cOn ten ts/
0.0 1.0 2.0 3.0 4.0
OUR GROUP ELEVATION MAPPING BASIC MAPPING MOISTURE MAPPING VEGETATION MAPPING
5.0 6.0 7.0 8.0
CA STATES AGENT MAPPING ON SITE TESTING
LEARNING OUTCOMES
GROUP MEMBERS
0.0 OUR GROUP
CAROL (JINGSI SUN) During the project, my contribution to the group was mainly getting all the basic maps (elevation, river, paths, forest, human activity) nailed down and communicating with the other groups to ensure they get what they need from our group and getting their outputs. I also participated in mapping out the hydrology map together with Siyu and figuring out some of the representation methods to visualise our data. For the elevation map, I created the mesh from the contours bigger than our boundary so we could have space for alteration as the elevation map is the most basic map and later I was glad I did this. The final mesh for the topography was quite accurate and because the topography of the surrounding area was really extinctive it was really easy for us to recognise the approximate real world locations referenced on the mesh. During the early period, Alex and I went through a few discuss on our site boundary and altered a few. The grid size to divide the site was also altered according to the new site boundary. Also, because of communication problems, I understood the grid size in a wrong way which made me redid some work. But gladly the mesh was big enough for the alterations and the grid was finally nailed dow to 150*150 hexagonal grids in size 2. All of the later maps and CSVs extracts the value of every point of the hexagonal grids. Later I mapped out the basic element boundaries of the site which could help us further define the site and could be used by other groups. To map out the CSV used by the dynamics and the direction group, the value points within the elements were remapped to 1 and the value outside were set to 0.1 so that the other groups could separate the elements. When feeding the CSV to other groups, communication was crucial to make sure we produce what they want. To produce the right intensity map, I went through many modifications. Especially for the elevation which always didn’t seem right because of lack of understanding I also met up with the direction group and produced the CSV through face to face communication. During the time when the other groups seem to be satisfied with our CSVs, I helped my group with some technical problems with the hydrology map, including populating the statistics that we have around the testing points and technical ways to deal with data. During later period I also participate in visualising data including rising the points according to its value to visualise data in a physical way, and take charge of the CA mappings.
In the part C project, the works that I have done so far including processing of the near infrared vegetation health photos that were taken with balloons with red filters in infragram.org. I used these photos in mapknitter.org and made a collage based on the google map positions. Then I did the visualization of the vegetation health map. Another job of me is to test out the agent movement in unity by making the agents reading our CSV maps and record the changes they made and made the agent paths map in grasshopper. In the process of completing for part C, probably because I was more into unity back in part B, therefore in part C, I am in charge of most of the unity works. I found that agents have interesting behaviors reacting to the CSV maps. Even if the map looks perfectly right in grasshopper, in unity, the agents tend to react differently to the CSV maps. More interestingly, when I change the values relating to the agents, e.g. sight width/length, map value deductions, etc., the agent paths changed dramatically. With some values, there will even result in nulls. In the game the unity teams are dealing with, there are so many possibilities when we change the algorithmic inputs of the settings and data. These agents data are a perfect way to see the links between our CSV map data with the game.
Gm
CHARLENE (LI XIA)
Meng Siyu
mae In our group, I mainly focused on moisture mapping at the start. I have manually inputting the data we collected align with its actual location on map, so that to have a relatively accurate measurement projected on the mapping grid. With this initial information, I have tried to break away from the circles by testing a few plugins and through several trials and errors, seeking ways to visualize the data. Furthermore, in order to reasonably evaluating a larger area, I took a point with actual measurement and locate it on a contour, so that we have evenly spread-out values along a contour. At the later stage, I was mainly in charge of visualizing all of our maps for presentation, unifying the graphics but also showing distinct aspects according to the information conveying. Following the presentation, we went to the site again for human activity information collection. I have transferred the “body algorithm� testing result to intensity maps and generate csv for further agents testing. Through the whole process, the other groups were mainly relying on our basic maps and csv output information. However, I have found that clear graphics could greatly enhance our understanding of the site, as well as for others’ comprehension. I enjoyed the exploring process.
Dights falls
1.0 elevtion
D f
1 e t
Mesh elevation bOundary fInal site boundary
The selected site is an approximate 448*520m rectangle around Dight Falls. Its Information is breaked down to different sized hexagonal grids according to different needs from different groups, converted to numbers and further passed on to different groups and utilised.
SITE BREAKDOWN
FABRICATION GRID
50*50 SIZE 6 BREAKS THE SITE’S STATISTICS DOWN AND UTILISED BY THE
UNITY GRID 150*150 SIZE 2 BREAKS THE SITE’S STATISTICS DOWN AND UTILISED BY THE UNITY GROUP
SITE CONTOURS PRODUCING THE ELEVATION AND FORMS THE BASIS OF THE ANALYSIS OF THE DIFFERENT COMPONENTS ON SITE
SITE MAP BASIC ELEMENTS (PATHS, RIVER, ROADS, FORESTS) MAPPED OUT ACCORDING TO THE SITE MAP.
ELEVATION curves not intersecting boundary projecting the contours to the xy plane to find which one intersects with the boundary (on the xy plane as well)
find collisions between contours and boundary
find collisions between contours and boundary
alll curve cut within boundary
divide the selected curves and join up division points with delaunay mesh to form the mesh
delaunay algorithm: uses triangualar method to join up the closest points to form the mesh.
eleVation Outcome
MESSY EDGES
VERTICAL SLOPES
The first try on building the elevation turned out to be a failure. Literally nothing was mapped out successfully on the mesh. Also, the shapes were edgy and had vertical slopes (which shouldn't happen). However, the elevation was very important and had to be accurate for defining other components on site.
Finally, we found out that the problem happened on projecting the curves on the xy plane. When dividing the curves, some of the curves projected on the xy plane were selected instead of the actual contour.
points created by dividing curve on the xy plane
SMooTHING MESHES
ORIGINAL MESH
SMOOTH 4 TIMES
SMOOTH 12 TIMES (NEARLY NOTHING WAS LEFT)
To make the mesh smoother, the first thing that we tried out was directly smoothing it in rhino. However, the result finally turned out to be the mesh became more and more flat and the topography still can not be defined. This was when we decided that we had some problem with our grasshopper definition.
ALTERATION
previous script
extrude the boundary and define if the contours are in the brep so that we won’t need to project the contours.
include the points on boundary
final mesh
points within brep
Final elevatiOn mesh
exaggerated eleVatiOn
projecting the grid points to the mesh
measuring the distance between the grid point and projected points and remap them to a greater value with greater difference forms a more exaggerated landscape
new way of presentation: instead of creating a mesh, we decided to use hexagons creating a kind of ‘point clouds’ to show the topography. Hexagons’ radious changes according to its height: the higher the bigger. the curves could further be offseted to enhance the effect.
move the grid points upwards according to the new length. creating a line between for visualisation and later guides.
V
flat built area (impermeable)
Steep valley along riverbed
exaggerated eleVatiOn After creating the mesh successfully, we began to try out different ways of representation. This picture is built by lines created by the projection points on the mesh elevation. However we found that it was very hard to recognise the site and its characteristics through this way. Later tests were all unsuccessful. Finally, we found out that this was because the topography did not differ much. Thus, we decided to exaggerate the form to make the representation look cool.
stream
d
highest point on site (across the river)
river at the lowest point
METAPHORIC ELEVATION
FINAL Noise filter is added to the terrain to make the height of each point alter randomly in a range, which looks more complicated in unity.
ALTER 3 Extracting the roads in the built environment.
ALTER 2 Extracting the highway, making it way more higher than the surrounding environment, forming a physical ‘barrier’ in the game to let players notice the impermeability around and the degradation it causes.
ALTER 1 Sinking the river area and making sure the river is lower than any part of the site so that later a river could be created in the game, giving people a better sense of the site in the virtual world.
nega posit exag
Initially we measured the distance between the two points however de-constructing the points and extracting its z value made it easier for the calculation.
ative value for the river to sink and tive numbers for pathways and roads to ggerate the human activity area.
Adding the noise filter to make the topography in unity have more meshes and make the surface more noisy and complicated.
common sense utilisation
2.0 Basic mapping
c s u
Throughout our mapping, we have been asking ourselves a lot: Why are we mapping out these things? Why are they important? The game we are creating shouldn't just come out of imagination. In fact, all games should have its logic: how the players should win, what they will experience, how they should make decisions, how they should think through playing the game. Thus all the settings in the game should have its meaning and work for the whole system. The game system should support itself with logic so that the player could know what they are looking at and what decisions they should make. As the mapping team, we are the ones to produce this logic, and the best way to set up logic is to take the real world into the game settings and use the player's common sense and utilise the logic that are already established in the player's minds.
Reflection
B m p
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-217.5, -158.482649, 7.930603 -217.5, -155.018547, 8.479755 -217.5, -151.554446, 9.113309 -217.5, -148.090344, 9.624797 -217.5, -144.626242, 10.120383 -217.5, -141.162141, 10.885942 -217.5, -137.698039, 11.712228 -217.5, -134.233938, 13.645944 -217.5, -130.769836, 15.103326 -217.5, -127.305734, 16.721767 -217.5, -123.841633, 18.189715 -217.5, -120.377531, 19.140469 -217.5, -116.91343, 19.500076 -217.5, -113.449328, 19.669712 -217.5, -109.985226, 19.800675 -217.5, -106.521125, 20.037144 -217.5, -103.057023, 20.6475 -217.5, -99.592921, 21.242954 -217.5, -96.12882, 21.510892 -217.5, -92.664718, 22.224922 -217.5, -89.200617, 23.053295 -217.5, -85.736515, 23.821139 -217.5, -82.272413, 24.789121 -217.5, -78.808312, 26.057028 -217.5, -75.34421, 27.646113 -217.5, -71.880109, 29.569537 -217.5, -68.416007, 31.694251 -217.5, -64.951905, 53.339658 -217.5, -61.487804, 53.972081 -217.5, -58.023702, 54.341708 -217.5, -54.5596, 54.391692 -217.5, -51.095499, 54.170121 -217.5, -47.631397, 53.846224 -217.5, -44.167296, 42.806153 -217.5, -40.703194, 44.046239 -217.5, -37.239092, 45.172815 -217.5, -33.774991, 45.13379 -217.5, -30.310889, 45.333707 -217.5, -26.846788, 53.641888 -217.5, -23.382686, 53.97182 -217.5, -19.918584, 54.31625 -217.5, -16.454483, 54.650641 -217.5, -12.990381, 54.921794 -217.5, -9.526279, 55.040069 -217.5, -6.062178, 55.024251 -217.5, -2.598076, 45.990969 -217.5, 0.866025, 45.507853 -217.5, 4.330127, 44.914448 -217.5, 7.794229, 44.320866 -217.5, 11.25833, 54.030684 -217.5, 14.722432, 54.119494 -217.5, 18.186533, 54.21123 -217.5, 21.650635, 44.234068 -217.5, 25.114737, 44.135011 -217.5, 28.578838, 43.909375
-217.5, 32.04294, 43.578278 -217.5, 35.507042, 43.194005 -217.5, 38.971143, 42.907348 -217.5, 42.435245, 42.855484 -217.5, 45.899346, 43.327796 -217.5, 49.363448, 43.904218 -217.5, 52.82755, 44.350235 -217.5, 56.291651, 44.425437 -217.5, 59.755753, 44.19556 -217.5, 63.219854, 43.964423 -217.5, 66.683956, 44.097707 -217.5, 70.148058, 44.501325 -217.5, 73.612159, 45.007075 -217.5, 77.076261, 45.463844 -217.5, 80.540363, 45.762935 -217.5, 84.004464, 54.423252 -217.5, 87.468566, 54.149446 -217.5, 90.932667, 53.583806 -217.5, 94.396769, 44.684613 -217.5, 97.860871, 44.234513 -217.5, 101.324972, 43.979603 -217.5, 104.789074, 44.192084 -217.5, 108.253175, 44.591046 -217.5, 111.717277, 44.867868 -217.5, 115.181379, 44.89054 -217.5, 118.64548, 44.804439 -217.5, 122.109582, 44.803017 -217.5, 125.573684, 44.972132 -217.5, 129.037785, 45.155593 -217.5, 132.501887, 45.418491 -217.5, 135.965988, 45.703085 -217.5, 139.43009, 45.882165 -217.5, 142.894192, 45.905336 -217.5, 146.358293, 45.780038 -217.5, 149.822395, 45.583695 -217.5, 153.286496, 45.386466 -217.5, 156.750598, 45.213059 -217.5, 160.2147, 45.153153 -217.5, 163.678801, 45.30411 -217.5, 167.142903, 45.588365 -217.5, 170.607005, 45.889689 -217.5, 174.071106, 46.138821 -217.5, 177.535208, 46.392904 -217.5, 180.999309, 46.726499 -217.5, 184.463411, 47.104817 -217.5, 187.927513, 47.390343 -217.5, 191.391614, 47.46841 -217.5, 194.855716, 47.236247 -217.5, 198.319817, 46.832083 -217.5, 201.783919, 46.565051 -217.5, 205.248021, 46.717596
EXTRACTING SITE ELEMENTS
PATHS Indication of small amount of human activity and low permeability.
FORESTS Indication of good vegetation behaviour and lower human activity. However, forests could be damaged by human activity and cause pollution.
IMPERMEABLE SURFACES Indication of high level human activity. Maximum water runoff, minimum soil hydrology. Maximum land degradation. low plant health and vegetation coverage.
RIVER Indication of water source that should help vegetation growth. However due to pollution trees around river bed also has health problems
BASIC LANDSCAPE
COMBINED MAP
basics computation During the computation of the site components, we came across a lot of problems. When we import data to the csv generating file, the intensity map looked right in rhino, but it became nulls in unity.. Thus we explored many different ways and tried many times until we got it right into unity.
problem 1: The origin point of the grid wasn’t identified in the previous versions. Thus, the grid was twisted and didn’t follow the right sequence, which created nulls in unity.
problem 2: make sure the circle here have a value instead of 0. Zeros are likely to create nulls when mapping.
Initially we generated the circles for visualisation however as they are all numbers as we considered, outputting numbers from the circle should also work. However, we omitted that circles contain the value pi, which couldn’t be recognized by unity and generates nulls.
problem 4: this was also the biggest problem that we came across and very hard to find. The input data of the CSV should be generated by the numbers directly outputted by the pick and chose component instead of the output numbers of the circles. problem 3: the input 0 and 1 of the pick’n’chose component needs to be at the same length. Thus the length of the number list is repeated to 3 instead of directly plugging 3 into the input.
sOIL moisture
3.0 MOISTURE MAP
300m
ON SITE RESEARCH
300
0m
Instead of the hole site, hydrology was tested based on a 300*300m area. The whole site was too big and manual testing will be too time consuming. The area was divided into 15*15 square grids with grid sizes of 20m. We used the square grid as a base to define our testing points and keep our tests in record.
Testing points (24 in total)
R S P
RESEARCH PROCESS
Instead of researching data from the internet, we tested the hydrology directly on site. This gave us a more direct sense of what we are dealing with and what we are expecting to get. It also made us more familiar to the site. The hydrology tester is a copper made tester connected to the laptop. The tester is plugged into the ground and tests out the hydrology by measuring the voltage between the two copper wings. This is then expressed by numbers between 0 and 1024. When the soil is dry, the resistance is bigger thus the voltage is bigger. Thus, higher numbers indicates drier soil. However we did not figure this out in the beginning so we tested the moisture in the air and in the water. According to our test, the value produced measuring the air is 1024 and the one measured in water is around 200. Thus it could be seen that the wetter the area is, the smaller the number should be.
MOISTURE DATA
TESTING RESULTS
Moisture sensor
Things to get rid of
MAPPING RESULTS Culling the first row indicating the numbered rows Get rid of nulls
The first time that we experimented, the empty data branch was not removed, so we flipped the data and dispatched the nulls. However the second time we opened the file, the nulls were removed through the clean tree component.
The measurer measures each place 10 times automatically so the list should finally have branches of 10 numbers
Numbers over 800 were some of the measurements that we measured in the air, so we got rid of them
Evaluating weather the moisture is above or below average: because as the soil is more hydrological, the number is smaller, we used the average to subtract each hydrological value
Use list item to identify the branch needed and match the data with the points in sequence as what we recorded
Point charges
EVALUATING HYDROLOGY
To evaluate hydrology of the hole site, we went through many different methods. After putting the 10 points into grasshopper, we created a point charge according to the value at each point. According to the colours, the blue parts has higher hydrology value and the pink parts had the least. Theoretically speaking, in this way each point of the site will have its own hydo-value and all we need to do is to extract value from the basic hexagonal grid point. However, because we didn't have enough points to test with, the hydrology faded when it came to the places that we didn't manage to test, which made the values illogical.
ITERATION 1: RANDoM POPULATION BASED oN STATISTICS
Outcome: Did give an improvement but still lack of logic.
ITERATION 2: MESHING OUT HYDROLOGY
Times of loops.
Loops being weaved together
LOOPINGS
50
50
100
100
150
150
50
50
100
100
150
150
VISUALLY ITERATIONS
LOOPINGS
3D POINT CHARGES
MESH POPULATION
ITERATION 3 HYDRO-CONTOURS
Finally, we figured out the logic between contours and hydrology: the hydrology within the same altitude are likely to be the same and as the topography grows higher, the soil tends to be drier and correspondingly, the lower parts lower. As we have already gathered statistics from different points, lying on different heights, we could then figure out the difference of hydrology between contours and populate the moisture values to the hole site and even further to other parts of Melbourne. This to us seems to be the most logical way to figure out the expression of hydrology.
HYDROLOGY AVERAGE OF HYDROLOGY TESTED ON SITE
POPULATED HYDROLOGY
POPULATED 10 TESTED VALUE AROUND TESTING POINTS
HIGHTEST HYDROLOGY ON SITE
HYDRO MESH
MESH FORMED BY TESTED HYDROLOGY AND FLOATING WATER COMPONENT
HYDRO POINTS WATER COMPONENTS POPULATED AROUND MEASUTED HYDROLOGY
HYDRO FALL FLUID LOOP CREATED BY HYDROLOGY FLOWING FROM THE MOST MOISTURISED PART TO THE DRY
HYDRO CONTOURS HYDRONIC VALUE ON THE SAME CONTOUR LINES ARE NORMALLY THE SAME.HOWEVER AS CONTOURS GROW HIGHER, THE LAND TENDS TO BE DRYER. THUS THE HYDROLOGY OF A WIDE RANGE OF AREA COULD BE INFERRED.
HYDROLOGY EVALUATION
EVALUATION OF HYDROLOGY ACCORDING TO CONTOURS.
TESTING LOCATIONS
BLUE HEXAGONALS INDECATES THE LOCATION THAT WE TESTED ON SITE.
RIVER
PLANT HEALTH
4.0 VEGETATION
P
VEGETATION HEALTH IS EXAMINED BY TAKING PHOTOS WITH THE INFRARED CAMERA ON SITE. BECAUSE PLANTS ABSORBES LIGHTS BUT REFLEXT INFRARED LIGHT, THIS CAMERA CAPTURES THIS LIGHT TO ASSESS THE PLANT HEALTH. THE PLANTS IN THE INITIAL PICTURES ARE IN BLUE AND WHITE. THE BRIGHTER THE PICTURE IS, THE HEALTHIER THE PLANT IS. HOWEVE TAKING THESE PHOTOS FURTHER TO THE INFRARED WEBSITE, WE WILL BE ABLE TO GET A MORE CONTRASTED AND ACCURATE PICTURE FOR US TO EXAMINE IN GRASSHOPPER,
V E T
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BALLOO CAMER
Infl
Vegetation Health Data Collection
Vegetation health datas were collected on site, using photos taken with camera fixed on the balloons. The camera was fixed downward in a cut bottle, and the bottle was connected to the balloons. The camera took near infrared photos every few seconds from the sky.
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Near-Infrared photography originally taken with the balloon camera
Processed NDVI image for RED filter Post processed by ‘Infragram.org’.
A
B
C
NDVI image for RED filters (B-R)/(B+R)+0.02
VEGETATION HEALTH
Vege coLLage
The modified digital camera can catch the near-infrared and blue light in a same image, but in different channels. In nature, plants absorb red and blue light to produce sugar but not infrared. So by comparing the brightness of the colour, we can infer how much the plants are photosynthesising. The near infrared photographies were than
used to compose a vegetation collage image using mapknitter website. By changing the size, adjusting the shape and scale of the photos (due to the windy weather, some of the photos taken were not accurately on the right angles, so there were some distortions in some photos), we made a collage based on the real world map.
HIGHEST VEGETATION HEALTH VALUE
Highest vegetation health value
Extracting values higher than a certain number.
VEGETATION HEALTH MEASURED ON SITE This data cloud was generated by the balloon image collage. The data was up to date of our visit, it is more dynamic than the value we generated using the map.
Vegetation Health Extracted from map
VEGETATION HEALTH FROM NEAR MAP Data cloud generated from satelite map. It showcases the overall vegetation conditions of our site. However, it is not near-infrared photo, so it is not eligible to present the vegetation health on site.
Site Topography
SITE TOPOGRAPHY
WORLD CONNECTIONS
5.0 CA STATES
W
The CA is produced by the dynamics team with our mapping as a basis. This is an indication of the interaction between two groups. We provide the dynamic groups with basic informations and they decide how to utilize this data into their dynamic settings, they will further generate more information and different data from unity and feed us this data so we could further map it out.
Reflection
CA is the spawn of unity units in the virtual word. However our job is to connect them with the real world. Through overlapping the CA and the real world map, it could be observed that the virtual world and the real world coinciding each other. This gives a good cognition of the spawn of the different elements.
C S
OUTPUT 1
OUTPUT 2
OUTPUT 3
OUTPUT 4
OUTPUT 5
CA VS REAL WORLD
CA OVERLAP
DYNAMIC SYSTEM
6.0 AGENT MAPPING
Agent history
Reading agent output from unity definition given by Jules.
Agents. There are two types of agents in our testings. One of the types reads intensity map of vegetation health, another type reads intensity map of human activities. The agents are attracted by locations with higher values, and will avoid going to places like river, motor ways, as little vegetaion growth and human activities take place in either of the areas.
Values of each intensity map is deducted by the agent by different values in each testing. The image on the left visualises the value deduction by set the agents to deducting values from intensity map of topography. Agents then look for locations with higher values, rather than traveling on same paths repeatly.
We tested the agents with different sight width & length, deduction of map values, and observed that the agents traveled different paths in uinty. The path traveled by agents is recored as CSV files in unity. We converted the files into multiple lines using grasshopper definition given by Jules.
Agents Movement
Agents Path
Sight width: 80/90 Sight length: 100/120
CSV: vegetation health CSV: human activity
Vege lover: -1 -0.1 Human lover: -0.1 -0.8 Time: 10s
Vege lover: -0.2 -1 Human lover: -1 0 Time: 15s
change
Vege lover: -0.1 -1 Human lover: -1 -0.1 Time: 15s
Vege lover: 0 -0.1 Human lover: -0.1 -1 Time: 15s
Sight width: 99/99 Sight length: 150/110
Vege lover: -1 -1 Human lover: -1 -1 Time: 10s
Vege lover: -0.1 -1 Human lover: -1 -0.1 Time: 13s
Vege lover: -1 -0.1 Human lover: -0.1 -1 Time: 15s
Vege lover: -2 -2 Human lover: -2 -2 Time: 10s
Update the CSV file which isolate the river
Vege lover: -0.1 -1 Human lover: -1 -0.1 Time: 10s
Vege lover: -1 -0.1 Human lover: -0.1 -1 Time: 10s
Agents tESTING
Agents Movement
Body Algorithm
Loud Quiet People During our third site visit to the Dights Falls, fabrication team and us had several tests of the 'body algorithm'. Each of us are assigned with three tasks, which are: 1. Find the most quiet places; 2. Find the loudest places; 3. Walk away from people we meet. We completed the tasks, and spent 10 minutes
on each task and recorded our paths seperately using the app 'strava'. Then these paths are transferred into CSV maps. Using the same techniques of our last agent path generation process, we tested out the different paths our agents will generate by reading the new CSV maps.
QUIET
Loud
PeOPLE
QUIET
Loud
PeOPLE
Sight Width: 100 120 Sight Length: 100 200 Add value: 0.5 0.5 Time: 10s Direction: towards high values
Sight Width: 100 120 Sight Length: 100 200 Add value: 0.5 0.5 Time: 10s Direction: towards high values
Sight Width: 50 80 Sight Length: 50 80 Add value: 0.5 0.5 Time: 10s Direction: towards high values
Sight Width: 50 80 Sight Length: 50 80 Add value: 0.8 0 Time: 10s Direction: towards high values
QUIET
Loud
PeOPLE
Sight Width: 50 80 Sight Length: 50 80 Add value: 1 1 Time: 10s Direction: towards high values
Sight Width: 150 120 Sight Length: 30 30 Add value: 1 1 Time: 10s Direction: towards high values
Sight Width: 150 50 Sight Length: 120 50 Add value: 1 1 Time: 10s Direction: towards high values
Sight Width: 150 50 Sight Length: 120 50 Add value: 1 1 Time: 10s Direction: NOT towards high values (NULL)
GAME TESTING
7.0 ON SITE TEST
The final game was tested and played on site. Testing the game is not only to ensure the game runs well but also observe how this influences human behaviour. The game runs for 10min for one play, and the players each went through 3-4 plays so that the site could be fully explored (10min are not enough to explore the site). The following section will be reflecting on some of the testing outcomes.
Testing Players:
Player 1: Alex Player 2: Jules Player 3: Direction group Player 4: Dynamics group Player 5: Mapping group Player 6: Fabrication group
What is the game like?
A
Time for some explanation on our game system. Which group goes first?
The first thing we went through after gathering on site was receiving a simple presentation by the dynamics and direction groups on the system that they built and how the game should be played. Admittedly, we all had a rough idea on what each group should be doing but we had no idea how the final game system looks like, in other words, what happens in unity (we believe the fabrication group feels so as well). During the two group's presentation we felt a little bit confused on some of the elements they were establishing and not until then did we get to know where our intensity maps went to. We were by all means trying to gather information from the site and feeding them to other groups, and also made intensity maps according to other groups' needs, but in fact we never got a chance to know how these data was used and which intensity map was used the most widely. Sometimes we even doubted if our data is really used by the game, however after the presentation, we were happy to hear how our maps were being read by different elements in the game.
Finally testing the game!
Let's go! Oh this looks cool!
Oh you are winning! I've got nearly 10,000 yellows!
No, liar.
Eugene said there will be rare blue spawns near the water, did you see any?
I don't know! Just destroy them with the brush!
Oh now the greens come out! But I think we're winning. Why are there so many purples? And why doesn't it show my destroyed buildings?
Good question...
Wait are we actually moving?
WE HAD THE WRONG VERSION!!!
By the way you're nearly at the sit boundary.
Yeah working pretty well.
Oh okay but we're going into the woods to explore.
How's your GPS working? Is it moving?
The game is really making people move to places that they don't tend to reach and what is even better is that the site boundary couldn't be noticed when playing the game and just makes people keep on exploring.
Our GPS isn't responding and we're always stuck at one place!
FINALLY GOT THE GAME RUNNING!
Oh really! You can have one of our phones!
I like the surface
Yeah and we could locate ourselves according to the river!
Finally seeing some different views! The yellows are so cute!
Oh and the GPS is actually working VERY well! You see the highway?
As the mapping team, we have been to the site most frequently (4 times). Also, during every site visit, we have been walking around and exploring the site as much as we can. This took away a large part of fun away from us during the game test as we were not surprised wherever the elements spawn simply because we mapped it out and knew exactly where they are in the real world (laugh).Also, because we have been referring to the real map a lot, we knew exactly where the site boundary is and how far we have gone. However for the testing teams that haven't been to site as often, they became very excited and explored places that people rarely go to. According to Femke (Direction group), they went into the woods trying to find the corporations. During the third site visit when we mapped out the body algorithmic for the agents, we deliberately went through the hidden paths inside the woods, intending to let the agents lead the players' ways through places that they don't usually go to. However, through the game test we found that this was in many ways already achieved by the corporation spawn.
Reflection
REFLECTION
8.0 OUTCOME
TEAM WORK IS IMPORTANT! This not only refers to our group but also a whole studio as a BIG TEAM creating one BIG GAME! Under this circumstance, communication and corporation became extremely important. Teams will need to understand each others’ needs and enhance corporation, which we really need improvement. GAME IS NOT THE END! To complete the game is really not our final game. What we were expecting to see was the manipulation of the game on people to drive the way they think, their cognition and their behaviour. The game was successfully built and the test on site went out successful, but the next step is how to let people all to get to play our game! How do we get to drive people through the site! COMPUTATION IS MORE THAN A TOOL! All of our statistics for the game were created algorithmically. Though this process, we were really impressed by what computation could achieve and how beautiful information is. The usage of computation is actually two-sided. We manage to map out what we want by grasshopper, and correspondingly, in many ways the algorithmic process inspires us on what we might want to achieve. We should neither regard Grasshopper as a pure mapping tool nor should we always expect to be inspired by its coincidence. HOW FAR DO WE WANT TO GO!? Designing never ends, and Merry Creek is never the only site that we will be handling. In future designs, the idea of human interaction and algorithmic methods could further be utilised.