Spatial Analysis (Laban Notations, Sensory Map, Google Earth Engine and Arcpy)

Spatial Analysis Claire Liu Yang

Rethinking Urban Streets in Laban Notation STREET COMPARISON: ST MARKS PLACE, NYC vs CHESTNUT STREET, PHILADELPHIA

LEGEND

ALEJANDRA RAMOS & CLAIRE YANG

symbols are taken from labanotation, a pictographic method used to record movements in dance

short tree canopies do not allow for much natural light to reach the street level at all times, allowing shade from the sun for people to leisurely stroll, sit, and talk under for sustained outdoor activity

long

DIRECTION place

forward

left

backward

right

SHADING low

2ND AVE

3RD AVE

ST. MARKS PLACE

middle

up

RELATIONSHIP TAGS

TURNS left

right

left or right

DEGREE OF STRENGTH

N

1

2

3

4

5

6

ACCENT SIGNS TO NOTE LIGHT & BUILDING CHARACTERISTICS

building height continuity allows for a small city feel

building facades are monotonous action is at storefronts, presenting soft edges

strong heavy unemphasized

stronger

soft

emphasized passive

shaking

Rethinking Urban Streets in Laban Notation CHESTNUT STREET 37th - 30th (4- 5pm)

30th - 20th (5-6pm)

20th - 10th (6-7pm)

10th -Front (7-8pm)

story of concrete

TIMING short

DIRECTION place

boring facade

projections towards street

scale diffrence in posts

dense sustained direct

left

dark window decoration tells stories

Dab

tree canopy

right

middle

up

RELATIONSHIP TAGS Free Wring free sustained dense

light quick direct

low

scale and pace corelated

street park

delicate facade

Free Slash

TURNS

free quick dense

left

5

green infrastructure

target morphs into neighborhood

signage of busy street

street furniture

skyline hints at what lies ahead

2

3

4

strong target morphs into fabric

heavy signage of touristy place

interesting detail lower ground level

left or right

5

6

ACCENT SIGNS TO NOTE LIGHT & BUILDING CHARACTERISTICS

transition of facade maintanence issue

right

DEGREE OF STRENGTH 1

seat with no shading

light on concrete with trees behind

backward

SHADING

rthymic shadow

Press

forward

light that lures you in

bus stop blend in

human scale narrow door

long

unemphasized

stronger

soft

emphasized passive

shaking

Square Recording in Sensory Map SENSING PUBLIC SPACES: CLARK PARK vs READING TERMINAL MARKET

LEGEND

ALEJANDRA RAMOS & CLAIRE YANG

rings

CLARK PARK (NTS- NORTH PORTION) observation time- Sunday 9/11 around noon ice cream truck

ET

43RD STRE children playing

opacity of concentric ring correlates with time spent by group: deeper shade means longer time spent

The built environment is often experienced in an enclosed space, thus a market is not just significant because of its function, but in its design that directs easy circulation of people that are able to visit a wide variety of stands that each bring unique experiences.

jazz band & dancers

In the case of a park, the design is important in that having seating areas invite longer periods of time spent reflecting, reading, thinking, or even meditating. Outdoor public spaces in a city naturally bring many sounds and sights that add to the overall ambiance of the space.

CHESTER AVENUE

chess players

VENUE ORE A

bocce ball game

BALTIM

bigger circle size represents intense activity level

As Jan Gehl and Birgitte Svarre state in How to Study Public Life, no one really knows why people enjoy public spaces so much. Our theory is that successful public places depend on the amount and variety of new sensory experiences provided, be they through scale, organization, surrounding facades, or transparencies, for example. Senses are activated in part through sub-conscious relation to one’s past sensory archive. Emotional value from overlapping sensory experiences bring to surface feelings from experiences and memories stored that in turn influence the present use and occupation of a space.

0-15 min

touch (sense) sight sound

READING MARKET (NTS)

observation time- Sunday 9/11 morning

taste smell ARCH STREET A

Sensory experiences mediate the aesthetic and human focus of urban design. Multimodal sensories layered to coat the market: the warmth of red paint, intimacy of lighting, smell of food in the airflow, and sonic sphere that is sealed from traffic but rich enough to allow private talks. The meanings and narratives people derived from the senses are individual, but in shared sensory experiences come together to improvise the dynamic whole.

frisbee players

N

11TH STREET 11T 11

12TH STREET

TREES

46 min- over an hour

color

TABLES BENCHES

16-45 min

FILBER STREET

N

Social Movement Hub

+inequality = social movement hub

+ 2km around universities

+2km around breeding grounds

+ 2km around universities

/* "space is not merely a variable or “container” of activism: it constitutes and structures relationships and networks."Which census tract in Connecticut is most likely to be a hub of social movements? There should be young passionate college students, tradition of resistance, public space, enough people, and inequality. */ //Code // Add and Display Connecticut var CT=ee.FeatureCollection('ft:1xa2PvKTf7ynyAAEXEeHoltriaHFkyFJpvd74BLc6'); Map.addLayer(CT, {color:"FFCC99"}, "Connecticut"); Map.centerObject(CT,9); // Error Handling (type below any needed error handling) // 2km around Universities var CTUni=ee.FeatureCollection( 'ft:1PI555y2W1P8VdYiUKRHxeSKIIdfdpiJ02FpPGFW0','L atitude'); Map.addLayer(CTUni,{color:"5CB8FF"}, "Universities"); var UniBuffer = CTUni.geometry().buffer(2000); var UniTracts = CT.filterBounds(UniBuffer); Map.addLayer(UniTracts, {color:"FF6699"},"UniTracts"); print("2km around Universities",CTUni.getInfo()); // 2km around Breeding Places of mid 60s Underground Newspapers var Underground=ee.FeatureCollection('ft:1- tKFuHPxy3vDEWcbEsZYKBb_uWd7L1cn7XqPt4Pl'); Map.addLayer(Underground, {color:"9900CC"}, "Underground"); var UnderBuffer = Underground.geometry().buffer(2000); var UnderTracts = UniTracts.filterBounds(UnderBuffer); Map.addLayer(UnderTracts, {color:"CC0033"},"UnderTracts"); print("2km around breeding places of underground newspaper",UnderTracts.getInfo()); // Top 20 Populated var CrowdTracts = UnderTracts.limit(20, "POP",false); Map.addLayer(CrowdTracts, {color:"ED6300"},"CrowdTracts"); print("top 20 populated",CrowdTracts.getInfo()); // Have Public Spaces var PublicSpace = ee.FeatureCollection("ft:1QoKUy6azOoyOjvtOEge8V1NG5f4EgzkryWMD9vdC"); var PublicTracts = CrowdTracts.filterBounds(PublicSpace); Map.addLayer(PublicTracts, {color:"99FF33"}, "PublicTracts"); print("have public space",PublicTracts.getInfo()); // Inequality var MeanPov = PublicTracts.aggregate_mean("Pct_BlPov"); var PoorerTracts = PublicTracts.filterMetadata("Pct_BlPov", "greater_than", MeanPov); var UnequalTract = PoorerTracts.limit(1, "Med_Inc",false); Map.addLayer(UnequalTract,{color:"FFFFFF"},"SocialMovementTract"); print("Higher than average below poverty rate with highest median income", UnequalTract.getInfo()); //End Work cited and consulted: Piazza past homework Deborah Martin and Byron Miller (2003) Space And Contentious Politics. Mobilization: An International Quarterly: June 2003, Vol. 8, No. 2, pp. 143-156.

Nicholls, Walter, Justin Beaumont, and Byron A. Miller. Spaces of Contention: Spatialities and Social Movements Earth Engine JavaScript by Claire Yang

+top 20 populated

+have public space

Iraq Fire Anomalies

+inequality = social movement hub

New fire (places on fire September 13th but not on June 1st)

+ 2km around universities

Decreased fire (places on fire June 1st but not on September 13th)

+2km around breeding grounds

/* Satellite image analysis is essential to humanitarian relief actions. It has assisted the understanding of natural disasters, patterns of refugee camps, mining activities, etc. This script will show the change in fire anomalies in Iraq (near Mosul) this summer between June 1st and September 13th 2016. (ISIS is lighting fire as they retreat)*/ //Code // Add Base Image var BaseImage = ee.Image('MODIS/MYD09Q1/MYD09Q1_005_2016_09_13'); Map.setCenter( 43.3, 35.1, 8 ); Map.addLayer( BaseImage, {'bands': ['sur_refl_b02', 'sur_refl_b02', 'sur_refl_b01'], 'min': -100, 'max': 8000},'Base Image'); // Use Mask to Show Fire Anomolies in September 13th var SepFire = ee.Image( 'MODIS/006/MYD14A2/2016_09_13' ); var data = SepFire.select('FireMask'); var Mask = data.gt(6); var MaskedSfire = SepFire.mask( Mask ); Map.addLayer( MaskedSfire, {'bands': ['FireMask', 'QA', 'QA'], 'min': 3, 'max': 9},'Sep Fire'); // Use Mask to Show Fire Anomolies in June 1st. var JunFire = ee.Image( 'MODIS/006/MYD14A2/2016_06_01' ); var dataJ = JunFire.select('FireMask'); var MaskJ = dataJ.gt(6); var MaskedJfire = JunFire.mask (MaskJ); Map.addLayer( MaskedJfire, {'bands': ['QA', 'QA', 'FireMask'], 'min': 3, 'max': 9},'Jun Fire'); //Use Where to Show Places that were on Fire on June 1st but not on September 13th. var SepF = data.gte(6).and(data.lte(10)); var MaskD = MaskJ.where(SepF.eq(1), 0); var MaskedDiff = JunFire.mask (MaskD); Map.addLayer( MaskedDiff, {'bands': ['QA', 'FireMask', 'QA'], 'min': 0, 'max': 9},'Decreased Fire'); //Use Where to Show Places that were on Fire on September 13th but not on June 1st. var JunF = dataJ.gte(6).and(data.lte(10)); var MaskN = Mask.where(JunF.eq(1),0); var MaskedNew= SepFire.mask(MaskN).select ('FireMask'); Map.addLayer( MaskedNew, { 'min': 3, 'max': 9},'New Fire'); // End

//Citations MODIS Surface Reflectance product (MYD09Q1.005 Surface Reflectance 8-Day L3 Global 250m) MODIS/Aqua Thermal Anomalies & Fire product (MYD14A2.006: MODIS/Aqua Thermal Anomalies & Fire 8-Day L3 Global 1km)

Earth Engine JavaScript by Claire Yang

June 1st fire near Iraq +top Mosul, 20 populated

September 13th fire public near+have Mosul, Iraq space

Serial Vision: Montage-Time Seen-Fuzzy viewshed Introduction Google night walk in Marseilles inspired this project. Serial Visions explores how a moving observer experiences its surroundings visually along the road.The existing ArcGIS viewshed 2 tool requires extra processing computing power and doesn't give a sense how moving observers see along the road.

This tool will include three parts: Montage which will represent a moving observer at different point of time and simulate the progressing viewing experience. Time Seen is the mosaic (sum) of Montage results and shows which grid cells are looked at the most time by the observer. If we assume the observer travel at constant speed, we can even calculate the time spent looking at the overlapping grid cells. Fuzzy Viewshed will take distances into account and create a Distance Decay viewshed for the mosaic (maximum) of Montage. The further the less visible probability.

t

create points along the route, loop- run viewshed

Code Selection from the 6-page script: # Mosaic to the Time Seen using Sum operator and mosaic to the Booleen using Maximum operator arcpy.MosaicToNewRaster_management(nameOfInputRaster1; nameOfInputRaster2;name OfInputRaster3;nameOfInputRaster4;nameOfInputRaster5;nam eOfInputRaster6;nameO fInputRaster7,nameOfOutputRaster1,New Folder, "TimeSeen","1", "SUM","FIRST") arcpy.MosaicToNewRaster_management(nameOfInputRaster1; nameOfInputRaster2;name OfInputRaster3;nameOfInputRaster4;nameOfInputRaster5;nam eOfInputRaster6;nameO fInputRaster7,nameOfOutputRaster1,New Folder, "BooleanA","1", "MAXIMUM","FIRST") # Create Euclidean Distance Raster around the Road maxDistance = 400 cellSize = 12.0266846079753 EucDistance(nameOfInputShapefile, maxDistance, cellSize, nameOfOutputRaster1) # Apply basic distance decay funciton Visibility = 1/distance² to distance raster to create Decay1 nameOfOutputRaster2 = 1/Square(Raster(nameOfOutputRaster1))

boleen surface (existing Viewshed2 tool): 0 (nonvisible) or 1 (visible) 400 ft buffers (Philly Center City block is ~400 ft)

additive surface: 0 (never seen) to n (most seen)

clip and extract montage to the buffer geometry.

Data Sources The ArcScene of Logan Square from Virtual 3D City Philadelphia; building footprints and parcels； Elevation Raster； Road Shapefile of Benjamin Franklin Pkwy N 22nd St and N 16th St

Key scenes from Time Seen

Euclidean Distance

Distance & viewshed

Con("Decay1",(1/(1+ (Square(("Decay1"-Interval ) "Visibility = 1/distance²": Interval)))),1,"value >= Interval"):

Distance Decay 1

Distance Decay 2

Viewshed and the Distance Decay raster 2 =

Fuzzy viewshed decay with distance # Adjust the Decay1 to Decay2 nameOfOutputRaster3 = Con(Raster(nameOfOutputRaster1),(1/(1+ (Square((Raster(nameOfOutputRaster1) - 40)/80)))),1,"value >= 40") # Multiply Decay2 and Booleen mosaic to create Distance Decay Viewshed nameOfOutputRaster4 = Raster(nameOfOutputRaster3)*Raster(r"BooleanA") except Exception as e: # If unsuccessful, end gracefully by indicating why arcpy.AddError('\n' + "Script failed because: \t\t" + e.message ) # ... and where exceptionreport = sys.exc_info()[2] fullermessage = traceback.format_tb(exceptionreport)[0] arcpy.AddError("at this location: \n\n" + fullermessage + "\n") ArcPy By Claire Yang