Tao Tang and Jiazhen Zhang Department of Geography and Planning, and the Great Lakes Research Center State University of New York Buffalo State

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1 Tao Tang and Jiazhen Zhang Department of Geography and Planning, and the Great Lakes Research Center State University of New York Buffalo State College 1300 Elmwood Ave., Buffalo, NY Phone:

2 In order to study the geographic processes or geographic events that are happening in a particular region, measurement, data collection, data analyzing and modeling, as well as result presentations are all need to be spatial scale appropriate. The rules of map presentation and spatial modeling are: 1. Large scale represents small geographic area 2. Small scale represents large geographic area

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4 Researchers studied the spatio-temporal data modeling applying geosensor data (Llavesa, A. and Kuhnb, W., 2014; Devarajua, A., Kuhnb, W. and Renschlerc, C. S., 2015) both for in-situ sensors and web based sensors. Similar to spatial scale, time scale should also be considered in studying geographic processes or events, as well as the spatial pattern changes of geographic features. In this case, small time scale means brief time period. By contrast, large time scale means relatively longer time period.

5 Brief time event Long time event

6 NY State digital orthographic aerial photographs 2014 one foot Very high spatial resolution, but what time is it?

7 Database user activities by time source: ORACLE Time stamps of attributes in a database

8 In this research, an unmanned helicopter (UAV) with a built-in remote sensor was utilized to conduct two GIS projects of time sensitive or in relatively brief (small) time scale. The geographic events of what is happening? or what has happened? that were captured would otherwise not be recorded by any other means.

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10 Shortage of on-campus parking space during school days is one of the major problems for universities in the United States, particularly for urban campuses. Wendling (2011) indicated that in the United States, the parking situation in a university is important enough to become one of the factors to either promote or hinder the university s development. However, the time scale of parking lot land use is very small (brief), and the spatial or geographic scale is very large (detail oriented). Therefore, it is inadequate to utilize the means of conventional remote sensing, such as digital orthographic images or satellite images to conduct the research simply because the time resolution of these images are too low or inaccurate.

11 In this research, aerial imagery data of occupational land use changes of student parking lots was collect during typical school days, either Monday, Wednesday and Friday or Tuesday and Thursday. One of the important aspects of remote sensing applying unmanned helicopter or drone platforms is to obtain the image data at the times and locations needed. Relating the field-collected imagery data to class offering schedule database of time and location information can help determine the possible causes of intense competitions for parking spots.

12 METHODS AND APPROACHES Image data collections were mainly focused on the beginning, middle, and the end of the semester separately. 1) The original hypothesis of this study is that the students may be more actively attending the classes in the beginning and ending weeks of a semester. We expect the parking lot occupation rates during the mid-term surveying period to be lower than the other two sampling periods. Image data collected at one of the parking lots (G-24,February 04,2014 afternoon surveying time) Image data collected at parking lot G-20 (February 04,2014 afternoon surveying time)

13 2) Many of the students prefer to take classes on Tuesdays and Thursdays, so they can have more time to work on part-time jobs. Many of the faculty would also like to teach classes on Tuesdays and Thursdays as well. Therefore, the second hypothesis is that parking lots are busier on Tuesdays and Thursdays than on Mondays, Wednesdays, and Fridays. 3)The third hypothesis is that students may prefer to take morning classes rather than afternoon classes. Example of data collected during fall semester of 2014 applying DJI PHANTOM 2 Vision+

14 The spatial and temporal relations of class offering locations and the student parking lot occupations are shown in following figure. The symbols of parking lot utilization range from blue as the lowest to red as the highest. By contrast, the class offering rate in a building is represented from light yellow as the lowest to dark brown as the highest at that time period. The name of the lots Old name of the lots R-13 F R-5 G R-15 K G-24 L-1 G-20 M I-39 R I-33 Y

15 The highest utilization rates of both student parking lots and classrooms occur on Tuesday and Thursday afternoons. During this time, a large number of classes are offered in the Classroom Building, the Bulger Communication Center, and the Rockwell Hall. The student parking lots I-33 (Y), R-13 (F) and G-24 (L) are either overflowed or reach their capacity (in red).

16 By contrast, less competition of student parking spaces occurs during the other three time periods, namely Monday, Wednesday, and Friday morning and afternoon, as well as Tuesday and Thursday morning. Two major characteristics exist across the four modeling time periods. 1) Parking lot I-33 (Y), located in the east part of the campus and close to class offering buildings, is always full no matter the time period or day. 2) Large student parking lots that are located at the west part of the campus, such as the G-20 (M) and R-5 (G) lots, are always among the lowest occupation rates during school days. The name of the lots Old name of the lots R-13 F R-5 G R-15 K G-24 L-1 G-20 M I-39 R I-33 Y

17 Japanese knotweed is a herbaceous perennial plant species native to East Asia in China and Japan. It is one of the world s worst invasive species, as World Conservation Union (WCU) listed.

18 New York State Department of Environmental Conservation (NYS-DEC), in collaboration with the Cornell University, established the imap Invasive website for crowdsourcing the reports of invasive species by local people, in particular those people doing recreation activities. However, the imap Invasive internet based sensor system and its database do not record the time, intensity, and distribution of invasions. imap Invasive does not verify the growing situation of invasive species as well.

19 1. To locate and verify the major distributions of the F. japonica (Japanese knotweed) in Erie County, New York 2. To measure the invasion area of F. japonica (Japanese knotweed) 3. To analyze the habitat conditions of the F. japonica (Japanese knotweed) invasive species in a three dimensional (3 D) environment.

20 Aerial images, temperature data, and relative humidity (RH) data were collected in-situ using an unmanned helicopter and the on-board sensors. HOBO meteorological data logger/sensor was utilized to collect temperature and relative humidity data. The images and attribute data of the micro-climate were collected with both time and location stamps. Original aerial image Data logger

21 watershed, and sometimes, photos. Summary table example of Japanese Knotweed verification and invasive areas ID(website) Latitude Longitude Observed ( ) ( ) date May 14, NY U Aug. 13, NY U NY U Aug. 13, 2014 NY U Aug. 13, 2014 NY U June 10, 2014 NY U May 11, 2011 NY U June 4, 2014 NY U May 29, 2014 NY U May 29, 2014 NY U May 29, 2014 NA NA NY U Aug. 9, 2013 NY U Aug. 9, 2013 NY U Aug. 9, 2013 NY U Aug. 9, watershed Tonawanda Creek Tonawanda Creek Tonawanda Creek Tonawanda Creek Buffalo River Buffalo River Tonawanda Creek Buffalo River Buffalo River Buffalo River Buffalo River Buffalo River Buffalo River Buffalo River Buffalo Appearances in Area field investigation (m 2 ) No NA Yes Yes, burgeon NA No NA Yes Yes Yes Yes Yes Yes Yes Yes Yes 102.2

22 Most of the F. japonica are located in the 200- meter buffer area around Lake Erie. Only four out of 20 detected sites of invasion were located outside the 1,000-meter buffer area.

23 Since the measured temperature and relative humidity (RH) at different sampling days vary, the temperatures and RH at different altitudes were subtracted from ground surface temperatures and RHs at the same time to normalize the values in the measured datasets. Relative humidity (RH) shows no clear spatial patterns as the altitude increases from ground level to 14 meters above the ground. However, it does show the horizontal changes from the Lake Erie shore to the inland areas (following figure).!( Legend!(!(!( GWR_slopRH!(!(!(!(!( StdResid!( < -2.5 Std. Dev.!( Std. Dev.!( Std. Dev.!(!(!(!( Std. Dev.!( Std. Dev.!( Std. Dev.!( > 2.5 Std. Dev.!( 0 1,875 3,750 7,500 Meters Kriging Prediction Map [GWR_slopRH].[LocalR2] Filled Contours !(

24 The change in temperature ( ) The change in temperature of each of F.japonica patches meters 4 meters 6 meters 8 meters 10 meters 12 meters 14 meters Altitude (m) The temperature shows decrease as the altitude increases within 14 meters from the ground. However, it does not follow the normal lapse rate (0.64 ºC per 100 meters)

25 A. B. C. D. E. F. G. Temperature distribution at different altitudes: a) 2m; b) 4m; c) 6m; d) 8m; e) 10m; f) 12m; g) 14m.

26 Earthquake early warning system CTA Live Bus Route Sensing and Digital Mapping

27 Therefore, time scale issues must be considered from data collections, data analyses, data modeling to data presentations. Applications of both in-situ sensors and internet or crowdsourcing sensors must consider the time scale issues and time sensitive issues. Real time temperature sensing

28 Geographic processes or events are both spatial- and time-dependent. Geographic processes or events may behave differently not only at different spatial scales, but also at different time scales. More research needs to be conducted on brief (small) time scale geographic processes and events at various spatial scale levels. Time scale sensitive sensor development and applications both in-situ and at online websites are very useful and important. A few examples: 1) Earthquake prediction 2) Traffic and travelling route condition broadcasting and planning 3) Forecasting and analysis of the spread of infectious diseases

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30 Raster: Elevation Raster: Intensity