Latest Contributions by CharlotteJ

Oh thank you, that's great ... View more

And thank you for your help ... View more

Ok then Yes, it sounds like using chi makes more sense. My advisor wasn't sure which test would be best to use so I thought I'd just discuss it again with her once I'd decided which one would be more suitable. Ah ok then, thank you, I'll take a look at that website! ... View more

Ok then, I'll give it a go if that would be more appropriate. And thank you for the link. But just for future reference, I've done quite a bit of google searching but am still a bit confused as to whether it is alright to use 2 ratio variables to carry out spearman's rank. Is it ok to do this then, even if one of the variables isn't ordinal? ... View more

Oh good. And I have actually been measuring distances by network distance, using Arcmap's network analyst extension. I see, so do you think it would be fine using spearman's rank over pearson's then? I would do chi squared but I don't feel very confident with it as I've never done it before. But I didn't think that Spearman's should be used for testing the association between 2 ratio variables (distance and percentage), as I was under the impression that at least one variable should be ordinal in order to use it? ... View more

Ok then, and yes perhaps I will change the percentages for each output area to population densities then. Thank you for your advice, yes as long as I show that I've acknowledged any uncertainties surrounding my study, I think this will be ok. And in terms of where my work fits into the bigger picture of geography, I suppose that measuring accessibility to services is important for comparing the spatial distribution of demand relative to supply, and looking at how access can vary spatially. As access to green spaces is generally considered to be associated with improvements in wellbeing, assessments into the adequacy and equality of access across cities seems important to investigate whether people have equal opportunities to yield these benefits to wellbeing, regardless of where they live. I can then use my assessments to identify areas which are potentially in greatest need of improvements to access. Hope that addresses the question. Throughout my study I've measured distances to different types of spaces and calculated the percentages of the population for which different accessibility standards set for green spaces have been met. However I feel that these measures are all quite simplistic, since my results only really consist of average distances and percentages. So this is why I was hoping to bring in some statistical analysis by testing to see how the distributions of social groups vary with access, and whether green space are therefore well located relative to demand. But would you suggest that this isn't really worthwhile since there clearly won't be strong relationships between the variables? But if I do decide to use Pearson's to measure correlations between the variables, would you mind explaining what would be involved with transforming my data as you previously mentioned to do? You mentioned to log the percentages, rather than using the original values? ... View more

Sorry I'm not sure that I understand your first point, I decided on the classifications myself by just dividing up the range covered by each of the variables, so that each separate category covered an equal range. I thought this would be the clearest way of showing how access and the distributions of each group vary across the city. Or would you suggest using natural breaks in the data to determine the classifications? And yes, dropping the decimals sounds sensible, and possibly using absolute numbers instead of percentages also seems to make sense. Because I suppose that one area may have a lower number of older people than another, but the percentage may be higher. Or perhaps mapping population density may be sensible? Then the area of each of the output areas can also be accounted for, as well as the absolute populations, so may be a better indicator of the distribution of each social group. And thank you for your other points, I'll take them all on board. I'm intending on writing about limitations and uncertainties such as these in my discussion as I understand that they're all valid points. And regarding your 6th point, I only mapped out spaces I understood were publically accessible. ... View more

Yes, I've actually already made some maps   Here are the ones I've created to assess variations in access for the older individuals of the population, to identify areas which priority should perhaps be given to in improving accessibility. The first shows the percentages of the population of each output area who are aged 65 and above, as well as the distribution of green spaces. And the second shows the average distances to the closest green space for each output area. It's a bit hard to compare them both though because of how many output areas there are and how small they are. But I suppose it can just about be seen that the average distance to the closest green space is fairly low for the only area with over 80% of the population over 65, being less than 400 metres. Then most of the areas in which 40-60% of the population are over 65 have slightly higher average distances of between 400-1200 metres. But most areas with poor access, over say 1800m, seem to have smaller older populations. So perhaps it could be said that, in this case, the distribution of the spaces seem to be reasonably fair in the sense that areas where access is poorer, above say 1200m, tend to have smaller populations of older people, if it's assumed that good access should be prioritised for older people. But I can use arcmap to analyse this a bit more carefully though by looking at the attribute tables for each variable. I should probably change the classification colours for the second map so that it's a bit easier to distinguish between the categories for the output areas as well. And perhaps it would be good to combine both maps into one, but I can imagine this would be quite difficult to interpret and not very clear. ... View more

Ok then, that makes sense. Thank you. No I'm not surprised that there doesn't seem to be clear relationships for any of the graphs. Would carrying out clustering analysis enable me to see whether there are areas with similar percentages of the social groups with similar access to the spaces? But perhaps carrying out a statistical analysis won't be worthwhile at all then since the graphs show fairly clearly that there isn't a relationship between any of the variables. Or perhaps I could do one just to clarify that there isn't a relationship. I may just discuss how the graphs show that there are variations in access for both output areas with higher percentages and lower percentages of individuals from each of the groups. And I can discuss how the areas with higher percentages shown to have slightly poorer access are perhaps in the greatest need of improvements to access. ... View more

Ok then, thank you My supervisor seemed to think that using either pearson's or spearman's would be ok, but wasn't sure which would be more appropriate. Can I just clarify that both my variables are ratio variables? If they are then I'm assuming that spearman's probably wouldn't be as appropriate to use as pearson's since spearman's is better used for ordinal variables? But I understand that variables have to vary linearly in order for pearson's to be carried out and I'm uncertain whether this is the case or not for mine? Here are the scatter diagrams I've plotted for them at the moment. I'm also measuring access for older individuals and those considered disabled, as well as for children. Or if I transformed the data so that it becomes normal, as you describe, would it solve the data not being linearly related being an issue? I'm not too sure how to go about doing this though. Thank you. ... View more

Thank you for your response. No I'm not intending to imply causality, I'd just like to investigate whether areas with higher percentages of children tend to have poorer or better access to green spaces, so yes, maybe chi squared would be better then. I'm completely new to this though because I haven't done a chi squared test before so I'm a bit uncertain about trying to do one since this is for my dissertation. When you suggest categorizing the variables into classes do you mean say dividing the percentages and distances into categories like for example 0-20%, 20-30% etc and 0-100 metres, 100-200 metres etc. Then would I cross tabulate these in a table to fill with counts of the number of output areas which fall within each category? Then use this to perform the chi squared analysis? ... View more

How do you go about doing a "Select by Location" to select all junctions within a certain distance of the park boundaries? Would this be done through the "Select layer by location" tool? If so then this doesn't seem to allow me enter the network junctions as the input feature layer, as it doesn't allow you to load data from geodatabases, only files. So as the network junctions feature class is within a geodatabase, and I can't seem to copy it into a file to select it from here instead, I'm not sure how to work round this ... View more

Do you know I go about removing this discussion? I can't seem to work it out ... View more

Hi, Thank you so much for you help. I've tried loading the locations after checking the "Exclude restricted portions of the network" button and it seems to be calculating routes for a lot more of my incidents now which is really good. Routes still aren't being created for 1073 of them though, an error message now showing up to list all those which no facilities have been found for e.g. "Warning: No facilities found for BS206GU in Incidents." So I'm still unsure why routes aren't being created for them all. I've checked that all my layers have the same projections as I've heard that this can prevent the analysis working properly, but they all do seem to be the same so this can't be the issue. But I don't think that the problem is that my incidents are on non traversable portions of the network anymore, as this message is no longer displayed in the error box. And creating a restriction attribute for the non-car-traversable edges sounds like a good idea so I'll try doing this. When going through the OS Mastermap Integrated Transport Network Data Preparation wizard to prepare my raw ITN data, I left the speed profiles as they were, so in the "drive" profile speeds were already set by default to 0 kmph (sorry I made a mistake before in saying mph) for paths networks such as the bridleways and footpaths. I then assumed that by setting the impedance to "drive" in the analysis settings of the closest facility analysis layer that these are the speeds which would be set each type of road. But if it's better to set networks which are non traversable as restrictions, as I'm also hoping to calculate walking speeds, should I then also set create restriction attributes for certain roads, since as these would be non traversable for pedestrians, I wouldn't want them to be included in this analysis. Thanks. ... View more