Data and study area

As data received from the council is not available for all environmental issues, in this section, only ’rubbish’ is used rather than all signs of disorder. So only reports of rubbish are considered. I will therefore evaluate objective SSO mea- surements of rubbish versus subjective questionnaire measurements of rubbish versus complaints about rubbish in the first section. Rubbish features promi- nently in the signal crimes narrative. Innes (2014) found that ”the dumping of rubbish signalled to residents that an area is ’deteriorating’ ”(p.29). Further, litter has been found to be a form of disorder impacting upon a lot of people, but fairly diffusely (contrastingly, for example being ’intimidated and pestered’ is not something many encounter, although those who do are more intensely affected by it) (Innes, 2014). Evidently, litter is something many people experi- ence, and has the potential to be interpreted as a signal disorder, yet will often not be interpreted so. Therefore an SSO measure of litter can be hypothesised

6.1. Testing different measures of disorder 125 to over-estimate the extent of signal disorder encounters with the issue. Litter is also the most commonly reported environmental issue that can be considered an instance of disorder (enviro-ASB). Overall there were 182 FMS reports of litter in Camden.

The unique data to this chapter of SSO reports of rubbish is provided by Camden council, and contains systematically collected data about instances of rubbish in the environment. This is collected by Council monitoring officers, who patrol the borough observing instances of rubbish. They typically log be- tween 500-1000 reports of litter per month. For the study period of 4 years (to match the FMS and METPAS data) there were a total of 46,262 reports made by Camden officers. This data represents the SSO method of collect- ing observed instances of disorder (see Chapter 2, Section 2.3 for discussion of enviro-ASB, and Chapter 4, Section 4.1 for distribution of reports in various categories).

Another data set provided by Camden council contains all complaints made to the council about rubbish for the same time period. This is useful, as it can be used to compare with FMS reports. As discussed in Chapter 3, Subsec- tion 3.3.4, crowdsourced data can be biased in who it represents. It can now be compared against reporting of complaints through all modes to the council, to see how wide the gap between the two sources of data on complaints really is. There are a total of 14,610 reports made by the public about litter within the study time period of 4 years. These complaints were made using online plat- forms, through sending emails, and over the phone. The majority were made over the phone, making up 84.3 per cent of all complaints. The second most common way for a complaint to come in to the council is via email (13.6%). FMS reports sent to the council would be counted in this category. A sepa- rate channel for reporting is Camden Council’s own problem reporting online tool, which made up just 1.6% of complaints. 0.4 per cent were made through calls to the Emergency Telephone Service, which means it was a phone call outside of normal working hours (08:00 to 18:00, Monday to Friday), and can come from the police, or a member of the public. The remaining 0.1 per cent were made by people who had to be contacted by a member of the council’s

administration team (for example to follow up on an initial complaint) or through a partnership of local businesses.

To measure perceived levels of rubbish in the area, as mentioned in Chap- ter 3, the METPAS is used as a traditional survey measure. The question used here is worded: ’How much of a problem is rubbish or litter lying around?’. This data represents the purely subjective measure, collected through traditional survey questionnaire approaches. The dataset contains a total of 1174 respon- dents across the borough. Possible responses are ’Very big problem’, ’Fairly big problem’, ’Not a very big problem’, and ’Not a problem at all’. Responses were coded 1 to 4, with 1 being ’Not a problem at all’ and 4 being ’Very big problem’. The aggregate scores for each neighbourhood were calculated by taking the median of the responses from residents within each neighbourhood, in line with Likert scale questionnaire analysis best practice (Clason and Dor- mody, 1994; Boone and Boone, 2012; Johns, 2010). The median rather than the mean is taken, because this is not a composite score, rather the answer to only one question, so it is an ordinal variable, of ranked qualitative answers. Higher scores mean that the neighbourhood residents perceive rubbish to be more of a problem.

Due to availability of SSO data this chapter also restricts the spatial cov- erage to the London borough of Camden only, rather than all of London, as is the case in the other chapters. This case study area covers approximately 22 square kilometres in inner London, with almost 210,000 people living in the borough at the time of writing. In terms of socio-economic make-up, it is one of the most polarised boroughs in London with some of the wealthiest areas in England as well as some of the most deprived. Overall recorded crime lev- els are above the average for London (Camden Council Sites Team, 2012). With these characteristics, this borough presents a good representation of var- ious land uses and populations, as well as an above-average crime rate, which might also indicate a higher presence of signs of disorder, providing enough data to make comparisons between the different methods for gathering infor- mation. The borough of Camden is made up of 133 Lower Super Output Areas (LSOAs) which are our unit of analysis (see Chapter 3, Section 3.6).

6.1. Testing different measures of disorder 127 In terms of analytical approach, as in the previous chapter, a spatial regres- sion model is used, because it is likely that there will be spatial dependence in the data - it is reasonable to assume that neighbourhoods that are near each other have more similar characteristics than they do with neighbourhoods fur- ther away. Following the decision process outlined by Anselin (2004), a spatial error was deemed more appropriate for this analysis (p.199). Spatial error ac- counts for (spatially correlated) co-variates, that if left unattended would affect inference.

Subsection 6.1.2 uses the FMS and council complaints data to investigate first the extent to which complaints made through FMS reflect complaints made through other channels. Then in Section 6.1.3 I move on to use FMS reports about litter to see whether complaints (made through FMS) reflect either of the two traditional measures of disorder in neighbourhoods. I hypothesise that it will be associated with both, but directly mirror neither, and instead point to the measurement of something that can relate to both observed disorder (as it is something tangible in the environment) as well as perceived (something identified as a problem by the person reporting it).