Nina Lam - Profile on Academia.edu (original) (raw)

Papers by Nina Lam

Geomorphometry and Mountain Geodynamics: Issues of Scale and Complexity

Error and Accuracy Assessment for Fused Data: Remote Sensing and GIS

An Evaluation of Fractal Surface Measurement Methods for Characterizing Landscape Complexity from Remote-Sensing Imagery

ABSTRACT The rapid increase in digital data volumes from new and existing sensors necessitates th... more ABSTRACT The rapid increase in digital data volumes from new and existing sensors necessitates the need for efficient analytical tools for extracting information. We developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates the three fractal dimension measurement methods: isarithm, variogram, and triangular prism, along with the spatial autocorrelation measurement methods Moran's I and Geary's C, that have been implemented in ICAMS. A modified triangular prism method was proposed and implemented. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all of the surfaces, particularly those with higher fractal dimensions. Similar to the fractal techniques, the spatial autocorrelation techniques are found to be useful to measure complex images but not images with low dimensionality. These fractal measurement methods can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

Study of the relationship between the spatial structure and thermal comfort of a pure forest with four distinct seasons at the microscale level

Urban Forestry & Urban Greening, Jul 1, 2021

Abstract The local thermal environment of forest green space has a great influence on users' ... more Abstract The local thermal environment of forest green space has a great influence on users' experience and health. Thus, it is of great value to systematically study the relationship between forest spatial structure and thermal comfort at the microscale. This study examined the relationship between the spatial structure and thermal comfort of bamboo forest using a case study of "Zhu Hai Dong Tian" Park in Dujiangyan city, Sichuan Province, China. We selected 20 in-forest study sites and three out-of-forest control sites and measured 13 spatial structure parameters and five climatic parameters (air temperature, relative humidity, wind velocity, surface temperature and global radiation) at the 23 sites for three representative days in each season. Then, the five climate parameters, human body parameters (sex, age, height, weight, clothing, and activity), and sky view factor (SVF) were input into Rayman software to calculate the physiological equivalent temperature (PET) value of each site in each season. PET is the physiological temperature value of the equivalent thermal environment based on energy balance. In this study, PET values between 15 and 22 °C indicated that the thermal environment was comfortable. The results showed that the PET values of the 20 study sites in the bamboo forest were mostly at a comfortable level in spring and autumn but had various degrees of heat stress and cold stress in summer and winter, respectively. The relationship between microstructure parameters and PET in four seasons was expressed by four models. In the spring and summer models, CPR (cover plant ratio) and CDW (canopy diameter width) were positively correlated with PET and had the greatest influence. In the autumn and winter models, DFS (degree of facing the sun's trajectory vertically) and CPR were positively correlated with PET and had the greatest influence. In addition, DBH (diameter at breast height), DB (density of bamboo forest), S (slope), CPH (cover plant height), ST (slope of the trunk), SP (spacing), H (height of the space), and IL (internodal length) were included in the models of all four seasons. The research results provide useful information for improving the thermal comfort of microscale pure forest space by modifying the forest structure. The results can be used to guide forest management to further create forest space that integrates climate comfort, landscape, and service.

Spread of AIDS in rural America, 1982-1990

PubMed, May 1, 1994

Using a national county database, we examine the hypothesis of increasing spread of acquired immu... more Using a national county database, we examine the hypothesis of increasing spread of acquired immune deficiency syndrome (AIDS) in rural America. Data for county-level AIDS caseloads for the period 1982-1990 were obtained by contacting state health officials of individual states. Yearly and cumulative AIDS cases by county or health district were converted to rates with use of the 1986 population figures. The data were grouped into 3-year periods, 1982-1984, 1985-1987, and 1988-1990, and analyzed. The top 25 counties that had the highest rates of increase were identified, and their average population sizes were derived. Pearson's correlation coefficients between the rates of increase and county populations were also computed. The results corroborate data from previous studies based on selected regions and clearly point to an increasing spread in rural counties on a national basis. During 1982-1984, highly populated counties had the highest rates of increase in number of cases of AIDS, with the populations of the top 25 counties averaging 1.1 million. Between 1988 and 1990, the top 25 counties that had the highest rates of increase are mostly rural counties with an average population of 73,000. Not only are we presently faced with a much larger base of population infected with AIDS than before, the epidemic has also entered a dangerous phase of spreading to rural America where health care facilities are far less adequate than in urban areas.(ABSTRACT TRUNCATED AT 250 WORDS)

$Research paper thumbnail of Description and measurement of Landsat TM images using fractals$

Description and measurement of Landsat TM images using fractals

Photogrammetric Engineering and Remote Sensing, 1990

Areal interpolation: a variant of the traditional spatial problem

10. Urban Road Extraction from Combined Data Sets of High-Resolution Satellite Imagery and Lidar Data Using GEOBIA

Many types of event sequence data exhibit triggering and clustering properties in space and time.... more Many types of event sequence data exhibit triggering and clustering properties in space and time. Point processes are widely used in modeling such event data with applications such as predictive policing and disaster event forecasting. Although current algorithms can achieve significant event prediction accuracy, the historic data or the self-excitation property can introduce biased prediction. For example, hotspots ranked by event hazard rates can make the visibility of a disadvantaged group (e.g., racial minorities or the communities of lower social economic status) more apparent. Existing methods have explored ways to achieve parity between the groups by penalizing the objective function with several group fairness metrics. However, these metrics fail to measure the fairness on every prefix of the ranking. In this paper, we propose a novel list-wise fairness criterion for point processes, which can efficiently evaluate the ranking fairness in event prediction. We also present a strict definition of the unfairness consistency property of a fairness metric and prove that our list-wise fairness criterion satisfies this property. Experiments on several real-world spatial-temporal sequence datasets demonstrate the effectiveness of our list-wise fairness criterion.

Monitoring terrain elevation of intertidal wetlands by utilising the spatial-temporal fusion of multi-source satellite data: A case study in the Yangtze (Changjiang) Estuary

Geomorphology, Jun 1, 2021

Abstract Intertidal wetlands are dynamic geomorphological areas located at the land-sea interface... more Abstract Intertidal wetlands are dynamic geomorphological areas located at the land-sea interface and perform multiple ecosystem functions. Owing to increased human activities, intertidal wetlands have been subjected to dramatic changes in recent decades; therefore, high-resolution monitoring of wetland topography is critical to its management. However, satellite imagery with high spatial resolution usually demonstrates a low revisit frequency (e.g. several days to greater than ten days) and is frequently obstructed by clouds, limiting its capability to display the high-resolution time-series information of intertidal wetland terrain elevation variations. Conversely, satellite imagery with a high revisit frequency generally demonstrates a lower spatial resolution. In this study, a spatial-temporal data fusion method was utilised to generate hourly time-series images with a spatial resolution of 16 m by combining the satellite GF-1/WFV data (spatial resolution: 16 m; revisit frequency: 4 days) with geostationary satellite GOCI data (spatial resolution: 500 m; revisit frequency: 1 h). In combination with the tidal level information, digital terrain elevation (DTM) data of the intertidal wetland can be derived from fusion images. The DTM was synchronously validated by the terrain elevation data acquired on the same day utilising unmanned aerial vehicle (UAV)-borne LiDAR in the North Branch intertidal wetland of Chongming Island, Yangtze Estuary, with a root mean square error of 0.16 m. The application in Chongming-Dongtan indicates that this method is effective for monitoring high dynamic changes in intertidal wetland terrain elevations.

Interpolation: Areal

International Encyclopedia of Geography: People, the Earth, Environment and Technology, Mar 6, 2017

Journal of Homeland Security and Emergency Management, 2015

Mass evacuation of urban areas due to hurricanes is a critical problem in emergency management th... more Mass evacuation of urban areas due to hurricanes is a critical problem in emergency management that requires extensive basic and applied research. Previous research uses agent-based models to simulate individual vehicle and driver behavior, and is limited mostly to a small study area due to the complexity of the models and the computational time needed. To better understand evacuation behavior, simulating the evacuation traffic in a larger region is needed. This paper develops a two-level regional disaster evacuation model by coupling two agent-based models. The first model uses each census block centroid, weighted with its corresponding number of vehicles, as an agent to simulate the local road network traffic. The second model, developed on the platform of a commercial software program called VISSIM, treats each vehicle as an agent to simulate the interstate highway traffic. This two-level agent-based model was used to simulate hurricane evacuation traffic in New Orleans. Validation results with the real Hurricane Katrina's evacuation data confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. The modeling results show that the average root-mean-square error (RMSE) for the three major evacuation directions was 347.58. Under a simultaneous evacuation strategy, and with 240,251 vehicles in 17,744 agents (census blocks), it would take at least 46.3 hours to evacuate all residents from the New Orleans metropolitan area. This two-level modeling approach could serve as a practical tool for evaluating mass evacuation strategies in New Orleans and other similar urban areas.

13. Remote Sensing and Socioeconomic Data Integration: Lessons from the NASA Socioeconomic Data and Applications Center

Spatial Interpolation

Elsevier eBooks, 2009

A National Assessment of Changes in Flood Exposure in the United States

AGUFM, Dec 1, 2017

Annals of the American Association of Geographers, Mar 19, 2018

This study develops a linear regression model to select local, low-collinear explanatory variable... more This study develops a linear regression model to select local, low-collinear explanatory variables. This model combines two well-known models: geographically weighted regression (GWR) and elastic net (EN). The GWR model posits that the regression coefficients vary as a function of location and focuses on solving the problem of explaining the relationships under the spatially nonstationary condition, which a global model cannot solve. GWR cannot fulfill the task of variable selection, however, which is problematic when there are many explanatory variables with nonnegligible multicollinearity. On the other hand, the EN model is a member of the regulated regression family. EN can trim the number of explanatory variables and select the most important ones by adding penalty terms in its cost function, and it has been proven to be robust under the high-multicollinearity condition. The EN model is a global model, however, and does not consider the spatial nonstationarity. To overcome these deficiencies, we proposed the geographically weighted elastic net (GWEN) model. GWEN uses the kernel weights derived from GWR and applies EN locally to select variables for each geographical location. The result is a set of locally selected, low-collinear explanatory variables with spatially varying coefficients. We demonstrated the GWEN method on a data set relating population changes to a set of social, economic, and environmental variables in the Lower Mississippi River Basin. The results show that GWEN has the advantages of both the high prediction accuracy of GWR and the low multicollinearity among explanatory variables of EN.

Annals of the American Association of Geographers, Mar 14, 2018

Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These ... more Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial-temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012. The study area includes 126 counties affected by Hurricane Sandy. The objectives are threefold: (1) to derive a set of common indexes from Twitter data so that they can be used for emergency management and resilience analysis; (2) to examine whether there are significant geographical and social disparities in disaster-related Twitter use; and (3) to test whether Twitter data can improve postdisaster damage estimation. Three corresponding hypotheses were tested. Results show that common indexes derived from Twitter data, including ratio, normalized ratio, and sentiment, could enable comparison across regions and events and should be documented. Social and geographical disparities in Twitter use existed in the Hurricane Sandy event, with higher disaster-related Twitter use communities generally being communities of higher socioeconomic status. Finally, adding Twitter indexes into a damage estimation model improved the adjusted R 2 from 0.46 to 0.56, indicating that social media data could help improve postdisaster damage estimation, but other environmental and socioeconomic variables influencing the capacity to reducing damage might need to be included. The knowledge gained from this study could provide valuable insights into strategies for utilizing social media data to increase resilience to disasters.

International Journal of Geographical Information Science, Nov 27, 2017