Urban Ecological Infrastructure (UEI) increases landscape sustainability by meeting multiple socioeconomic and environmental objectives. Community parks are a common form of green or terrestrial UEI that improve access to open space in urban areas. They also provide environmental benefits such as increased biodiversity, pollution filtration, urban heat island mitigation, and rainwater drainage. Decision-makers should consider these factors when siting parks to maximize services, especially since land acquisition involves budget constraints. Geographic information systems (GIS) include various tools that can be used to site parks based on multiple spatial datasets. This research develops a GIS process to identify suitable parcels for parks, filling a gap in the literature through developing a small-scale analysis that considers local context and includes smaller pockets of potential green space in Metro Phoenix, Arizona. This process involved compiling a dataset of socioeconomic and environmental criteria, creating park suitability layers in ArcGIS Pro, and conducting statistical and spatial analyses on the suitability layers. The analysis involved assigning scores to each parcel, where higher scores indicate higher park suitability, and lower scores indicate lower park suitability. Factors that resulted in higher suitability scores were barren land cover, low tree canopy, high surface temperature, in a flood zone, far from existing parks and trails, high percentage of minority and low-income residents, and in an urban area. The resulting maps show significantly higher scores in the southern and western parts of Maricopa County, particularly in and around Gila Bend. While most high-ranking parcels are situated along rural highways, there are many large clusters of high-ranking urban parcels along waterways in Metro Phoenix. Based on this assessment, I recommend park implementation efforts focus on land along the Salt River, Gila River, and Agua Fria River in southern and western Phoenix. Further research could build on this methodology, integrating additional datasets such as walkability scores and experimenting with the parameters to see how the results change.