There is too much literature on the subject for a complete survey here, and reliance has been placed on some existing reviews. Present views on the subject are polarised. Particular studies have been selected for mention to indicate why, and to facilitate resolution. Nearly all of the available material is from the USA and the UK. Useful formal studies have presumably been done in other countries but few indications of this were found in Internet searches or scans of reference lists in papers cited in this work. A more thorough search of the literature including eight large bibliographic databases by Farrington and Welsh (2002a,b) [34,35] produced a more extensive collection, but again mostly of UK and USA origin.

A standard convention is followed in this document. If an increase in light is accompanied by an increase in crime, a positive correlation, it is called a positive association or effect, naturally. This possibility is not often mentioned in the crime prevention literature but if it is, it tends to be called a negative effect. To follow that usage here would be to perpetuate confusion. Increased crime accompanying increased light is therefore a positive association. A decrease in light accompanied by a decrease in crime is a positive association also. Increased light and decreased crime is a negative or inverse association, and so is decreased light and increased crime. Useful or beneficial effects are unambiguous - they mean a reduction of crime in any circumstances.


The National Institute of Law Enforcement and Criminal Justice of the US Department of Justice presented a thorough study of sixty street lighting projects to the US Congress in February 1977 (Tien, O'Donnell, Barnet, Mirchandani and Pitu 1977 [107], IDA IS63 1998 [51]). The abstract states, in part:

``In particular, while there is no statistically significant evidence that street lighting impacts the level of crime, especially if crime displacement is taken into account, there is a strong indication that increased lighting - perhaps lighting uniformity - decreases the fear of crime.''

Twenty years later, the National Institute of Justice of the US Department of Justice presented an even more comprehensive report (Sherman, Gottfredson, MacKenzie, Eck, Reuter and Bushway 1997 [102]) on crime prevention to the US Congress in February 1997. The following quotes are from `Conclusions for Open Urban Places' in Chapter 7 by Eck (1997) [31]:

``Not much has changed since Tien and his colleagues (1979) [] gave their critical assessment of the impact of lighting on crime.''

``We may speculate that lighting is effective in some places, ineffective in others, and counter productive in still other circumstances.''

``Consider lighting at outside ATM machines, for example. An ATM user might feel safer when the ATM and its immediate surrounding area are well lit. However, this same lighting makes the patron more visible to passing offenders. Who the lighting serves is unclear.''

``Lighting has received considerable attention. Yet, evaluation designs are weak and the results are mixed. We can have very little confidence that improved lighting prevents crime, particularly since we do not know if offenders use lighting to their advantage. In the absence of better theories about when and where lighting can be effective, and rigorous evaluations of plausible lighting interventions, we cannot make any scientific assertions regarding the effectiveness of lighting. In short, the effectiveness of lighting is unknown.''

Eck (2002) [32] has since revised his views:

``The recent lighting studies from Great Britain appear to remove the lingering doubts about lighting's efficacy. Lighting appears to work in public areas, especially residential communities. Generalizing beyond these types of settings is highly speculative, given the rudimentary nature of current lighting theory (Painter and Farrington, 1997 [82]). Lighting may be effective in some places, ineffective in others and counter-productive in still other circumstances. The problematic relationship between lighting and crime increases when one considers that offenders need light to detect potential targets in low-risk situations (Fleming and Burrows, 1986 [38]). As Pease (1999) [90] correctly points out, we should address the specific conditions where lighting is effective, rather than assume it is always effective.''

That lighting is sometimes effective against crime may be a truism. What needs to be resolved is the extent of any net benefit in practice.

Understandably, most people want the incidence of crime to be reduced. It appears to be widely believed by the public that more and brighter outdoor lighting would help. Of course, extending the belief to its ultimate stage means there should be little or no crime in the bright outdoor lighting conditions of daytime, but that is far from the facts. For example, 54 % of violent crime in the USA occurred between 6 am and 6 pm, and only 20 % of rapes involve unknown assailants at night (BJS 1999 [10]). Only 35 % of all burglaries in the USA are reported to have occurred at night, or 48 % of all burglaries for which the time of occurrence is known (UCR 1996 [111]). Note that these figures are for all reported crime in the whole of the USA, which gives them much face validity.4

In more recent details of crime by region in New Jersey (DLPS 2000 [29]), only the burglary data are partitioned into night, day and unknown time. The day rate for burglary is almost as much as the combined rate for night plus unknown time.

Graphs of percentage of violent crime as a function of time of day for years 1991 through 1996 are given in a US Juvenile Justice Bulletin (NCJRS 1999 [74]). Compared with adults, juveniles (under 18) tend to commit a greater proportion of violent crimes in the hours immediately after school gets out on school days. The difference is less pronounced for robberies. The juvenile violent crime rate on non-school days tends to peak in the evening, an hour or so earlier than the peak at about 10 pm to 11 pm for adult violent crime. On all days, the level of juvenile violence is already low during the time of day that juvenile curfew laws are in effect. All of the curves show a minimum at about 5 am to 6 am. Apart from the school-out peak, the crime rate rises more or less steadily during the day and early evening and falls steadily but more steeply after midnight. There is no obvious relationship to the large changes in light level over the 24 hours, other than the location of the peak in hours of natural darkness. Social factors such as the school attendance hours and the preponderance of daytime work and evening leisure time would appear to have a larger influence than light levels.

Rape and domestic violence are more likely to occur after sundown (Cohn 1993 [23]). Cohn noted that although domestic violence tends to be impulsive, rapes are often planned well in advance. Furthermore, social factors and biological photoperiodicities provide alternatives to explanations based on the direct visual effects of light-dark variation. Data from Maguire and Pastore (2002, Table 3.181) [64] indicate that about two thirds of all reported sexual assault and rape cases occur indoors where outdoor conditions could hardly have had much direct influence. Domestic violence also tends to occur indoors where illumination from outside in daytime and from artificial light at night is generally much brighter than it is outdoors at night.

Quinet and Nunn (1998) [95] analysed the number of calls for police service before and after additional streetlights were placed in Indianapolis neighbourhoods. Their results on the deterrent effect of increased lighting were inconclusive at best, but they didn't quite say so. They claimed that disentangling the effects of social disorganisation, police initiatives and behaviour patterns was beyond the scope of work on crime and the physical environment.

Schumacher and Leitner (1999) [98] described spatial crime displacement5 resulting from the most recent wave of urban renewal in Baltimore. (Perhaps, instead, it was an established phenomenon that happened to be observed during the urban renewal.) They expected that increased presence of security personnel, increased street lighting and increased pedestrian traffic would discourage criminal activity in redeveloped areas. ``However, the crime rates throughout the city - and in the downtown, overall - remained at high levels despite the redevelopment. This suggested that the renewal programs did not eliminate, but merely displaced, the criminal activity...'' possibly thereby indirectly harming the neighbourhoods affected by the displacement. They acknowledged the undesirability of such displacements but pointed out that the city's downtown renewal programs have generated a great deal of revenue and improved the city's image.

Loukaitou-Sideris, Liggett, Iseki and Thurlow (2001) [63] studied the effect of the built environment on crime at 60 bus stops in downtown Los Angeles. Although there were substantial differences between stops in crime incidence, no relationship was found between crime and the paucity of pedestrian lighting at the stop. This seemed to surprise the authors, who wrote ``...we can by no means conclude that lighting is not important. For one, we did not account for lighting from near-by establishments. Also, the presence of a pedestrian light did not always mean that this light was lit at night.'' Sherman et al. (1997) [102] is not even listed in the references. The same applies to Sherman and Weisburd (1995) [101], who stated ``Bus stops, pay telephones, and intensive lighting were common features of hot spots''. Numerous other publications about this LA bus stop work were found in an Internet search. Several of these mention lighting as a crime prevention method or, apparently without justification, claim less crime at stops equipped with shelters and adequate lighting (eg Benson 2000 [9]).

Summing up, relatively short-term studies in the USA appear to indicate that there is no clear overall effect of the amount of outdoor light or lighting either in increasing or decreasing actual crime rates . This confirms earlier assessments (eg IDA IS51 1992 [50], IDA IS63 1998 [51]). Farrington and Welsh (2002a,b) [34,35] concluded that there was a relatively small beneficial effect. Their work is discussed in Chapter 5 below.


At the 1989 annual conference of the (UK) Institution of Lighting Engineers (ILE 1989 [52]), increased lighting in a multi-racial inner city area was reported as producing a 16% reduction in robberies, theft from cars, burglaries and vandalism in the following 12 months and a further 10% in the next 12 months. Shaftoe and Osborn (1996) [100] examined this work and concluded:

There ``were some reductions in crimes committed at night but this could not be associated with the lighting improvements... It may have been that the lighting improvements did reduce crime in these streets, with some displacement to neighbouring streets in the same beat area. However, if all the schemes achieved was this kind of local displacement, it would be difficult to claim they had been a success.''

The ILE collected details of six street relighting schemes in the UK in 1991. These involved the replacement of 35 W low-pressure sodium (LPS) luminaires6 or mercury-vapour luminaires with 70 W high-pressure sodium (HPS) luminaires7 (Fisher 1997 [37]). Measured illuminances increased by anything from 1.9 to 40 times. The replacements changed the colour of the light from the quasi-monochromatic yellow of LPS or bluish-white of mercury vapour to the orange-white of HPS. HPS lamps are physically much more compact than LPS, allowing luminaire designers more scope for beam shaping and shading. The resulting glare also tends to be worse because HPS lamps have a much greater intrinsic luminance. In field experiments, it would be difficult to eliminate, counterbalance or otherwise disentangle any effects caused by these factors, not all of which are improvements of the sort that might be supposed to reduce crime or the fear of crime.

The ILE (1989) [52] claim appears to have been based at least partly on the early work of Painter. Ramsay and Newton (1991) [96] examined data from four reports by Painter about three small-scale increased lighting projects in parts of London and found important shortcomings in methodology and analysis, going so far as to append a ``statistical health warning'' to a table of data from the Edmonton project (Ramsay and Newton (1991), p 28 [96]). They queried the findings that the lighting changes had resulted in reductions in total (all-hours) crime, but accepted that the changes had been accompanied by substantial observed increases in utilisation of the relit area in one case. They noted that the projects had been funded by the local lighting industry.

Ramsay and Newton (1991) [96] reviewed the literature and concluded that better street lighting had little if any demonstrated effect on actual crime. Nevertheless, fear of crime did diminish with brighter lighting and there was considerable public faith in lighting as a crime prevention measure. In interviews of over 300 experienced burglars, lighting was virtually not mentioned as a deterrent. In interviews with 45 street robbers, conditions had been dark in only about one eighth of all the offences, time of day was regarded as unimportant and only two robbers actually mentioned darkness as a contributing factor. There was a similar lack of concern about lighting in the choice of location for robbery. In interviews of nearly a hundred car thieves, only one mentioned unlit parking places as assisting the theft but nearly a quarter of the total mentioned seclusion. In both sets of interviews, being seen committing the crime was not of much concern to the offenders, as bystanders were considered generally to take no notice or to take no action.

Atkins, Husain and Storey (1991) [3] conducted a large and apparently thorough relighting study in Wandsworth, a London Borough. They found that the brighter lighting did not significantly change the relative proportion of day and night recorded crime, but interviews indicated that people in the relit areas did feel safer at night.

In a Glasgow neighbourhood, Nair, Ditton and Phillips (1993) [71] studied the effect of relighting the area surrounding the homes of respondents together with other environmental and security changes. They found ``little improvement in victimisation or fear of victimisation could be documented'' and ``It is more likely that improved lighting is no panacea for all ills, and may only be effective under certain conditions''.

Tilley and Webb (1994) [109] mentioned expenditure on increased lighting as an anti-crime measure in the UK but they found no evidence to justify this in the towns they studied.

Barker and Bridgeman (1994) [5] described an attempt by British Telecom in 1985 to reduce public telephone vandalism by fitting 24-hour lighting to the booths. The immediate result was the loss of 2000 light globes a year. Barker and Bridgeman provided a bibliography for guidance in the use of security lighting and other measures to prevent vandalism, but there is nothing in their report to justify use of lighting for this purpose.

Fisher (1997) [37] described features of a paper by Painter (1994a) [79], given at a conference of the Institution of Lighting Engineers. Relighting of streets increased pedestrian usage by males and by females. In three schemes, usage of relit roads and paths had increased between 34 and 101 %. Beneficial effects of relighting on crime were also reported for a housing estate in Dudley, UK.

Eck (1997) [31] summarised Painter (1994b) [80]:

``She examined lighting improvements on two separate street segments and a footpath, all located in `crime prone' areas within London. Pedestrians were interviewed before and after the lighting improvement. All interviews were conducted after dark and were completed within 6 weeks of the relighting. No interviews were conducted in control areas. Substantial reductions in robberies, auto crimes, and threats were reported in two sites (86 percent, 79 percent). These crimes were eliminated in the third site, but the number of crimes before relighting was small so this could have been the result of other factors.''

The size of the reductions will be of interest in connection with Section 5.2 below.

A score on the Scientific Methods Scale was applied to papers reviewed in the Sherman et al. (1997) [102] report. The score depends solely on the experimental design, and no account is taken of adverse factors such as experimenter bias or unrecognised confounding of variables such as lighting intensity, spatial distribution, colour and glare. The design in Painter (1994b) [80] Painter (1994b) scored 2 (Eck 1997 [31]) on an ascending ordinal scale of 1 to 5, effectively meaning it was weak.

Shaftoe and Osborn (1996) [100] described a study of lighting improvements to individual streets and small areas with high crime rates in a multi-racial inner city part of Bristol. The purpose of the lighting changes was to reduce the fear of crime and actual crime in the high-crime localities. The result was ``a patchwork of original lighting, new low pressure sodium lamps and, in particularly vulnerable areas, high pressure sodium lamps''. No discernable reductions in recorded crime could be attributed to the lighting changes. Farrington and Welsh (2002a,b) [34,35] found the study difficult to interpret because the lighting changes were introduced over 28 months. Nevertheless, they managed to extract quantitative information indicating that the changes were effective in reducing crimes other than robbery.

Painter and Farrington (1997) [82] described the Dudley study in which crime victimisation survey interviews were done before and after part of a residential estate was relit. The results indicated a reduction of crime in the relit area in the daytime as well as at night. Painter and Farrington (1999a) [83] described a somewhat similar experiment in Stoke-on-Trent. The results indicated more of a victimisation decrease for nighttime than daytime in the experimental area after relighting. These and related papers are reviewed in detail in Chapter 4 below

An extensive statistical study of crime in Bexley, UK by Pascoe and Harrington-Lynn (1998) [88] indicated that internal and external lighting had little or no influence on crime rates.

Several other apparently relevant reports on this section topic are listed on the web pages of the Scottish Office Central Research Unit (SOCRU 2002 [104]).8


If a nearby light assists a burglar to defeat a door lock or force a window at night, the light has provided direct physical assistance in the commission of crime. If the light deters the burglar from starting, or makes the illegal activity visible to a neighbour who alerts the police, the light has had a direct anti-crime effect. Immediacy appears to be an essential characteristic of a direct effect in this context. If bright outdoor lighting somewhere attracts potential criminals who, individually or in company, are motivated or enabled to commit crimes subsequently at this place or elsewhere, this is described here as an indirect effect of lighting in aiding the commission of crime.9 A time delay appears to be an inherent characteristic of indirect effects.

A daytime effect on crime, increase or decrease, by what some commentators have called `switched-off outdoor lighting' can hardly be regarded as anything but far-fetched as a direct effect. Nevertheless, there are known environmental and economic effects on crime incidence, eg weak seasonal effects (Jochelson 1997 [54], DCPC 2001 [25]) or, say, the effects of currency exchange rate changes on tourist numbers and hence numbers of tourists as crime victims. These are indirect effects on crime. Light and lighting may possibly play a part in the first example.

The concept of indirect effects can be considered as complementary to the known beneficial and adverse direct effects of light on crime at night. Social or economic effects of changed night lighting on daytime crime seem perfectly reasonable to discuss, as Painter, Pease (1998, 1999) [81,89,90] and several others have done. But so far, the only sort of indirect effect mentioned appears to have been a beneficial daytime effect. There is no reason to expect that indirect effects cannot also act at night. It would depend on the time course of development and decay of the effect. Nor is there any reason to suppose that they can only be beneficial.

In the absence of firm knowledge or good reason about the direction of effects being investigated, it would seem important to keep an open mind about the directions for day and night in the course of analysis. It might be possible, for instance, to have an indirect effect in which the night and day segments effectively had opposite signs or different magnitudes, or both.

Switching a light on at night may start to aid or deter a burglar within milliseconds. Failure of lighting in an area may affect transport and result in crowds or deserted areas that change the pattern of crime over hours. Lighting in a public place may have a cumulative effect over years in determining pedestrian usage and opportunities for crime. The time for light to have an appreciable effect could well be a continuum, in which case the distinction between direct and indirect effects would be arbitrary. The distinction does seem to have practical value, however, without going so far as to define time constants for growth and decay of any particular effect. For the present purpose, it seems adequate to define direct effects of light on crime as those having a substantial influence on a criminal act at any time during its decision, commission or escape phases.


This section is about a set of quasi-experiments devised specifically to illustrate difficulties that can arise with the before-after (or pre-post) experimental design incorporating experimental and control areas, as has commonly been used in lighting and crime studies.

These ready-made quasi-experiments have been constructed using real-world data for consecutive years, viz UCR total crime data for 1999 and 2000 in each of the 21 counties in New Jersey (DLPS 2000 [29]), along with a map and county population data from the US Census Bureau (2002) [112].

The total number of crimes for each county was converted to a crime rate per 100 000 of the county population,10 and the 21 counties were sorted in order of ascending crime rate for 1999. Pairs of counties were identified in which the crime rates matched within 10 % of the larger crime rate of the pair. The minimum number of pairs considered adequate for this demonstration was chosen as ten in advance. Pairs with a shared length of boundary were identified from the New Jersey map. Here these are called contiguous pairs. As there were only five of them, pairs with a physical separation of one county were also selected. There were five of these pairs also, making up the required minimum. They are shown in Table 1, listed in ascending order of total crime rate for the first county of each pair, with the contiguous pairs identified by italics.

The idea is to consider each pair as a quasi-experiment in which a treatment is applied to one of the pair at the end of 1999. Here the first member in each case was selected as the experimental or treated county and the other is the control. The outcome of this demonstration is not dependent on this selection. Control counties need to be a good match with experimental counties, which is why they were chosen to be adjacent or nearby and to have comparable crime rates before the treatment. The crime figures after the treatment are the actual values for 2000. Of course, there was no deliberate treatment with lighting or anything else, ie null treatment.

For each of the ten pairs, the relative change in crime from 1999 to 2000 was calculated as the first county's ratio of change divided by the second county's ratio of change.11These values are given in Table 1. The probability of each result arising by chance was determined with a $\chi^2$ test. The contiguous pairs returned small changes that could be expected as chance results. However, four of the remaining five pairs exhibited unexpectedly substantial changes, some positive and some negative. No deliberate interventions (treatment) or other reasons for this are known, and the differences have arisen through interference from real-world conditions that are unknown here, apart from the one-county separation.

TABLE 1. Crime change in pairs of New Jersey Counties
County Popula- Crime Crime Crime Relative Probability
Pairs tion Rate 1999 2000 Change in $\chi^2$ (1 df)
/100 000 Crime, %
Sussex 146671 522.9 767 947 49.1 29.55 p$<$0.0001
Hunterdon 125135 576.2 721 597
Warren 105765 731.8 774 740 -6.5 1.415 ns
Morris 472859 773 3655 3737
Monmouth 622977 1020.3 6356 6288 -1.8 0.495 ns
Ocean 527207 1055.2 5563 5605
Monmouth 622977 1020.3 6356 6288 -0.4 0.023 ns
Burlington 432121 1124.2 4858 4826
Ocean 527207 1055.2 5563 5605 1.4 0.903 ns
Burlington 432121 1124.2 4858 4826
Burlington 432121 1124.2 4858 4826 -4.2 3.000 p$<$0.1
Middlesex 757191 1127 9291 9638
Passaic 491077 1727 8481 7585 -13.6 46.12 p$<$0.0001
Union 523396 1750.3 9161 9484
Cape May 102352 1948.2 1994 2041 12.0 10.78 p$<$0.005
Camden 509350 2062.4 10505 9601
Cape May 102352 1948.2 1994 2041 1.9 0.226 ns
Cumberland 146289 2153.3 3150 3163
Camden 509350 2062.4 10505 9601 -9.0 10.63 p$<$0.005
Cumberland 146289 2153.3 3150 3163


Schumacher and Leitner (1999) [98] described spatial crime displacement observed during urban renewal in Baltimore. They expected that increased presence of security personnel, increased street lighting and increased pedestrian traffic in redeveloped areas would discourage criminal activity, but this did not happen:

``However, the crime rates throughout the city - and in the downtown, overall - remained at high levels despite the redevelopment. This suggested that the renewal programs did not eliminate, but merely displaced, the criminal activity...''

A key result in Schumacher and Leitner (1999) [98] is the spatial and temporal volatility and dynamism of the burglary hotspots they tracked with a geographic information system. Substantial changes in number, size and position of the hotspots took place in each of the two-year intervals shown from 1988 to 1996, and a substantial part of central Baltimore was affected by these changes in that time. This behaviour was ascribed to crime displacement for socio-economic reasons and to areas with reduced risk of apprehension. If the before-after experimental-control type of experiment and analysis had been applied with null treatment to these burglary hotspots with nearby areas used as controls, the experimental area would tend to show a relative decrease in burglary, a false benefit, because of displacement. This is a regression to the mean phenomenon. The control area would have shown an increase, no effect or a reduction in crime. Without spatial analysis, any appreciable change could be interpreted as either displacement of crime or diffusion of benefit from the experimental area, as is often claimed in such lighting and crime experiments.

Given the widespread belief that increased lighting will prevent crime, municipal officers who select areas for relighting might reasonably be expected to favour areas with increased crime instead of deciding purely on the basis of electrical or visibility criteria. Police advice may be sought or proffered.12Local officials may be reluctant to state the strategy publicly as tantamount to labelling identifiable areas as crime-prone, but politicians seem to be less inhibited, especially during election campaigns.

Any such bias in the relighting process tends to result in a confirmatory bias in the results of any subsequent investigation into the efficacy of relighting for crime prevention. The process therefore has a net positive feedback, which encourages its continuation.

All before-after lighting and crime experiments without time-series spatial observations and analysis of ambient light as well as crime should now be regarded as suspect if not invalid. The methods used to date demonstrate how not to study the relationships between street lighting and crime, regardless of the number of crime measures and the quality of statistical analysis applied.

B. A. J. Clark