GIStechnical

32 PositionIT – October 2010

Spatial development planning at Rhodes University

by Ben Cobbing, Martin Bekker and Mark Hazel, CSS

GIS is the perfect tool for spatial modeling in a feature-dense area such as an academic campus. There are multiple relationships between features that have an impact upon how a campus operates, from walking distances between lecture theatres, green space management and facilities management for parking as examples. By knowing what is where, it is possible to understand the spatial relationships within a campus and plan accordingly.

In 2004, Rhodes University and specialised GIS company CSS (both based in Grahamstown,

South Africa) initiated a large scale, fine resolution spatial data capture project with the aim of developing effective GIS based data layers to assist the university with spatial development planning. To date over 4000 spatial features have been captured within the university campus area of around 307 hectares. In a developing environment like the campus area of Rhodes, to know where features are and the relationships between spatial features is an invaluable resource to have. Rhodes has data available for spatial development planning that possibly makes it the most progressive campus in terms of GIS planning in South Africa. Rhodes makes use of its spatial database continually to assist with planning.

Data capture

The availability of free georeferenced high resolution aerial photography, which became available for Grahamstown, in 2004, from the Chief Directorate: National Geo-spatial Information, NGI (previously the Chief Directorate: Surveys and Mapping, or CDSM) made this project possible. Base maps were printed at scales between 1: 500 and 1: 1000 and in-field annotation was completed of all spatial features within the university campus. Time and cost constraints limited the use of Global Positioning System (GPS) data capture, but differential GPS was used to supplement heads-up (desktop

based) onscreen data capture from the aerial photography, particularly in areas that were in shadow and could not be accurately located or new features not present on the aerial photography. All data was captured in the projected format: Transverse Mercator (Gauss Conform), WGS 84.

Spatial data was digitised in ArcView 3 and ArcView 8, using the aerial photography backdrop, and the data was checked for spatial integrity i.e. no slivers, overlaps or undershoots, to create a contiguous landcover data layer. Being a landcover project the bulk of the data captured is in polygon format, but certain datasets are in point or polyline format which

can be layered onto the polygon data layer. Sub-surface features, like water, electrical and fibre optic conduits, have also been captured and the depth of the feature noted. Two metre contours are available for Rhodes Campus area which allow for accurate 3D modeling.

In addition to the spatial extent of features being captured, various attributes about the feature were also recorded. Attribute data is critical for the successful use of a spatial dataset. It is vital for Rhodes to know the size, length surface, condition and name or official ID for a feature. By providing features captured with the official ID that

Fig. 1: Walking distance model on campus.

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PositionIT – October 2010 33

Feature Count Area (sqm)

Academic 9 1440,1

Alien vegetation 32 180 890,1

Bare ground 5 520,5

Bench 3 33,5

Boardwalk 1 39,5

Bridge 11 247,0

Building 376 132 381,2

Bush 27 114 110,7

Degraded land 12 81 326,1

Diesel tank 1 14,7

Driveway 34 4366,9

Dump 1 5,8

Electrical facility 8 207,0

Entrance 1 67,6

Field 6 69 260,1

Footpath 6 4336,2

Garden 596 97 787,7

Grassland 40 881 441,8

Grassy Fynbos 5 6454,9

Grave 15 44,4

Grid 18 50,0

Gutter 236 5341,2

Hedge 52 2334,9

Indigenous forest 4 27 764,8

Lawn 470 337 199,4

Maintenance service 1 75,0

Manholes 8 4,8

Mine/quarries 3 56 174,0

Organic tip 10 5733,0

Park 82 384 798,3

Parking 137 46 303,1

Path 539 46 966,0

Patio 35 1702,1

Pavement 23 5974,2

Paving 174 10 717,7

Place of interest 1 0,1

Quad 71 8848,4

Ramp 1 7,8

Research sites 2 2733,3

Reservoir 1 1633,5

Residential 9 911,2

Road 131 96 053, 6

Scrap storage 1 1060,5

Shrubland 39 355 031,0

Sports facility 21 23 291,6

Stairs 618 4386,1

Stands 10 1696,1

Study site 6 1468,6

Treatment tanks 1 478,1

Wall 88 2563,7

Water course 9 16 076,7

Water feature 23 12 059,9

Water tank 4 21,7

Waterbody 4 12 993,8

Weir 4 174,7

Wetland 14 21 533,7

Total 4039 3 069 138

Rhodes uses, one is able to link all non-spatial information, such as financial records to a spatial building feature.

Table 1, presents the count and list of surface spatial data features captured to date (June 2010).

The spatial data features in Table 1, are consistently being updated. New features are built and old features are sometime assimilated into new features. This requires that the GIS data remains up-to-date since certain feature types listed in Table 1 continually change. Natural vegetation feature types, where a class like "Alien vegetation", would change to "park", for example since the university is actively removing alien vegetation.

Current use of the spatial data

Rhodes University makes ongoing use of the GIS data available for spatial planning. Examples include site location for new buildings from 3D slope and aspect maps, parking space design and alignment, services facilities maps and grounds and gardens maps. They say a map speaks a thousand words, and spatial planners at Rhodes use the data to present maps as part of their budget requirements.

Spatial modeling

Spatial modeling allows planners to consider multiple spatial datasets against the needs of planners at the university. As an example consider walking distances between lecture theatres, in terms of campus development. There is a limited amount of time between lectures, and students may need to get from one area of the campus to another within the gap between lectures. Thus one needs to know where lecture theatres are and the permissible routes which students take in order to know whether there is enough time to get from one lecture to another. Fig. 1 demonstrates the result of a simple time and route mapping project, where walking time is mapped per route, or path, taken from three central points on campus.

Fig. 1 illustrates that it is possible to walk from one end of the campus to another within the timeframe between lectures. Table 1: Spatial feature types and count.

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34 PositionIT – October 2010

Another example of utilising spatial data for planning can be seen in Fig. 2. In this example, point feature light sources (street lamp, small lamp post, floor light etc.) have been buffered by a reasonable area to which an object can be reliably identified i.e. what can be seen at night. The buffer areas are overlain onto the campus area to show areas that are lit and areas that are dark. The lit areas are

Fig. 2: Light and dark areas of the campus.

then intersected with all pathways and walking routes show pathways that are dark or have insufficient lighting for security purposes. Fig. 2 shows that the campus pathways are sufficiently lit, and although some areas may appear as unlit, these are generally sports field areas which are all floodlit at night during sports events. This sort of data provides the Campus Protection Unit with a

Fig. 3: 3D route profile.

guide as to where it may be best to optimise campus protection routes and discourage crime.

By adding a vertical (3D, or Z-field dimension) element to spatial development planning, consideration can be given to elevation and slope. It is easy with accurate contours to show areas that can be excluded from development planning where slope may be too steep, or a route profile for example. Fig. 3 shows an example of a route profile generated from a 2 m contour dataset.

The route profile provides spatial planners the tool to optimise pedestrian or cycling routes to and from the residential to various stops within the campus along the route, particularly since at Rhodes over 200 m of elevation gain can occur from the bottom end of the campus area to the top.

Facilities management

Another important use of spatial data is for facility management. Previously when the campus area was originally designed feature location records were not always kept. This is particularly true for sub-surface features like water, electrical conduits and recently fibre optic cabling. In an effort to address this, sub-surface marker balls have been placed on key sub-surface installations, particularly in areas where broad excavation cannot take place, such as in the middle of a sports field. Sub-surface marker balls contain a free floating leveling device in a hard case watertight polyethylene shell, which can be read by a locator at a later stage. In a recent anecdotal example the contract manager saw the back actor (backhoe) excavating a portion of fibre optic cable and was able to stop it just in time, saving the cable from being torn in half. In these cases it is important to know exactly where to dig, and by placing marker balls along the line, it is simple to locate the sub-surface feature.

Fig. 4 outlines the process where marker balls are placed on key installations when being built. The position of the features are captured by precision GPS and stored in the GIS database. The marking and mapping of sub-surface features usually happens realtime as the

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Fig. 5: Power use per building.

Fig. 4: Sub-surface facility management.

building development happens, since once an area is laid by concrete or lawn, it is too late to place a marker. For future building development the sub-surface feature can be found in-field by a locator and marked so that any trenching does not impact upon the feature. This is crucial in a high feature density campus environment, where sub-surface features like fibre optic cables run beneath roads, parking lots and sports fields.

In an another example of the use of spatial data for facility management, Rhodes has begun capturing power use per electrical item per building to demonstrate total power use for the campus. All buildings have unique ID codes for both non-spatial estate planning and spatial GIS data. Using the building codes, a link between electrical items in a building can be linked to the spatial data.

The aim of the data presented in Fig. 5 is to spatially represent power use at the campus; a data type which is often difficult to map accurately. A challenge for this type of map is make sure that the networks that provide the various servers, computers and other network items are assigned to the correct building, since the network source may not be in the same building as the feature item.

Conclusion

In this short article we have tried to demonstrate how an accurate spatial landcover and features dataset can assist with spatial development planning and facilities management at a high feature density area like a university campus. There will always be GIS-type challenges that present themselves in terms of data maintenance and spatial accuracy, but on the whole we feel that the data that Rhodes currently has available for planning is both accurate and up to date. The spatial data project at Rhodes University is a continuous project, and as the landscape changes, the spatial data will need to be updated, while still providing an extremely useful tool for spatial development planning.

Contact Ben Cobbing, CSS, Tel 046 622-4526, bcobbing@cssgis.co.za