交通排放是都市中重要的空氣污染來源，對健康也有不良影響。污染物濃度依離馬路遠近之不同，在小尺度空間分佈上實有顯著的差異，民眾也因而遭受到不同暴險程度。本研究利用新發展並完成驗證的「三維數位地理分析法」，結合了地理資訊系統以及高解析度的數值地形模式，來探討大臺北地區（包含臺北市及新北市）各村里之交通污染「高暴險社群人口比」以及「高暴險人口密度」，並探討「高暴險社群」（定義為住在離市區馬路5公尺內一、二樓之民眾）之人口社經特徵及空間分佈。研究結果顯示，大臺北地區平均各村里有14%的人口居住在離市區馬路5公尺範圍內之一、二樓，屬於「高暴險社群」。分析這些居住著高密度「高暴險社群」之各村里人口特徵顯示，女性、15至65歲間之民眾、教育程度高、及申報所得較高者傾向居住於較高「高暴險人口密度」之村里，他們有著相對較高的暴險程度。空間分析則指出，「高暴險社群人口比」與「高暴險人口密度」皆具空間聚集分佈，此外，在去除極端值後，「高暴險人口密度」平均值為3,771人／km^2，標準差為4,006人／km^2，最高達38,519人／km^2，充分顯示大臺北地區存在交通暴露不均等現象，高暴險人口密度區大都位於市中心之老舊社區。「局部空間指標關聯分析（LISA）」結果則指出大臺北地區之人口社經特徵之空間不均等及交通暴露不均等同時發生，與都市發展歷程及早期人口分佈相關。然而社會經濟不均等並未連帶影響社經弱勢族群遭受到加重的交通暴險，反而是教育程度高的社群，由於自我選擇造成其高度聚集於高交通暴險區。本研究顯示，使用新發展之「三維數位地理分析法」可較精確地找出高交通暴險區及其人口特徵，未來，此創新方法可推廣應用在其它任何著重於三維人口分佈特性之研究。

In urban areas, traffic emissions are a major source of air pollution, which results in health effects. Traffic-related pollutant concentrations vary both horizontally and vertically with distances from major roads, resulting in different exposure levels for urban dwellers. The objectives of this study are (1) to assess the three-dimensional distribution of the population with high exposure to traffic emissions at the district level with an innovative approach, Three-Dimensional digital Geography methodology (3DIG), and (2) to explore the socio-demographic characteristics of the high exposure population in Taipei. The results show that on average 14% of people live within 5 m of municipal roads on the first and second floors in each township in the studied area. High Exposure Population Density (HEPD) represents the number of people within the high exposure areas per square kilometer. After excluding one extreme value, the average HEPD is 3,771 persons/km^2 with a standard deviation of 4,006 persons/km^2; the maximum is 38,519 persons/km^2. This clearly shows the spatial inequality of traffic exposure in Taipei. Additionally, a correlational analysis indicates that females aged 15 to 64, and those with higher income and higher education tend to live in the higher HEPD areas. Furthermore, geospatial analysis shows that the population with high traffic-related pollution exposure is highly clustered, resulting from the expansion of metropolitan Taipei. However, the socio-demographic spatial inequality does not coincident with spatial inequality of traffic exposure. It turns out that residents with higher education levels living in downtown areas have relatively high traffic pollution exposure in Taipei. This study demonstrates that 3DIG is a versatile methodology which can be used in any research focusing on three-dimensional population distribution.