0-NEW POST IDEAS

Quotations

“If I have seen further it is by standing on the shoulders of Giants” – Isaac Newton

“Do what you can, with what you have, where you are” – Theodore Roosevelt 

“Do not only go where the path may lead, go instead where there is no path and leave a trail.” – Ralph Waldo Emerson

“The expert at anything was once a beginner.” – Helen Hayes 

“The expert at anything was once a beginner.” – Helen Hayes

“I have never met a man so ignorant that I couldn’t learn something from him.” – Galileo Galilei 

“It’s mapping Jim, but not as we know it…” – Dr. Leonard “Bones” McCoy 

 

 

GI Data Portals

GI Data Portal Insight 1

We Can Build-In Functionality For Fundamental Spatial Analysis “On-The-Fly”

As you can see from the “School Map & Charts” page of the Distance & Mode  Analysis Module on the STHC Demo Data Portal below, if set up properly “at the back-end”, it is possible for users to do some further querying of the sptial analysis results data shown on the map “on-the-fly” within the browser itself. Just tick a few check-boxes to set the analysis criteria, click the button, and the map and charts update with the results. To quote the meercat – “Simples”!

BUTTON: Open STHC Data Portal In A Separate Browser Window 

http://sthc.co.uk/portals/demo/Accident_School_Map_Pupil.html

GI Data Portal Insight 2

It’s Not Just About Maps

As you can see from the School Bar Charts, Stats & Report page of the Distance & Mode Analysis Module on the STHC Demo Data Portal below, the key results of spatial analysis do not necessarily need to be presented on a map. Tables and charts play their part, as do “headline stats”, presented in a modern, inforgraphic style.

BUTTON: Open STHC Data Portal In A Separate Browser Window 

http://sthc.co.uk/portals/demo/Proximity_School_Map_Pupil.html

GI Data Portal Insight 3

Once Your Data Is ‘In’ The Portal, It Can Be ‘Mashed-Up’ With The Other Data There – Also Your Own Or From Public Domain – For Extra Insight (Often For Little Extra Effort)

Just by adding other GI datasets into the mix, it is possible to get further spatial insight without having to do full blown spatial analysis using GIS. On the “School Map & Charts” page of the Accidents Analysis Module on the STHC Demo Data Portal below, we have added the ability to display where the pupils are travelling from as an anonymised “hot spot” style layer. Thus users can use “Eyeball 1.0” to assess the significance of the accident clusters with regard to pupil travel to the given school.

BUTTON: Open STHC Data Portal In A Separate Browser Window

http://sthc.co.uk/portals/demo/Accident_School_Map_Pupil.html

GI Data Portal Insight 4

If It Makes Logical Sense And You ‘Show Your Working’, Quite Complex Questions Can Be Answered By The User

To build further insight though spatial analysis, we keep following the same train of thought to its logical conclusion. This process is perfectly illustrated on the “School Map & Charts” page of the Proximity & Pupil Choice Analysis Module on the STHC Demo Data Portal below.

“Local schools for local pupils” is a philosophy most of us can subscribe to. Nobody benefits from pupils being driven from a school on their doorstep to one on the other side of town (no doubt passing several others that are equally eligible). As well as environmental & traffic consequences of this, there are consequences for the local school and the community. Less pupils mean less funding, means less facilities, means less attrictiveness, means less pupils, and so it goes on. What if we could identify schools where this is a real issue, so stakeholders can begin to offer extra support to stem the leaks?

For the Proximity & Pupil Choice analysis we flip the the Distance analysis on it’s head. Thus rather than looking at where pupils are travelling from to get to a given school, we start with all the pupils that live within a reasonable walking distance of it (the ‘walk threshold’), and look at which school they are actually travelling to. A surprising number areThus taking their business elsewhere, often driving to get there (often passing several more schools on the way). Thus stakeholder can visualise and quantify the eligible pupils that are “leaking away” from a schools’ “doorstep”.

A note of caution though. With each “layer” of analysis, comes added nuance and complexity, and more criteria that need to be taken into account in order to still get to a meaningful result. Any analysis will give a result, but is it meaningful, is it actually telling you anything useful?

BUTTON: Open STHC Data Portal In A Separate Browser Window 

http://sthc.co.uk/portals/demo/Proximity_School_Map_Pupil.html

GI Data Sourcing

GI Data Sourcing Insight 1

Feature: UK MAGIC Environmental Data Portal Knowledge Map

The MAGIC website is an online data catalogue providing geographic information datasets about the rural, urban, coastal and marine natural environment of Great Britain from across government. This knowledge map was prepared by us as part of a larger consultancy project around the Transforming The Trent Valley (TTTV) project,  which is about using exisiting GI data to undertake landscape assessments and wildlife audits in support of a bid to the Heritage Lottery Fund. Knowing what data exists in the public domain, and just as crucially, the processes involved in getting hold of it, are essential initial steps in all projects involving GI. Creating data catalogue maps like this and sharing it with all stakeholders makes the process a lot  (and we mean A LOT!) easier.

BUTTON: Open Knowledge Map In A Separate Browser Window

MAGIC_Datasets_Oct17(4)_ORG.html

GI Data Sourcing Insight 2

Transforming The Trent Valley (TTTV) Data Sourcing Project

The MAGIC website is an online data catalogue providing geographic information datasets about the rural, urban, coastal and marine natural environment of Great Britain from across government. This knowledge map was prepared by us as part of a larger consultancy project around the Transforming The Trent Valley (TTTV) project,  which is about using exisiting GI data to undertake landscape assessments and wildlife audits in support of a bid to the Heritage Lottery Fund. Knowing what data exists in the public domain, and just as crucially, the processes involved in getting hold of it, are essential initial steps in all projects involving GI. Creating data catalogue maps like this and sharing it with all stakeholders makes the process a lot  (and we mean A LOT!) easier.

BUTTON: Open Knowledge Map In A Separate Browser Window

MAGIC_Datasets_Oct17(4)_ORG.html

GI Knowledge Map

GI Knowledge Map Example 1

ISO 3166 Knowledge Atlas Maps

It’s always been part of our mission to explore the synergy between using geographic maps and knowledge tree maps to describe and document the ‘real world’. This map shows how far we have come with that mission…

BUTTON: Open Embedded Map In A Separate Browser Window

https://www.knowledgemappers.com/HTMLMaps/Countries_ISO3166-1_Atlas_Basic_GEO-ORG_Dev04b.html

GI Knowledge Map Example 2

GEO Network

To build further insight though spatial analysis, we keep following the same train of thought to its logical conclusion. This process is perfectly illustrated on the “School Map & Charts” page of the Proximity & Pupil Choice Analysis Module on the STHC Demo Data Portal below.

“Local schools for local pupils” is a philosophy most of us can subscribe to. Nobody benefits from pupils being driven from a school on their doorstep to one on the other side of town (no doubt passing several others that are equally eligible). As well as environmental & traffic consequences of this, there are consequences for the local school and the community. Less pupils mean less funding, means less facilities, means less attrictiveness, means less pupils, and so it goes on. What if we could identify schools where this is a real issue, so stakeholders can begin to offer extra support to stem the leaks?

For the Proximity & Pupil Choice analysis we flip the the Distance analysis on it’s head. Thus rather than looking at where pupils are travelling from to get to a given school, we start with all the pupils that live within a reasonable walking distance of it (the ‘walk threshold’), and look at which school they are actually travelling to. A surprising number areThus taking their business elsewhere, often driving to get there (often passing several more schools on the way). Thus stakeholder can visualise and quantify the eligible pupils that are “leaking away” from a schools’ “doorstep”.

A note of caution though. With each “layer” of analysis, comes added nuance and complexity, and more criteria that need to be taken into account in order to still get to a meaningful result. Any analysis will give a result, but is it meaningful, is it actually telling you anything useful?

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GI Knowledge Map Example 3

Scottish Members of Parliament

Just by adding other GI datasets into the mix, it is possible to get further spatial insight without having to do full blown spatial analysis using GIS. On the “School Map & Charts” page of the Accidents Analysis Module on the STHC Demo Data Portal below, we have added the ability to display where the pupils are travelling from as an anonymised “hot spot” style layer. Thus users can use “Eyeball 1.0” to assess the significance of the accident clusters with regard to pupil travel to the given school.

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Large Format Printing

Large Format Printing Example 1

Large Batch ‘One Off’ Site Centred Maps

We encourage client authorities to put the STHC analysis results back into all their schools (whether or not they collect pupil usual mode of travel data or have a formal school travel plan). After all, this is where the source data comes from in the first place, and this is where we are trying to effect change on the ground (even if they themselves havene’t yet expressed an interest in changing!).

Even in this technological age we have found that this is best done as a mix of paper and digital resources. Thus school-centred maps on big bits of paper, delivered back to the school as part of a STHC School Pack, have always been a key part  of the STHC analysis output.

With 2 maps and a spreadsheet per school and up to 550 schools in an authority (the biggest one we have handled to date), that’s a lot of maps to print off and compile into School Packs in a short space of time, so we have had to make the process as automated as possible!

The primary purpose of Pupil Travel Maps is to clearly show where all the pupils that attend the school are travelling from and how. The Standard STHC Pack has 2 maps with different backgrounds – aerial imagery or the appropriate street-level scale of Ordnance Survey topographic map.

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Large Format Printing Example 2

‘Continuous Length’ Printing 

No text.

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Large Format Printing Example 3

Andrew Crummy, Community Tapestry Template Panels 

AS per featured client elsewhere

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Spatial Analysis Insight

Spatial Analysis Insight Example 1

Establish The Basic Outcomes & Deliver The “Quick Wins” 

This example from our School Travel Health Check service shows how knowledge that can only be derived through spatial analysis, brings practical insight to all stakeholders that are interested in how pupils travel to school, from where and by what means. It enables them to better target their limited, behaviour-changing resources to schools where modeshift is more likely to be achieved ie. those where the most pupils that live within a reasonable walking distance of the school still travel by car.

A classic “quick win” for clients upon first receiving the STHC analysis data is to re-order the individual school results by “the number of pupils living within walk threshold travelling by car”. This will give them a target list in descending order of the schools with the biggest potential for modeshift, which can then form the basis of their day-to-day activities for the next few weeks.

Care must be taken here however. Although it is maybe more intuitive to look at the percentages to rank potential target schools, the measure of success for modal shift is the actual number of journeys where the mode of travel can be changed. Think missionary work and the “saving of souls”!

As you can see from the example screenshots opposite from an actual STHC client authority, if we only went by percentages there are only 80 potential modal shift targets spread over the top 10 “offending” schools, compared to 477 if we play the numbers game. A 10% modal shift in these schools would actually result in a lot more CO2 saved!

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Spatial Analysis Insight Example 2

Users Don’t Need GIS To Do Basic Spatial Analysis “On-The-Fly” 

As you can see from the “School Map & Charts” page of the Distance & Mode  Analysis Module on the STHC Demo Data Portal below, if set up properly “at the back-end”, it is possible for users to do some further querying of the sptial analysis results data shown on the map “on-the-fly” within the browser itself. Just tick a few check-boxes to set the analysis criteria, click the button, and the map and charts update with the results. To quote the meercat – “Simples”!

BUTTON: 

Spatial Analysis Insight Example 3

A Lot More Spatial Insight Can Be Gained By “Mashing-Up” With Other Data When Appropriate (Often For Little Extra Effort)  

Just by adding other GI datasets into the mix, it is possible to get further spatial insight without having to do full blown spatial analysis using GIS. On the “School Map & Charts” page of the Accidents Analysis Module on the STHC Demo Data Portal below, we have added the ability to display where the pupils are travelling from as an anonymised “hot spot” style layer. Thus users can use “Eyeball 1.0” to assess the significance of the accident clusters with regard to pupil travel to the given school.

BUTTON: 

Spatial Analysis Insight Example 4

Keep Following The Logical Train Of Thought To Build Insight (With Maybe The Occasional Flip 😉  

To build further insight though spatial analysis, we keep following the same train of thought to its logical conclusion. This process is perfectly illustrated on the “School Map & Charts” page of the Proximity & Pupil Choice Analysis Module on the STHC Demo Data Portal below.

“Local schools for local pupils” is a philosophy most of us can subscribe to. Nobody benefits from pupils being driven from a school on their doorstep to one on the other side of town (no doubt passing several others that are equally eligible). As well as environmental & traffic consequences of this, there are consequences for the local school and the community. Less pupils mean less funding, means less facilities, means less attrictiveness, means less pupils, and so it goes on. What if we could identify schools where this is a real issue, so stakeholders can begin to offer extra support to stem the leaks?

For the Proximity & Pupil Choice analysis we flip the the Distance analysis on it’s head. Thus rather than looking at where pupils are travelling from to get to a given school, we start with all the pupils that live within a reasonable walking distance of it (the ‘walk threshold’), and look at which school they are actually travelling to. A surprising number areThus taking their business elsewhere, often driving to get there (often passing several more schools on the way). Thus stakeholder can visualise and quantify the eligible pupils that are “leaking away” from a schools’ “doorstep”.

A note of caution though. With each “layer” of analysis, comes added nuance and complexity, and more criteria that need to be taken into account in order to still get to a meaningful result. Any analysis will give a result, but is it meaningful, is it actually telling you anything useful?

BUTTON: 

Spatial Data Infrastructure Capacity Building

Spatial Data Infrastructure Capacity Building​ Example 1

Everything In The One Document

Knowledge mapping is perfect for bringing together all the many strands that make up an SDI in the one place.

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Spatial Data Infrastructure Capacity Building​ Example 2

Association for Geographic Information (AGI) Sourcebook

One of our earliest knowledge mapping commissions was the creation of the UK Association for Geographic Information Source Book. The AGI, whose mission is to maximise the use of Geographic Information (GI) for the benefit of the citizen, good governance, and commerce, was one of the first national associations established to support the geospatial industry. One of it’s earliest activities was the publication of the AGI Sourcebook, a directory of resources for the geospatial industry. When we were commissioned as editor for the 2001 edition we chose to use a linked knowledge map structure. We used MindManager 2002.

Embedded example of home page map? Whole Bloody thing?

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