szczeciński, słupski, ełcki), in the central and southern subregions a strong increase (nearly 10%; nowosądecki, Kraków, poznański, tyski). The statistics for whole Poland indicates an increase of 2%. If the number of solved crimes is related to the estimated total number of crimes, the performance of police is very weak – generally in Poland it is only about 70%. In the subregions of largest Polish cities, detectability of crimes is much lower in comparison to other subregions (fig. 3). Crimes can be distinguished by four categories: criminal offences (the main two groups are: against property and against life and health), commercial crimes, traffic and the so-called “others”. In this paper, some kinds of crime activities were examined. Criminal crimes against life and health include crimes like homicide, deliberate wounding, assault, damage to health. Assaults and damages to health are, in general, committed by male adults; in recent years, more types of violent crimes are committed by the underaged. The subregions of Śląsk, West and North Poland, had relatively more violent crimes per 10 000 population (fig. 5). Category of crimes against property consists of various forms of theft, burglary and robbery, theft with assault, criminal coercion. Crimes against property account for 53% of total crimes and for 78% of criminal offences. In general, property crimes per 10 000 residents are strongly overrepresented in the crime profiles of the subregions of West and North Poland, Śląsk region (katowicki – 351; gliwicki ‒ 256) and all largest Polish cities (trójmiejski – 303; Kraków – 292; Poznań ‒ 291), but typically lower in the subregions of East (puławski ‒ 72; przemyski – 72; chełmsko-zamojski – 77; krośnieński) and Central (sieradzki) Poland (fig. 6). Commercial crime is the crime of “respectable” people. There is a very broad rande of examples of white-collar crime. Individuals (for example computer criminals, taxes), small businesses (for example VAT taxes), large corporations (for example creative bookkeeping), and governmental agencies (for example corruption) may get involved. Some researchers argue that commercial crime is even more serious than, the violent acts of the street criminals, because is like an insidious corrosion that slowly but surely destroys national economy. White-collar crimes per 10 000 people are overrepresented in subregions which are located in different parts of Poland, such as: Trójmiasto (161 crimes), sandomiersko-jędrzejowski (82), katowicki (74), bytomski, gorzowski and bydgosko-toruński (fig. 7). Traffic crimes – Polish law distinguishes between traffic violations (for example, driving at higher speeds than allowed, driving without a licence, or ignoring red traffic lights) and more serious traffic crimes, e.g. alcohol driving, accidents with victims, hit-and-run accidents. The rates of solved traffic crimes are typically low, compared to criminal crime rates. Traffic crimes per 10 000 people are overrepresented in the western (gorzowski – 78; zielonogórski ‒ 66), eastern (bialski ‒ 77) and central (sieradzki, skierniewicki) subregions of Poland, with the exception of the subregions which include large Polish cities (fig. 8). Basing on the crime patterns of the various categories of offences described above, the k-means cluster statistical technique has been used in the spatial typology analysis. Despite its popularity for general clustering, k-means suffers from major shortcoming: the number of clusters k has to be supplied by the user. In this paper a simple index for validating of the number of clusters has been used. It was the Davies-Bouldin Index (DBI). As it’s shown in fig. 9, the proper number of clusters is 4. As an example, consider partitions into 4 clusters of 66 Polish subregions (using police date crime) see the fig. 10. The different types include subregions: 1) the largest Polish cities (with highest criminal and commercial offences rates and the lowest rates of detectability); 2) located nearby the largest Polish cities (crime rates are similar to the Polish averages); 3) of North and West Poland (with highest criminal, traffic and commercial offences rates and high rates of detectability); 4) of Central and East Poland (with lowest criminal and commercial offences levels and highest rates of detectability).

PL

Celem tego opracowania jest przedstawienie zróżnicowania przestrzennego przestępczości na poziomie podregionów w Polsce. Pod uwagę wzięto główne rodzaje przestępstw: kryminalne, gospodarcze i drogowe. Ponieważ delikty te wykazują odmienne rozkłady przestrzenne postanowiono przedstawić typologię przestrzenną, dzięki której potwierdzono istniejące różnice natężenia zjawisk przestępczych między Polską wschodnią i zachodnią oraz niechlubnie wyróżniającą się pozycję dużych miast.

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3 Acta Universitatis Lodziensis. Folia Geographica Socio-Oeconomica

3 Katedra Gospodarki Przestrzennej i Planowania Przestrzennego U. Ł.

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Doran B.J., Burgess M.B., 2012, Putting Fear of Crime on the Map. Investigating Perceptions of Crime Using Geographic Information Systems, Springer, New York–Dordrecht–Heidelberg–London, ss. 283

Kradzieże w przestrzeni Łodzi

Struktura i typologia przestrzenna przestępczości w Polsce - przykład wykorzystania walidacji liczby skupień w metodzie k-średnich